rafmacalaba/datause-encoder-data huggingface dataset:
# **Other Development Policy Applications of CDR Data**

While CDR models are not yet sufficiently accurate to supplant traditional research methods, increasingly sophisticated techniques for combining CDRs with other data sources can enable cellular data to magnify the value of censuses and household surveys. Two especially promising approaches are described below.

## **_Unit-Level Predictions Using Mobile Survey Ground-Truth Data_**

A recent study in Rwanda used targeted telephone surveys to collect data on personal wealth rather than poverty incidence.<sup>12</sup> The advantage of this approach is that telephone survey responses can be combined with CDR data at the individual level. This is typically not possible with official household surveys, which are often anonymous and do not contain a cell phone number that can be cross-referenced against CDR records.

By coordinating the collection of survey responses with CDR analysis, the study was able to develop a set of high-quality matched individual data. However, the use of telephone surveys to gather the data presents its own limitations, as it is not possible to obtain the same detailed consumption information that face-to-face household surveys usually reveal. Instead, wealth proxies must be used—in this case, questions about asset ownership. If the results are not carefully validated, this can lead to errors in the output variables. Additionally, matching the data to telephone surveys requires that the CDRs not be anonymized, which generates substantial privacy concerns.

The Rwanda study offered an encouraging proof-of-concept for the “time extrapolation” application of CDR data. It found that a CDR model for 2009 provided a more accurate poverty assessment than the outdated 2007 household survey. However, the CDR model was not uniquely suited to this task, as macroeconomic indicators such as GDP growth could be used to create a similarly accurate projection.<sup>13</sup> Moreover, the CDR model only partially reproduced the estimates of district-level average household wealth recorded by the household surveys. Although correlations of around 0.9 were reported at the district level—higher than in the Guatemala analysis—it appears that a few wealthy districts may have driven that result.

## **_Combining CDR Data with Public Observable Data_**

> 12 See Blumenstock et al. (2015). 13 See Beegle (2016).

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14 year 2007 and for the year 2010 and only for low-income countries. Even though aid for trade correlates broadly with potential needs, the match is not perfect. Several countries whose scores would otherwise indicate they should receive large amounts of aid for trade in fact receive relatively below average amounts. The countries that are receiving relatively below average aid for trade are shown below the line in Figure 2. Moreover, these countries appear to be the same countries in both years with some exceptions such as Rwanda or Togo.

<!-- Start of picture text --> Figure 2. Aid-for-trade flows against the trade-capacity indicator<br>Pooled 2007-2010 regressions controlling for GDP per capita and quality of the legal system<br>BOL NIC<br>HND GIN<br>MNG BOL<br>NIC S KENEN CMR<br>KENAZE<br>MNGMOZ MOMLI Z TZAUGKGZ A KGZ<br>AZE<br>TZA CIV H NDBEN LK A UGALKA<br>CMRRWA TGO<br>RWA<br>BENGHAZMBMLI PANPL SEK BF ETHBFA N GHAA ETH CIV NP SDNL PAK<br>ZMB<br>GIN<br>SDN<br>NER NGA<br>NER NGA INDIND<br>TGO<br>-2 -1 0 1 2<br>Trade capacity indicator (weighted index cross sectional gravity)<br>2007 2010<br>Fitted values pooled aid for trade regression<br>Scatter exclude middle-income countries<br>2<br>1<br>0<br>-1<br>-2<br><!-- End of picture text --> Figure 2 also shows that the relationship between aid for trade commitments and potential needs is stronger for low-income countries: The fitted line in Figure 2 is based on the full sample of developing countries (low and middle-income countries) but the scatter-plot seems to indicate a stronger relation between supply and potential need for low-income countries. We thus analyse the impact of the aggregate potential need for aid for trade on the supply of aid for trade for the sub-sample of low-income countries. The results below (Table 4) confirm that the weighted trade related indicator and aid for trade are more strongly correlated for the subsample of low-income countries.

