rafmacalaba/data-use-mentions-extended huggingface dataset:
tions that regress an indicator for responding to a post-displacement round on the violence experienced prior to displacement and all 14 pre-displacement food access variables contained in the WFP survey from the most recent pre-displacement survey to which the household responded.<sup>19</sup> If better-off household were less likely to drop out of the survey and change the composition of the sample, we might expect better pre-displacement food access to be positively associated with the probability of responding to post-displacement rounds, especially for later rounds.

However, there is little correlation between pre-displacement food access indicators and the likelihood of responding to any single post-displacement round. For each of the thirteen rounds after displacement (month of displacement and 12 following months), the table reports the p-value of the hypothesis test of all pre-displacement characteristics jointly equaling zero. Only one of the 13 p-values is statistically significant at the 10 percent level, which is what would be expected if there were no relationship; and the p-values themselves vary between 0.065 and 0.915.

Third, the survey can only be representative of the mobile phone-using population. Although it is difficult to identify exactly how access to mobile phones has changed since the onset of the conflict, all evidence suggests that access has remained high. Approximately 85 percent of the population lived in a household that owned at least one mobile phone prior to the conflict, and this figure was high across the entire country and among vulnerable groups<sup>20</sup> ; there is anecdotal evidence that the share with access to mobile phones remained high following the start of the conflict (although some households re-

> 19Specifically, the independent variables for each regression are the number of times the household consumed staples, pulses, vegetables, fruits, proteins, dairy, sugars, and fats in the past week in the most recent pre-displacement survey answered (eight separate variables); the number of times in the past week the household relied on less expensive foods, borrowed to purchase food, reduced the number of meals consumed
which shows that mobile broadband connectivity (3G/4G) is associated with a 2.1 pp increase in local economic growth.

The paper proceeds as follows. In Section 2, I describe and present the data used in the analysis. The empirical strategy and results are presented in Section 3. In Section 4, I examine the effects of mobile connectivity across heterogeneous groups. Also in Section 5 I explore the robustness of the main results to alternative hypotheses. Section 6 concludes the paper with a summary of findings.

# **2 Data**

This section presents an overview of the main datasets used in the analysis. The core datasets are mobile phone network coverage maps and nightlights data. The unit of analysis is the second administrative district in each country hereafter referred to as districts.

## **2.1 Global Mobile Phone Network Coverage**

Data on the global mobile network coverage comes from the Global System for Mobile Communications Association (GSMA) in collaboration with Collins Bartholomew, a digital mapping provider.<sup>4</sup> GSMA is an association of mobile network operators around the world. The data is compiled from network coverage submissions by mobile operators. In the recent past, additional coverage maps have been developed based on data from opencellid.org.<sup>5</sup> The data provides spatial coverage maps for three main technologies: 2G, 3G, and 4G mobile networks. From a technical standpoint, the main difference between these network technologies is that 2G mainly supports voice calls, SMS text messaging, and multimedia messages (MMS). However, in addition to these, 3G and 4G networks enable mobile broadband Internet services allowing users to utilize the Internet at a faster speed.<sup>6</sup> It is also important to emphasize that if a given location is covered by 3G, it is by definition covered by 2G, likewise 3G if covered by 4G. Therefore, in this paper, 3G and 4G coverage are hereafter referred
other one for mathematics. ENLACE (Y/N) takes the value of 1 if the student took the 12th grade exam in 2012 and 0 otherwise. The three groups are formed using the terciles of performance in ENLACE math 2009.

# **5.4 Interpretation**

The results presented here are consistent with other evidence on the effects of scholarships and conditional cash transfers in EMS education outcomes in Mexico. The most recent evidence identifies the importance of targeting to make scholarships effective. For instance, (Parker and Vogl 2018) rely on a difference-in-difference estimator to show that young children exposed to Progresa-Oportunidades, a well-targeted conditional cash transfer program, increase the likelihood of graduating from EMS. Using administrative data and a difference-in-difference identification strategy, (Araujo et al. 2018) also show a positive and significant effect on EMS graduation associated with the expansion of Oportunidades into urban areas. However, when the cash transfer is not well-targeted it has no effects on EMS graduation rates, as shown by (Dustan 2018) evaluation of “Prepa Si” a universal scholarship program for EMS students in Mexico City. (Dustan 2018) also finds that “Prepa Si” had no impact on test scores measured by the end of upper secondary ENLACE.

