rafmacalaba/data-use-mentions-extended huggingface dataset:
|5.4|8.6|
||q1/q5|0.95|-|0.94|0.98|-|0.14|0.11|0.14|0.16|
||q5-q1|3.2||4.0|1.7||26.0|30.3|33.1|46.5|
|SBA|All groups|14.1|-|19.8|20.1||4.7|4.7|8.6|15.5|
||q5 Richest|15.4|-|24.5|23.9|-|20.9|21.2|40.2|55.6|
||q1 Poorest|11.6|-|18.6|18.5|-|0.9|0.7|1.7|4.5|
||q1/q5|0.75|-|0.76|0.77|-|0.04|0.03|0.04|0.08|
||q5-q1|3.8|-|5.9|5.4|-|20.0|20.5|38.5|51.1|

Source: Authors’ computation from administrative woreda level data and DHS data. Notes: _Horizontal equity:_ Data trimmed at the 1<sup>st</sup> and 99<sup>th</sup> percentiles; excludes Somali region. Relative inequality (q1/q5) compares the bottom 20% to top 20% of woredas. Absolute inequality (q5-q1) is their difference. _Vertical equity:_ Vertical equity data exclude Addis Ababa. Relative inequality compares the bottom 20% to the top 20% of households. Absolute inequality is their difference.

31
development and national growth. Using data for firms from around the world, we contribute to this debate by studying whether the link also exists at the firm level.

There are four key findings in this paper. First, firms experience a boost in sales and asset growth and an increase in productive capabilities—tangible assets, intangible assets, and employment—when raising funds in capital markets. These findings indicate that firms are not using the new funds just to change their capital structure or increase financial investments. Second, firms that are ex ante more likely to be financially constrained—small, young, and high-R&D firms— experience a larger boost in growth around issuance. These findings are consistent with the idea that capital markets allow financially constrained firms to relax their funding restrictions and grow. Furthermore, equity (but not bond) issuances have an especially strong, positive link with intangible investments by high-R&D firms. This finding contributes to research on the question of whether equity markets have a comparative advantage in funding more opaque investments. Third, countries with greater capital market development have a higher proportion of smaller, younger, and innovative listed firms than more bank-based countries. Furthermore, firms in countries with relatively welldeveloped capital markets experience a larger boost in growth when issuing securities than firms in bank-based financial systems. These findings contribute to research on debates concerning the connections between financial architecture and financing of different types of firms and activities. Fourth, firms that experience exogeneous changes in growth opportunities are more likely to issue securities in capital markets. This is consistent with the view that firms use capital markets to realize growth opportunities by expanding their productive capabilities.

The positive association between firms issuing securities and firm growth sheds new light on the debates concerning the mechanisms through which capital markets might influence growth. The evidence in this paper suggest that it is not just that firms grow faster in countries with better functioning capital markets. Rather, we find that issuers (especially smaller, younger, and higher-R&D 30
significantly lower, possibly driven by the lower qualifications required for pre-primary teachers.

Based on the 2013 IE data, we find that compared to public schools, teachers are less likely to be absent and more likely to be in class and teaching in private schools. As a part of data collection, schools were visited in February-March 2013 for headteacher and teacher surveys and again in October 2013 for unannounced visits to measure teacher attendance. During unannounced visits, nearly 20 percent of public-school teachers were absent, as compared to only 7 percent of private school teachers. The likelihood of a teacher being in school but out of class was higher in public schools than private schools (41 and 30 percent, respectively). Similarly, the likelihood of a teacher being in class but not teaching was higher in public schools relative to private schools (13 and 6 percent, respectively). All these differences are statistically significant. In addition, when data are disaggregated by level, a public preprimary/primary school is likely to have 30 more students per teacher than a private preprimary/primary school. However, there is no discernable difference between public schools and private schools at the secondary level (Table 2).

12
to generate moral hazard.<sup>8</sup> To provide additional motivating evidence for our study, we conducted a nationwide online survey in India with a sample of respondents representative of the population of consumer loan borrowers in terms of age, income, and gender. In the survey, we elicited borrower opinions about the likely effect of debt forbearance policies.<sup>9</sup> The results show that even in the case of an aggregate shock where forbearance can be reasonably assumed to benefit primarily borrowers in genuine financial distress, respondents overwhelmingly expect that repayment deferrals will generate moral hazard and damage overall credit discipline.

Figure 1 summarizes the main results of the survey.<sup>10</sup> We first asked borrowers whether they

> 7 Lenders were allowed to continue charging interest. The issue of compound interest during the moratorium, i.e. interest on accrued interest, was argued before the Supreme Court of India. Ultimately, borrowers ended up paying simple interest, with the government reimbursing lenders for the difference between simple and compound interest.