**Table 4:  Aid for trade allocation and trade related capacity index**

**Sub-sample of low income countries**
tenure than the youngest workers aged 20–24. The DP decomposition produces comparable results, finding a 3.4-year difference. The estimates of the two decompositions diverge further for older workers. For example, the ME approach finds that workers 50–55 have about 10.7 more years of tenure than workers from the youngest group; the DP decomposition reports an 11.7-year difference.

<u>Figure 4 presents the cohort effects on tenure for both estimation methods. The average tenure</u> declines over time for younger age cohorts in both decompositions. The negative gradient of job tenure is more pronounced in panel a, which shows the results of the ME decomposition. The average worker born in 1940, for example, has 3.6 more years of tenure than the average worker born in the 1980s. The DP decomposition shows similar dynamics, with a weaker decline in tenure over cohorts (the difference in tenure between the 1940 and 1980 cohorts is 2.6 years).

<u>Figure 5</u> shows the estimated period effects for both estimation methods. We grouped survey years into five-year intervals to preserve the APC age-period-cohort identity. This grouping results in only five survey periods, making the decomposition of the period effects less informative. Nevertheless, the ME decomposition indicates an increase in average job tenure between 1995 and 2020 by about a year. The DP decomposition produces a small, barely significant change in the time effects.

# **4.1. Subsample decompositions**

We now turn to subsample decomposition to explore the heterogeneity of the period, age, and cohort effects on tenure duration among different groups of workers. We also want to account, to some degree, for potential compositional change in the labor market during the period covered by the EU-LFS. The analysis in this section is based on the ME APC decompositions.

<u>Figure 6 shows the decomposition of the age effect by gender, educational level, region, and type</u> of contract. The age effect is increasing and similar for all subsamples. Panel a of figure 6 reveals a steeper age profile of tenure for male workers than for female workers. These results are consistent with other studies that report shorter tenure among female workers (Burgess and Rees <u>1998; Hollister and Smith 2014). The gender differences in tenure among older age groups may</u> also be attributed to the different official retirement ages for male and female workers (Turner and <u>Morgavi 2020).</u> 13
Devarajan, S. and Fengler, W. (2013). “Africa’s Economic Boom. Why the Pessimists and the Optimists Are Both Right”. _Foreign Affairs_ 92(3): 68-81.

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percentage increased to 8.7% in 2020 and further to 11.6% in 2021 as Figure 1 shows. The figure also indicates an increase in effective promotion rates during the pandemic (from 81 to 87 percent),<sup>8</sup> reflecting the automatic promotion policy implemented in the country during the duration of the crisis.<sup>9</sup> We also find evidence of greater movements from private to public schools, with a corresponding reduction in the opposite direction. These changes in the composition of the student body provide insights into household behavior during a crisis, since parents’ may consider that private schooling no longer provides greater net benefits, and thus move their children from these institutions into lower cost or free public institutions when there are fewer available resources due to economic shocks (Shafiq, 2010; Azevedo _et al_ ., 2021).

However, these are not the only stylized facts that appear in these data. World Bank (2022) provides an in-depth overview of trends in educational outcomes in Guatemala and other neighboring countries, highlighting that Guatemala remains behind many other countries with respect to human capital outcomes. The report finds differences in outcome trends across student characteristics, with greater dropout rates for boys, rural students, those in low-income households, and older children who attend lower and upper secondary schooling. This last stylized fact is important because educational attainment in Guatemala is below the Latin American average, with about 73% completing primary compared to more 91.6% for the region (SEDLAC, 2022). Most

> 8 Effective promotion is a binary variable that identifies whether a student is observed in a higher grade the following school year. By definition, it does not include dropouts nor graduating students with no next grade.