22
000 sq km, respectively).<sup>9</sup> **_Rail_** _lines density in terms of population_ , by contrast, is very high when compared to SSA, Middle East & North Africa, South Asia, East Asia Pacific, and Latin America & the Caribbean.  It is slightly higher than the average for all middle income and just below the benchmark upper-income group. South Africa’ _rail lines density in terms of land,_ however, is far behind those in middle income and high income countries and is also behind South Asia but ahead of other groups. Similar qualification for the interpretation of road densitiy applies here as well.

**_Quality—technical._** According to the database, South Africa has one of the lowest percentages of paved roads at 21%. This is lower than SSA (25%) and low income countries (30%) and far behind upper middle income (57%) and high income countries (82%). This may reflect the legacy of the strong urban bias and the economic configuration of economic activity inherited from the Apartheid era. Four important caveats are in order here. First, local information on paved roads show a higher percentage (31%) that was already achieved in 1994 (Perkins et al. 2005); as of writing, we could not reconcile this with the number from the benchmarking database that on this indicator originates in the World Bank’s World Development Report 2004. But even if the 31% figure is taken at face value, while it would imply better performance in South Africa than in SSA region, it would remain worse than in upper middle-income countries. Second, the quality of the paved roads variable itself probably hides considerable cross-country variation: paved roads in South Africa may be of higher quality than those in most SSA countries, but this variation would presumably be much less relevant for the upper middleincome group. Third, the proportion of roads paved may hide a relatively well developed road network
CIF (cost, insurance and freight) while exports are reported FOB (free on board), CIF costs are estimated and removed from imports values to compute FOB import values. Second, the reliability of country reporting is assessed based on the reporting distances among partners. These reporting qualities are used as weights in the reconciliation of each bilateral trade flow twice reported. Due to the use of this double information on each flow, BACI ends up covering a large set of countries not reporting at a given level of the product classification. The data set gives information about the value (in thousands of US dollars) and the quantity (in tons) of trade. Gaulier and Zignano (2010) provide

52
</sup> Exports covers all movements out of the country of the commodity in question. FAO also reports whenever possible trade in processed commodities expressed in the originating primary commodity equivalent. For example, wheat are imported as bulk grain and/or as processed (e.g. wheat flour, wheat bran, wheat germ, wheat gluten, etc). The value of imports and exports of these processed products in terms of the

> 12. However, as indicated by Tyers and Falvey (1989), this need not always be true in the case where significant and multiple distortions exist in the food sub-sectors.

> 13 The FAO data are based on official country trade statistics on standard trade classification (SITC). Trade data are included in both the Commodity Balance Databases (CBD) and the Supply and Utilization Database or Food Balance Sheets (SUA). The difference in the databases mainly concerns the processed products that are expressed in primary commodity equivalent in CBD whereas in SUA, trade accounts are shown by product weight, often in different detailed categories. However, the differences are relatively small and the grouping "total food" in CBD are very similar to those used in the supply and utilization accounts.

> 14 The import data are reported in terms of net weight, i.e. excluding the weight of the container.

12
inequality than equalizing parental background. The importance of credit access falls when students can supply work to pay for college (Garriga and Keightley 2007). Although Colombia is a large developing economy, the market for student loans is very limited, covering only 7 percent of students in 2003 (ICETEX 2010). Lack of family resources, limited opportunities to work during college, and missing credit markets for student loans are clear impediments to college access in countries such as Colombia.

In those countries, tuition subsidies may therefore expand college access. Our paper complements the literature on Chile’s recent free college policies (Bucarey 2018) and England’s free college elimination (Murphy et al 2019). It also joins in the vast literature of college financial aid in the U.S. (cited in Section 8.5), including the recent literature on free community college and the so-called “Promise” programs implemented in multiple U.S. states.

# **3 Data and institutional environment**

In this section we describe our data sources, Colombia’s higher education environment, and our study cohorts. Appendix A.1 contains further details.

Our data consists of student- and program-level information drawn from three administrative datasets: Saber 11, SPADIES, and SNIES. The Saber 11 dataset contains students’ test scores at the national mandatory high school exit exam (also named Saber 11) as well as selfreported socio-economic information. Saber 11 is a standardized test that measures a student’s preparedness for higher education—reflecting not only her innate ability but also her primary and secondary education quality. We use it as a measure of student ability, broadly understood as academic readiness for higher education. Family income is reported in brackets defined relative to the monthly legal minimum wage (MW), which is equal to 381,000 Colombian pesos (COP) in 2005 (US$ 1 = 2,321 COP in 2005).