> 8 In addition to the debate on repayment flexibility for individual borrowers, there has also been a debate on _regulatory forbearance_ , which describes policies that allow banks to postpone the recognition of credit risks. Chari et al. (2021) examine the effects of such policies enacted in the aftermath of the 2007-2009 global financial crisis in India and show that they led to widespread incentive distortions, “evergreening” of de facto non-performing loans, and credit misallocation.

> 9 Our approach for this descriptive exercise is similar to recent work that has used online surveys with representative populations to examine how people form opinions about social issues and public polices (see, for example, Stantcheva, 2020). We report descriptive statistics for the survey population in Table B.1 in the Supplementary Appendix. Table B.2 provides summary statistics on the responses to the online survey. 10 The full survey instrument and additional results are available in Table B.1 in
rich to meet social obligations. On the other hand, 64 percent of land sales were undertaken in order to generate capital for productive investment (digging of wells, purchase of pump sets, and children's education), and the land sales market actually contributed to a slight equalization of the land-ownership distribution. This suggests that in this case the poor were not only able to avoid distress sales, but were able, through access to cash-generating employment, to acquire some land as rich households liquidated agricultural assets to be able to pursue non-agricultural investment. Survey data on land transactions from India indicate that purchases of land are almost all undertaken by individuals with access to sources of income which are not correlated with agricultural production and that borrowing to finance agricultural land acquisition is virtually non-existent (Sarap 1995).

> 34 Distress sales have been important in China (Shih 1992) in early Japan (Takekoshi 1967) in the Indian Punjab (Hamid 1983), and **in** Latin America following the abolition of communal tenure (Brockett 1984).

21
13.5%<br>2016 2017 2018 2019<br>All Rural Urban<br><!-- End of picture text --> _Source: World Bank estimates based on EHPM (2016-2019)_ _Figure 7: The development of risk-induced versus poverty-induced vulnerability over time_ <!-- Start of picture text --> Risk-induced over poverty-induced ratio (2016-2019)<br>3.0 2.7<br>2.5 2.3<br>2.1<br>1.8<br>2.0 1.7<br>1.5 1.2 1.3 1.4<br>1.1<br>0.9 1.0<br>0.8<br>1.0<br>0.5<br>0.0<br>2016 2017 2018 2019<br>All Rural Urban<br><!-- End of picture text --> _Source: World Bank estimates based on EHPM (2016-2019)_ 15
segment is uniquely associated with a country, and its size indicates the total number of incoming and outgoing migrants in that country. The direction of the flow is encoded

> 15 To conduct this analysis, we followed four steps. First, we reconstructed the network of internal movements at the monthly level (similar to what was done in figure 8). Because we are interested in short-term dynamics, we used monthly data on migrants registered at the FMPs. Second, we identified significant spatial concentrations of migrants at FMPs located near each other. Specifically, we tested whether same-nationality migrant clusters occurred more frequently than would be expected if FMPs were distributed in a spatially random way. To this end, we used the join count test for k-colored factors (Cliff and Ori, 1981; Upton and Fingleton, 1985). We defined close FMPs as those located within a radius of 250 km, which is the minimum radius required to make sure that each FMP has at least one neighboring FMP, a condition that is necessary for implementation of the join count. Because only 5% of the FMPs are located more than 250 km from all the others, we dropped them from the sample. Third, for each month, _t_ , we identified the nationalities for which the test indicated significant spatial concentration. Fourth, for each of these nationalities, we plotted the location of the FMPs within 250 km of each other where the majority of the arrivals corresponded to nationality _i_ at time _t_ .

> 16 The distance between these two cities is more than 1,000 km, more than four times the radius used to detect migrant agglomerations, which means that the test evaluated the movements in these provinces separately and indicates that the two effects are independent.

> 17 This exercise requires assuming that migrants found in an FMP are all moving along the same route and are all trying to reach the most-preferred country of arrival recorded at the FMP. The assumption is instrumental to identifying the migration network passing through Libya, which is discussed below in Section 5
Aidoo. (2003). Measuring mental health in a cost-effective manner. _Health Policy Plan._ September; 18(3): 344-9.

- Hurley K., M. Black, M. Papas, L. Caulfield. (2008). Maternal symptoms of stress, depression, and anxiety are related to nonresponsive feeding styles in a statewide sample of WIC participants. _Journal of Nutrition_ ; 138: 799-805.

- Hay, D.F., Pawlby, S., Sharp, D., Asten, P., Mills, A., Kumar, R. 2001. Intellectual problems shown 11-year-old children whose mothers had postnatal depression. _Journal of Child Psychology and Psychiatry_ ; 42, 871–890.