> 9 The promotion policy in the country during the pandemic was regulated by the Ministry of Education’s Ministerial Agreement No. 2690-2020 published on September 22, 2020, which provided the guidelines for students’ learning assessments, enrollment, promotion, and certification for school year 2020. For pre-primary education, the promotion was automatic as it had traditionally been in the past before the pandemic. For primary and secondary education, students could be promoted to the next grade if they scored a minimum of 60 points, calculated based on the assessments implemented and the activities conducted during face-to-face instruction and distance learning. In practice though there was a lot of controversy, in 2020, about the use of this evaluation method as the grading methodology allowed everyone to get at least 60 points. In 2021, these guidelines were revised, and the Ministerial Agreement No. 3474-2021 published on December 7, 2021 regulated the implementation of learning recovery agenda and established that students from primary and secondary who did not score the minimum grade will have up to two assessment opportunities to be promoted to the next grade.

6
Policy Research Working Paper 8110

# **Abstract**

A major concern with the rapid spread of technology is that it replaces some jobs, displacing workers. However, technology may raise firm productivity, generating more jobs. The paper contributes to this debate by exploiting a novel panel data set for Chilean firms in all sectors between 2007 and 2013. While previous studies examine the impacts of automation on the use of routine tasks by middle-educated workers. this study focuses on a measure of complex software that is typically used by more educated workers in cognitive and nonroutine tasks for client, production, and business management. The instrumental variables estimates show that in the medium run, firms’ adoption of complex software affects firms’ employment decisions and the skill content of occupations. The adoption of complex software reallocates employment from skilled workers to administrative and unskilled production workers. This reallocation leads to an increase in the use of routine and manual tasks and a reduction in the use of abstract tasks within firms. Interestingly, the impacts tend to be concentrated in sectors with a less educated workforce, suggesting that technology can constrain job creation for the more skilled workers there. The paper concludes that the type of technology matters for understanding the impacts of technology adoption on the labor market.

This paper is a product of the Trade and International Integration Team, Development Research Group and the Education Global Practice Group 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://econ.worldbank.org. The authors may be contacted at afernandes@worldbank.org and ralmeida@ 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
implications for pollution exposure, and some possible remedies for the most vulnerable family members (particularly young children). Section 6 compares our results with those in a recent study for India. In Section 7, we assess the effects of income and adult education on both determinants of exposure: average pollution levels across households, and patterns of activity within households. Section 8 provides a summary and conclusions.

# **2.  Daily Location Patterns in Bangladeshi Households**

Within households, individuals’ pollution exposure may vary significantly because they spend very different amounts of time in cooking areas, living areas, and outside the house. Table 2 reports average daily hours in the three locations for a representative sample of 4,612 individuals drawn from 600 households in rural, peri-urban and urban areas of seven Bangladeshi regions (Figure 1): Rangpur (491 individuals) in the Northwest, Sylhet (578) in the Northeast, Rajshahi (491) and Jessore (490) in the West, Faridpur (497) and Dhaka (1,493) in the Center, and Cox’s Bazar (572) in the Southeast.

Table 2 present statistics by sex, because gender roles are quite different in Bangladeshi households. Among age groups, we distinguish infants (age 0-1) and young children (age 1-5) because they are most vulnerable to air pollution, and most tied to their mothers’ patterns of activity. We divide school-age youths into two groups with different patterns of school attendance that may have implications for exposure to indoor pollution: students 6-8 years of age, who attend school in the morning (9:00 a.m. – 12:00 a.m.), and students 9-19 years of age who attend from midday until late afternoon (11:30 a.m. – 4:30 p.m.). We divide adults into prime-age (20-60) and older (60+) categories.