SPADIES tracks every higher education student. For each semester, it records the
**Table 6. Normalized Magnitudes of the Effects of Education, Infrastructure and Data Governance on the Share of Adults Using Digital Payments during the Past Year (2021): Results from the Static and Dynamic Specifications with and without Interactions with the COVID-19 Shock**

|ExplanatoryVariables|(1)|(2)|(3)|
|---|---|---|---|
|(Standard deviations in<br>parentheses from Table 2)|Static model without<br>interactions (Table 3,<br>columns 3-8)|Dynamic model without<br>Interactions (Table 4, columns<br>3-8)|Dynamic model with<br>Interactions Computed at the<br>Average Sample Covid Shock<br>(Table 5, columns 1-6)|
||||Memo: Average Covid<br>Shock=7.62 in Table 2|
|Schooling (2.95)|15.98|3.12|2.58|
|Electricity (18.34)|4.45|2.03|1.46|
|Internet Use(24.53)|18.83|3.94|3.67|
|Mobile Subscriptions<br>(30.26)|-0.81|2.69|3.16|
|Enablers Data(2.96)|6.58|-0.46|-0.48|
|Safeguards Data(3.59)|8.80|0.59|0.90|
|Range of Adjusted R-<br>squares|0.38-0.62|0.89-0.93|0.89-0.94|

39
Policy Research Working Paper 10835

# **Abstract**

Understanding men’s and women’s time use is a key factor in addressing issues and formulating policies related to division of labor, domestic work, and related gender disparities. However, obtaining data on individuals’ time use can be difficult and costly in the context of household surveys. Leveraging unique survey data collected in rural Malawi, this study investigates the possibility of predicting men’s and women’s time allocation to an extensive set of activities, using sensor signal data captured by accelerometers. Using machine learning techniques, the study builds a supervised classification model that is trained on the accelerometer data and a random subset of the time use survey data to predict individuals’ time allocation to 12 broad activity groups.

The model can correctly classify each performed activity in 76 percent of the cases. The analysis shows that with 40 percent of the training data, this method can achieve 90 percent of the maximum level of predictive accuracy reached in the analysis. The findings prove the feasibility of this methodology and offer insights for enhancing both survey and accelerometer data collection processes to build better models. Using the method can improve the quality of costly and difficult to obtain time use surveys with cheaper, yet accurate, modeled estimates, obtained by combining objective data from wearable devices with time use data collected on smaller samples.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at tkilic@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.
does not reflect the intensity of the inadequacy of housing (i.e., how many dimensions are deficient) and therefore might be too simplistic for practical applicability. Results reported in the remainder of this paper are, therefore, based on the _AHI_ – if not otherwise explicitly stated as _<u>strict</u> Adequate Housing Index (sAHI)_ . Comparisons of _sAHI_ and _AHI_ at country level can be found in Annex 4.

While the _AHI_ offers an easy to interpret, unidimensional measure of housing adequacy that is comparable within and across countries, there are also several limitations. First, the _AHI,_ by its nature, reduces complexity and hence also reduces information on the seven adequacy dimensions. Composite indices like the _AHI_ are, therefore, often critiqued for only being able to convey “big picture” messages without being able to identify concreate policy actions (e.g., OECD 2008). Yet, while the _AHI_ reduces the visible size of the seven adequacy dimensions, we clearly present the underlying information for further analytical work that can rely on such disaggregation. As housing adequacy is a multidimensional concept which cannot solely be captured by one of the adequacy dimensions such as access to water, the _AHI_ provides a holistic and comprehensive
this common shock across sectors. The implication from the models I present is that, assuming cross-sectional differences in trade policy to be independent of cross-sectional differences in productivity is incorrect.

I employ Colombian data for the empirical tests of my theory. Colombia has been used in various studies (for example, Roberts 1996; Fernandes 2003; Melendez, Seim and Medina 2003) given that it provides a great natural experiment environment. Colombia experienced a drastic trade reform in the early 1990s and had a stable economy in this period without major crises. Given the existence of other studies using the same country, I get the chance to test my predictions with a comparable dataset. In Section 3, I discuss the trade policy in Colombia during the sample period of 1983 through 1998, and describe how we can rule out a uniform change in tariffs across

> 3For example, the infant industry argument is mentioned as an important motive for protection in Grossman and Horn (1988).

