rafmacalaba/datause-encoder-data huggingface dataset:
updates to the app and the revision to the data retrieval protocol such that the data were successfully downloaded from and subsequently cleared from each smartphone at every check-in. Table 1 provides a breakdown of the final household sample by treatment arm and across the study districts.

Table 1. Distribution of households across districts, by treatment arm

||Smartphone<br>(%)|24-hour recall<br>(%)|
|---|---|---|
|Central Malawi|||
|Nkhotakota|0.03|0.03|
|Lilongwe|0.13|0.13|
|Dedza (urban)|0.07|0.07|
|Ntcheu|0.04|0.05|
|Lilongwe City (urban)<br>|0.23|0.23|
|Southern Malawi|||
|Machinga|0.06|0.06|
|Zomba|0.06|0.05|
|Blantyre|0.03|0.03|
|Thyolo (urban)|0.07|0.07|
|Mulanje|0.06|0.06|
|Zomba City (urban)|0.03|0.02|
|Blantyre City (urban)<br>|0.21|0.21|
|Number of<br>households|377|358|

Figure 1 presents the structure of the study over the 11-day reporting period, including the visits made by interviewers to households in each treatment arm, and the administration of the 7-day recall module at the end of the survey period (day 11). As part of the study, and to inform a separate line of work in this program (Hocuk et al., in progress) respondents also wore a ActiGraph wGT3X-BT physical activity tracker over the reporting period and had height and weight measurements taken (informed by prior fieldwork in Malawi in 2017 - see Friedman et al., 2023), to inform ongoing research to develop machine learning models to reliably predict time use patterns from physical activity data and to more precisely identify physical activity demands of time use activities, across gender and different age groups.

In the smartphone treatment arm, an Android smartphone (Samsung Galaxy A12) was provided to each respondent for the duration of data collection. The smartphones were restricted to only allow respondents to use the _TimeTracker_ app on the device.<sup>7</sup> Sixty smartphones were reconditioned and redeployed throughout the fieldwork as survey teams moved from one enumeration area to another, and the teams were equipped with external batteries to recharge the respondents’ phones, if needed. As discussed in detail in Section 3, within the _TimeTracker_ app, respondents tapped on the images of activities (accompanied by captions in Chichewa) to begin recording time for those specific activities, and tapped the images again to stop recording time when they were done. On the day of deployment (Day 2), interviewers spent time guiding respondents on the use of the app. With the aid of paper illustrations, the interviewers provided an overview of all activities as depicted in _TimeTracker_ . Respondents were taught how to navigate

> extensive troubleshooting. This incident did, however, stoke fears of data loss, as such we again increased the number of sampled households for the smartphone arm by one household in 8 EAs that were subsequently visited. Overall, 19 EAs were spread across eight out of 9 districts that were covered by the study, and among the 19 EAs, the rural/urban split was 12/7.

> 7 Respondents also received a small pouch with a neck strap. A solar charger was also provided to each household in the smartphone treatment arm.

6
rural areas (Figure 2). The difference is greater when comparing E. coli at source point in urban versus rural areas (Annex 2).