Kurstjens, S. and Wolke, D. 2001. Maternal depression on cognitive development of children over the first seven years of life. _Journal of Child Psychology and Psychiatry_ , 43(5), 623-636.

- Lovejoy, M., P. Graczyk, E. O’Hare, and G. Neuman. 2000. Maternal depression and parenting behavior: a meta-analysic review. _Clinical Psychology Review_ , 20(5), 561-592.

22
with: where _φ_ ( _x_ ; _m, v_ ) is a normal density function with mean _m_ and variance _v_ evaluated at ̃ the value _x_ . Note that when the variance of the error term tends to zero (i.e. _σ_<sup>2</sup> _→_ 0), the conditional mean _E_ [log _yi|_ log ̃ _xi_ ] will tend to log ̃ _xi_ while the conditional variance will tend to zero, as they should.

A practical way to proceed is to draw an observation of NSS log consumption from the conditional distribution _Fy|x_ for each household, and evaluate the welfare measures of interest. We draw 50 observations of NSS-type log consumption for each household in the CPHS sample, and then compute the aggregate welfare indicator (i.e. poverty and inequality) for each _k_ = 1 _, . . . ,_ 50. The mean and standard deviation evaluated over the _K_ realizations will serve as the point estimate and standard error of the welfare indicator.

Alternatively, when measuring head-count poverty for example, one could evaluate for each household the probability that their NSS log consumption is below the poverty line conditional on the observation of their CPHS log consumption value -- and then compute the mean value of these probabilities across all households in the sample. Let the poverty line for log consumption be denoted by _z_ . The probability that household _i_ is poor equals: where Φ is the standard normal distribution function. Head-count poverty can then be estimated by: where _wi_ denote survey weights that are assumed to sum up to 1.

# _Implementation_

The assumed model (see eq. 2) contains three parameters: _a_ , _b_ , and _σ_<sup>2</sup> . As described above, a minimum of three moments (for both the NSS and CPHS log consumption data) are required to estimate all three of these parameters. All three moments of the CPHS log consumption distribution can readily be estimated using the observed CPHS consumption data. Estimation
>15</sup>

- (c) _Roads_ : Total length of the road network (in kilometers), obtained from the International Road Federation‘s World Road Statistics, and complemented with information from

> 13 For instance, in our panel data set the full-sample correlation between the total number of phone lines (main and mobile) and overall power generation capacity is 0.92, while the correlation between total road length and overall power generation capacity is 0.65, and that between road length and main telephone lines is 0.61. In turn, the correlation between paved (as opposed to total) road length and power generation capacity is 0.83, while that between paved road length and main telephone lines is 0.84.

> 14 A similar approach is employed by Alesina and Perotti (1996) in their analysis of investment determinants, and by Sánchez-Robles (1998) to assess the growth effects of infrastructure.

> 15 The International Energy Annual (IEA) is the Energy Information Administration‘s main report of international energy statistics, with annual information on petroleum, natural gas, coal and electricity beginning in the year 1980. See webpage: http://www.eia.doe.gov/iea/

7
indicators. Recent work has for example examined sources of measurement error in agricultural data (e.g. Beegle et al., 2012; Rosenzweig and Udry, 2019) and measurement methods for women’s agency (e.g. Donald et al., 2020; Jayachandran et al., 2021).

_Estimating structural models._ Structural models can exploit the experimental variation for identification and enrich experimental data, for example by evaluating the role of underlying preferences and behavioral factors for take-up decisions as a means to conduct welfare analysis or make predictions for the effects of new policies (e.g. Todd and Wolpin, 2006, 2010; Meghir et al., 2019; Guiteras et al., 2019).

_Statistical learning and machine learning._ A rapidly growing literature applies machine learning methods to RCT data. Examples include regularization methods to discipline covariate selection or the identification of treatment effect heterogeneity (e.g. Chernozhukov et al., 2018; Chernozhukov et al., 2018), and new sampling methods, e.g. multi-arm bandits and related adaptive experimental algorithms (e.g. Dimakopoulou et al. (2018); Caria et al. (2021); Kasy and Sautmann (2021)). Existing RCT datasets can be used to optimize algorithms, check large- or small-sample behavior, and simulate thousands of trials to improve speed and evaluate performance without incurring costs or burdening subjects.

> 4An issue potentially exacerbated by biases inherent to the publication process (Andrews and Kasy, 2019).