Table 2 shows that time-location patterns are very similar for infants of both sexes. They spend relatively short periods in cooking areas (1 hour per day), very long periods in living areas (20 hours/day), and the residual time (3 hours) outside the house. Infants spend more time indoors than 5
Policy Research Working Paper 6229

# **Abstract**

This paper introduces the Exporter Dynamics Database. The database includes exporter characteristics and measures of exporter growth based on firm-level customs information from 38 developing and seven developed countries, primarily for the period between 2003 and 2010. The measures are available at different levels of aggregation, including: a) country-year, b) country-yearproduct, and c) country-year-destination. Several new stylized facts about exporter behavior across countries emerge from the database. (i) Larger or more developed economies have more exporters, larger and more diversified exporters, and lower entry and exit rates than smaller or developing economies. (ii) In the short run, expansions along the intensive margin (exporter size) contribute more to export growth than expansions along the extensive margin (number of exporters). (iii) Exit rates are highly correlated with entry rates and both are negatively correlated with survival rates, average exporter size, and diversification. (iv) The number of exporters and the entry and exit rates in a country-product group are partially driven by country and productgroup effects; however, the average size of exporters in a country-product group is not. Although the first three facts can be explained by models incorporating firm heterogeneity and uncertainty, the fourth fact is more difficult to explain with existing models. Several findings are confirmed in this database, including the importance of large multi-product firms. This database can be a valuable tool to improve the understanding of the microfoundations of export growth, by providing new insights about exporter characteristics and dynamics.

This paper is a product of the Trade and Integration Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at tcebeci@worldbank.org, afernandes@worldbank.org, cfreund@worldbank.org and mpierola@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
Policy Research Working Paper 7379

# **Abstract**

When measuring inequality of opportunity, researchers usually opt to eliminate within-type variation. Provided that in practice it is impossible to observe all circumstances, this implies that the researcher estimates a lower bound of the true level of inequality of opportunity. By using data drawn from 27 Demographic Household Surveys (circa 2008), it is found that lower bound estimates can have substantial measurement error, and that measurement error can vary considerably across countries. As a consequence, lower bound estimates of inequality of opportunity can demand too little redistribution to equalize inequalities due to circumstances and can make the “traditional” cross-country comparisons misleading.

This paper is a product of the Poverty Global Practice Group. 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://econ.worldbank.org. The authors may be contacted atcbalcazarsalazar@ 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
the most striking development is the growing share for emerging products, from 2 percent to almost 40 percent. This product category has also affected the technological composition of Morocco’s export basket: a dominant and increasing share is manufactures (increasing from 60 to 90 percent of all emerging products); and, among the manufactures, the largest but only slowly growing share is med‐ or high‐tech manufactures (increasing from 50 to 60 percent; Figure 2).

Growth in emerging exports has been rapid and, during the last fifteen years accelerating. However, it has been driven by a relatively small number of products. In fact, two product categories accounted for half of the observed growth between 1995 and 2010: the first is “Fertilizers, n. e. s.” (SITC 5629) and the second “Insulated and electric wire, cable, bars, etc.” (SITC 7731). One tenth of the observed export growth was due to various small exports, including “Diodes, transistors, photocells, etc.” (SITC 7763) and various export discoveries in the textile industry.

Returning to Figure 1, among non‐emerging products, the major change is a decline in classicals (from 83 to 48 percent); in terms of technology, Morocco’s classical products are mostly primary and resource‐based or low‐tech manufactures. Among the remaining two product categories, disappearing products decline (by 7 percentage points) while marginals increase (by 3 percentage points).

Figure 1: Morocco’s Merchandise Exports 1980‐2010 by Pattern of RCA Shifts <!-- Start of picture text --> 1980 1985 1990 1995 2000 2005 2010<br>Classics Emerging<br>Disappearing Marginals<br>Source: Authors' estimates using UN-Comtrade Data<br>100<br>80<br>60<br>Exports, % of total 40<br>20<br>0<br><!-- End of picture text --> product categories have commonly been named classical (if D‐RCA=1 in the past and present), emerging (if D‐RCA=1 only in the present), disappearing (if D‐RCA=1 only in the past), and marginals (D‐RCA=0 in the past and present).

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