**Table 1: Weighted sample mean values of variables in specification (1)**

|**Category**|**Variable**|**Type **|**Obs**|**Mean**|**Std. Dev.**|
|---|---|---|---|---|---|
|Outcome|Stunting|Dummy|816|0.42|0.49|
|E. Coli Contamination|High E. Coli Cont. at HH Point|Dummy|816|0.39|0.49|
||Some E. Coli Cont. at HH Point|Dummy|816|0.62|0.48|
||High E. Coli Cont. at Source Point|Dummy|816|0.18|0.39|
||Some E. Coli Cont. at Source Point|Dummy|816|0.43|0.49|
|Water Treatment|Household Practices Appropriate|||||
|Practices|Water Treatment|Dummy|816|0.06|0.23|
|Water Source|**Water Sources (Household Point)**|||||
||_Unimproved Water Sources_|Dummy|816|0.01|0.14|
||_Improved Sources; Tube Wells_|Dummy|816|0.91|0.29|
||_Improved Sources; Piped Water_|Dummy|816|0.08|0.26|
|Child's Characteristics|Child's Age|Continuous|816|29.79|17.53|
||Child's Age (log)|Continuous|816|3.08|0.98|
||Child's Age Squared (log)|Continuous|816|6.16|1.96|
||Female Child|Dummy|816|0.47|0.50|
||Child Ever Breastfed|Dummy|816|0.99|0.12|
|Mother's||||||
|Characteristics|**Mother's Education**|||||
||_No Education_|Dummy|816|0.38|0.49|
||_Primary or Some Secondary_|Dummy|816|0.52|0.50|
||_Secondary or Higher_|Dummy|816|0.10|0.30|
|Community|Community Improved Sanitation|||||
|Characteristics|Ratio|Continuous|816|0.51|0.23|
|HH Characteristics|**Sanitation Status**|||||
||_Unimproved Sanitation_|Dummy|816|0.23|0.42|
||_Improved Sanitation; Shared_|Dummy|816|0.24|0.42|
||_Improved Sanitation; Private_|Dummy|816|0.53|0.50|
||Household size|Continuous|816|5.37|1.93|
||Household Size (log)|Continuous|816|1.62|0.34|
||Urban Household|Dummy|816|0.19|0.39|
||**Wealth Quintile**|||||
||_First Quintile_|Dummy|816|0.23|0.42|
||_Second Quintile_|Dummy|816|0.20|0.40|
||_Middle Quintile_|Dummy|816|0.18|0.38|
||_Fourth Quintile_|Dummy|816|0.21|0.41|
||_Richest Quintile_|Dummy|816|0.18|0.38|
||Floor is made of Earth or Dung|Dummy|816|0.74|0.44|
||Functioning handwashing stand|Dummy|816|0.30|0.46|
||Unsafe Disposal of Child Feces|Dummy|816|0.15|0.36|

9
And, while there is ongoing debate about the testing and measurement of these skills, most parents and policy makers alike accept the notion that cognitive skills are a key dimension of schooling outcomes. The question is whether this proxy for school quality—students’ performance on standardized tests—is correlated with individuals’ performance in the labor market and the economy’s ability to grow.

Until fairly recently, little comprehensive data have been available to show any relationship between differences in cognitive skills and any related economic outcomes. The many analyses of school attainment and Mincer earnings functions rely upon readily available data from censuses and other surveys, which find it easy to collect information on earnings, school attainment, age, and other demographic information. On the other hand, it is difficult to obtain data on cognitive skills along with earnings and the other determinants of wages. Although cognitive test and school resource data are increasingly available at the time of schooling, these are seldom linked to subsequent labor market information. Such analyses generally require tracking individuals over time, a much more difficult data collection scheme. Such data are, however, now becoming available.

A variety of researchers are now able to document that the earnings advantages to higher achievement on standardized tests are quite substantial.<sup>6</sup> While these analyses emphasize different aspects of individual earnings, they typically find that measured achievement has a clear impact on earnings after allowing for differences in the quantity of schooling, the experiences of workers, and other factors that might also influence earnings. In other words, higher quality as measured by tests similar to those currently being used in accountability systems around the world is closely related to individual productivity and earnings.

Three recent U.S. studies provide direct and quite consistent estimates of the impact of test performance on earnings (Mulligan (1999); Murnane, Willett, Duhaldeborde, and Tyler (2000); Lazear (2003)). These studies employ different nationally representative data sets that follow students after they leave school and enter the labor force. When scores are standardized, they suggest that one 6 These results are derived from different specific approaches, but the basic underlying analysis involves estimating a standard “Mincer” earnings function and adding a measure of individual cognitive skills. This approach relates the logarithm of earnings to years of schooling, experience, and other factors that might yield individual earnings differences. The clearest analyses are found in the following references for the U.S. (which are analyzed in Hanushek (2002b)). See Bishop (1989, 1991); O'Neill (1990); Grogger and Eide (1993); Blackburn and Neumark (1993, 1995); Murnane, Willett, and Levy (1995); Neal and Johnson (1996); Mulligan (1999); Murnane, Willett, Duhaldeborde, and Tyler (2000); Altonji and Pierret (2001); Murnane, Willett, Braatz, and Duhaldeborde (2001); and Lazear (2003).