2
//datatopics.worldbank.org/debt/ids/ 21For example, WB World Development Indicators (WDI) accesses its debt data from the IMF Government Finance Statistics Yearbook (GFSY).

22 www.imf.org/en/Data/IMF-Finances

> 23 https://ebizprd.worldbank.org/

> 24 In a 2018 survey of 25 MDBs, 16 had policies on public communication or disclosure (Engen and Prizzon, 2018). 25 According to the IMF/WB G20 Operational Guidelines for Sustainable Financing – Survey Results and Policy Recommendation: “Most countries provide information on their lending on the web, but it is not being done in a
62|0.0105|||16.40|-0.1516|

* Data are from Lu et al.<sup>35</sup> , except in the case of Japan and Singapore, where the numbers are from Imai<sup>18</sup> and the World Bank’s World Development Indicators respectively, and the OECD averages, which were computed from the OECD’s Health Statistics database in the cases of Western doctor visits and inpatient admissions, and Masseria and van Doorslaer<sup>36</sup> in the case of GP and specialist visits. OECD averages are based on 15-19 countries, depending on the variable. Figures for Hong Kong (China) refer to 2002, Japan’s and Korea’s to 1998, Singapore’s to 1994, and those for Taiwan (China) to 2001. Japan’s figure for Western physician consultations may include visits to traditional providers. The concentration index measures the extent to which use is higher among higher income groups: a positive value indicates that, on average, the better off make greater use of services; a negative index indicates the opposite.<sup>37</sup> The OECD concentration indices are from Masseria and van Doorslaer.
Policy Research Working Paper 9000

# **Abstract**

Many studies have demonstrated that Mexico’s conditional cash transfer program, PROSPERA, has substantial effects on educational attainment. Nevertheless, little evidence exists on whether increases in time spent in school have led to higher learning in the context of the poor areas where PROSPERA principally operates, which tend to have overall low school quality. This study combines data from nationwide achievement tests with administrative data on PROSPERA beneficiaries to estimate impacts on achievement tests. The analysis finds significant effects on learning, as measured by standardized achievement tests, on the order of magnitude of 0.05 to standard deviation, with larger effects for indigenous children. The analysis also confirms large effects on enrollment in secondary and high school, using administrative school enrollment data rather than self-reported household-level data, as generally used in previous studies. Finally, given the existence of several alternative tracks in secondary and high school, the study also examines where PROSPERA beneficiaries enroll. The findings show that most of the increase in enrollment occurs in tele-secondary schools and, at the high school level, in general high schools.

This paper is a product of the Social Protection and Jobs Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted atswparker@umd.edu and the coordinators of the studies may be contacted at cavilaparra@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
, inequality, and poverty nexus in SSA, Thorbecke (2023, p ii81) writes that “ _It is evident that economic growth in SSA in the past two decades has not been sufficiently inclusive_ ”, while Fosu (2023, p ii167) states that “ _Nonetheless, despite the apparent decline in poverty in SSA during the latter period_ [SSA’s growth resurgence period since the late-1990s], _the pace has been slow compared to other regions_ ”. In short, there is a widespread belief that economic growth in SSA has been less poverty-reducing relative to other regions or, in other words, that the growth elasticity of poverty – the percentage change in poverty rates for a percent change in a per capita indicator of economic output – in SSA has been systematically lower.

This paper uses the most up-to-date database of comparable growth spells in developing countries between the early 1980s and 2021 to test this assertion and identify the main factors that make economic growth in SSA less poverty reducing. Compared to the early research on the growth elasticity of poverty (henceforth GEP),<sup>3</sup> we take advantage of important progress made in recent years in the quality and availability of household data. In particular, we benefit from larger sample size and cover a larger share of the developing world’s population thanks to the strong increase in the number of household surveys since the early 2000s, including in previously un- or under-covered countries. We also benefit from the efforts to enhance intertemporal comparability of surveys within countries and include only comparable poverty estimates. While the main focus of the paper is on assessing regional differences in the GEP, the paper seeks to address three main questions: (i) does SSA, the continent with the highest poverty rate and also the slowest progress on poverty reduction, have a GEP that is significantly lower relative to the

> 2 In this paper, we consider the region as a whole (Sub-Saharan Africa). The reader is advised that there is large heterogeneity and several non-resource rich