7
DHS data are based on both direct and Indirect estimates. Direct estimates refer to vital registration data and maternit; nistories (interviews of womea about birth and death history over, say, the last two decades). Indirect estimatea do not carry information on each individual child. The procedure is simpler and faster: women are asked how many born alive children they have had, and how many died. Infant and child mortality figures are derived from the answers. This i-ethod is, of course, somewhat less accurate and is subject to recall and truncation bias.

The data in the anne:. tables on irfant and under five mortality, immunization, and physicians per pcpulation result from a careful review of different sources. As such they represent what we believe may be the best data currently available on tiaese indicators. The various sources are footnoted at the bottom of each table.

Life expectancy is defined as the number of years a newborn infant would live if prevailing patterns of mortality at the time of his/her birth were to stay the same throughout his/her life. Data on life expectancy is very scarce. The main source is an article by the UN, which itselZ uses several other sources.4 Most of the countries covered are unfortunately developed countries. According to the UN secretariat, The data set used for the analysis of the leas developed countries consists of 78 national life tables for the period 1945-1981 whose agesex patterns of mortality hrv been evaluated as relatively reliable 4United Nations Secretariat, ley Differetials in Survivorship in the Developing World: Levels. Regional Patterns and Demogranhic Determinants (1988); A.J. Coale and P. Demeny, Regional Model Lifg Tables And Stable Populations (Princeton: Princeton University Press, 1966); United Nations, Levels and Trends of Mortality since 1950 (1982); WHO magnetic tapes.
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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 growth and changes in the nature of work. For example, in Peru informality has been constant at about 75 percent over the last 30 years. In Sub-Saharan Africa, informality remained, on average, at around 75 percent of total employment from 2000 to 2016 and, in South Asia, it increased from an average of 50 percent in the 2000s to 60 percent over the period 2010-16. Hence, cross-section regressions are more relevant estimation methods here given the low variation in informal employment over time compared to time series or panel data methods.

> 10 Authors’ calculations.

> 11 See Table A.1 in the Annex for the exact data coverage. For over 80 percent of the countries considered, the latest data is between 2011 and 2017.

10
per capita, such as Indonesia and Malaysia, also had much smaller public sectors, accounting for 8% and 15% of total employment, respectively.<sup>8</sup> The size of Egypt’s public sector is more in line with other Arab states, where public sectors tend to account for between 20% to 30% of employment.<sup>9</sup> Figure 2 shows a breakdown of employment in Egypt. Most notable is the very large informal sector, which had grown over the six years, mostly at the expense of the public sector. Also notable is the small size of the private formal sector, approximately a quarter of the informal private sector.

**Figure 2: Share of employment in Egypt by sector, 2012 and 2018**

<!-- Start of picture text --> 2012 2018<br>25.21<br>30.58<br>54.14<br>14.18 60.61<br>15.27<br>Private informal Private formal Public formal Private informal Private formal Public formal<br><!-- End of picture text --> _Notes_ . The analysis relies on cross-sectional data from the Egypt Labor Market Panel Survey (ELMPS) in 2012 and 2018. The analysis is restricted to individuals aged between 20 and 59 years old in each survey round. A job is defined as being formal if the individual had both a work contract and social security in the primary job. A job is defined as informal if the individual did not have a work contract, a social security, or neither. Expansion weights are used.

# **4. Labor Market Transitions: Methodology**

Employment profiles provide a useful snapshot of individuals' labor market positions in a given point in time. But labor market positions are dynamic, i.e. they change and evolve over time. Such evolution is as important as an accurate description of a specific point in time. For example, it is very useful to record the unemployment rate in a given month and year but it is very different if most of those unemployed in that month and year have been in the state for a long or a short period of time.

To describe labor market dynamics, we construct transition matrices over the relevant labor market states. Transition matrices link the labor market state of a given individual at a given date with the labor market state of the _same_ individual at a future date. As a result, transition matrices

> 8 Source: ILO, Malaysia Labour Force Survey, and Indonesia National Labour Force Survey. Data on the year of 2020. Retrieved Jan 2022.

> 9 Source: ILO and national statistical offices. Data on the year of 2020. Retrieved Jan 2022.

7
|**Table 3: Unit Cost of Infrastructure in South Africa: Electricity and Telecom**|
|---|
|**Sector**<br>**Unit Costs**|
|**Power:**|
|Lower Bound 1/<br>$0.021 per kwh|
|Upper Bound 2/<br>$0.037 per kwh|
|**Telecom**<br>$1,000 pertelephonemainline|
|Notes|
|1/ Lower bound represents long run marginal cost of generation. Figure may be<br>underestimate since it is based on projected demand growth that the present study<br>suggestsmay be anunderestimate.|
|2/ Upper bound represents long run marginal cost of generation adjusted for average cost<br>ofgeneration. Upperbound= 1.8x Lower Bound.|

Note: Source for Telecom Cost: Fay (2001); Source for Electricity Cost: National Integrated Resource Plan (2002).

Our forecasts indicate that the **electricity** production would grow from 4,815.47 kwh per capita in 2002 to 5,418.23 kwh par capita in 2005, reaching 6,453.19 kwh per capita in 2010. The average annual forecast growth rate for electricity production based on the estimated long-run relation during 2003-2010 is 3.7%. See Figure 5a.

For **telecom** , our model forecasts the growth in total telephone lines per 1000 of population from 410 in 2002 to 474 in 2005, and then 664 in 2010. The implied growth rate in the period 2003-2010 is 6.0%. For comparison, a similar exercise for Latin American upper middle-income countries resulted in telephone lines of 255 per 1000 of population (Fay 2001). See Figure 5b.

14
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38
<!-- Start of picture text --> GIN<br>SWZ<br>CHL<br>AGO<br>MEX<br>BWA TZA MRT UGA ZAF<br>NIC<br>PAN URY PANEGY BWA<br>BRA SWZ ARGCOLNAMBRA CRI<br>CRI<br>ECU GTM<br>MEXNAM ECU SLV<br>PERGTM MAR<br>BOL AGO<br>SLV RWA PRY PRY<br>COLARG HND EGY<br>URY CHL KEN NICMRTBOLHND<br>PER<br>ZAF<br>MDG KEN<br>MAR GIN UGA MDGTZARWA<br>.1 .2 .3 .4 0 .02 .04 .06 .08<br>Data Data<br>R−squared=.1977976014267405 R−squared=.4941246242786512<br>(a) Tax evasion (b) GDP per worker<br>.5<br>.6<br>.4<br>.4<br>.3<br>Model Model<br>.2<br>.2<br>.1<br>0 0<br><!-- End of picture text --> Figure 7. Tax evasion and GDP per worker - Data vs the model predictions - Alternative detection probability

**Counterfactual experiments.** With the calibrated model, we first conduct three tax neutral counterfactual experiments on Guinea as in the quantitative analysis before exploring two additional counterfactual analyses. In the first round of experiments, we examine what happens in Guinea regarding tax evasion if the level of distortionary infrastructures and deterrence probability can be improved to the average level of the sample and by 50 percent respectively. These changes lead to a decline in sales not reported for tax purposes by 26.72 percent and 18.34 percent if the level of distortionary infrastructures and deterrence probability can be improved to the average level of the sample and by 50 percent respectively. Regarding the output per worker, it grows by 1.22- and 6.46-fold respectively. The third experiment combines both changes in the level of distortionary infrastructures and deterrence probability and leads to a 40.16 percent decline in sales not reported for tax purposes, while the output per worker grows by 6.62-fold. These counterfactual experiments provide the same conclusion as in the quantitative analysis, where an improvement in the level of distortionary

33