4
18 <mark>Di Giuseppe, and Zezza, 2017), and explore the prevalence, patterns and performance of rural nonfarm household enterprises (Nagler and Naudé, 2017). Together they provide key micro stylized facts to inform policy directions for the structural transformation of rural Africa.</mark> _The agricultural labor productivity gap is smaller than commonly assumed and mainly due to underemployment, not intrinsic lower productivity in agriculture._ One common view, especially popular among macro economists, is that labor is intrinsically far less productive in agriculture than elsewhere in the economy, and that a great deal is thus to be gained from transferring labor out of agriculture, i.e. from accelerating the structural transformation. This view finds support in the national accounts which show that, in Africa, value added per nonagricultural worker is six times larger than the value added per agricultural worker. In developing countries, the ratio is 3.5.

Yet, this does not account for differences in human capital and income diversification across sectors. Recent work by Gollin, Lagakos and Waugh (2014) shows that adjusting for those factors would bring the ratio down to 3.3 for Africa (and 2.2 for developing countries). But even this may be misleading. In addition to neglecting differences in capital use across sectors, these macro numbers do not account very well for production for own consumption, which in developing countries makes up a substantial share of agricultural output as shown by the agricultural commercialization figures above. Using output measures from micro data instead reduces the gap by another 20 percent in the ten countries for which Gollin, Lagakos and Waugh (2014) had data.

The paper by McCullough (2017) in the special issue takes the argument one step further still. Instead of comparing (micro based) output estimates _per worker_ , she also uses detailed micro data on hours worked in both primary and secondary occupations to measure and compare labor productivity _per hour worked_ . Doing so shrinks the labor productivity gap to 1.6 on
might increase total fatalities rather than reduce them. However, empirical analyses focusing on the safety regulation of motorized vehicles have generated mixed results. For instance, Cohen and Einav (2003) found that while seat belts usage has a small negative effect on traffic fatalities, there is no evidence suggesting that seat belts usage increases reckless driving. In contrast, using data from recreational boats, McCarthy and Talley (1999) found that the passenger was less likely to wear a personal floatation device (PFD) when the driver had received a formal training. Oza (2009) hypothesizes that the introduction of over-the-counter access to the “morning after pill” lowered the risk of unintended pregnancies, but also lowered the opportunity costs of unprotected sex. She shows that for women who were exposed to the policy, abortion rates decreased while STIs increased, corroborating disinhibition behaviors.

> 8For example, Rao, Gupta, Lokshin, and Jana (2003) found that prostitutes in Calcutta, India who agree to condom-free sex are paid more, implying that clients derive more satisfaction from condom-free sex. Gertler, Shah, and Bertozzi (2005) found similar results using data from the states of Morelos and Michoacan in Mexico.

> 9We abstract from other sexually transmitted diseases and birth control.
to the timing of data collection. As this paper shows, farmers’ labor allocation across crop and livestock products, and hence their self-reported intended use of output, can vary significantly over the course of the agricultural production cycle, and this poses challenges for cross-country and cross-survey comparability of rural employment statistics, especially if the data are collected at different points in time during the agricultural year. Our results suggest that end-of-season surveys may lead to higher employment ratios and labor force participation rates than surveys conducted at the beginning of the season, but these patterns are not necessarily generalizable to all parts of Sub-Saharan Africa. Moreover, there may be important nonlinearities over the course of the season, with farmers potentially allocating most of their labor to seasonal crop production during harvest time, followed by a sharp drop-off and reallocation of labor from crops to livestock thereafter. Since the end-of-season data used in this paper were collected, for the most part, after the main harvest they are not well suited to reveal such non-linearities. It would be useful for future

> 34 How this can be achieved is an open question, especially in the context of academic studies which are used to having a single indicator (e.g. employment or labor force participation) of the extensive margin of labor supply.

23
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

**Acknowledgments.** We thank Horeja Cham, Lamin Dibba, Kristen Himelein, Kai Kaiser, Johan Mistiaen, Ryosuke Shibasaki, Matarr Touray, Tara Vishwanath, and participants at the UN 2020 conference on Big Data, World Bank Poverty & Equity Brown Bang Lunch, and anonymous reviewers for helpful comments.

**Funding statement.** This study received support from the World Bank Trust Fund for Statistical Capacity Building III (TFSCB-III) which is supported by the United Kingdom’s Foreign, Commonwealth & Development Office, the Department of Foreign Affairs and Trade of Ireland, and the Governments of Canada and Korea.

**Competing interests.** We declare that we have no relevant or material financial interests that relate to the research described in this paper. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank or any affiliated organizations, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work.

**Data availability statement.** The program codes and the aggregated statistics (if possible and currently under negotiations) will be made available through the GitHub platform (link <u>here : https://github.com/worldbank/covid-mobiledata/tree/1b9f114abc9231964d9109f62df29a146912b4a2/cdr-aggregation#summary-of-indicators).</u>

**Ethical standards.** The research meets all ethical guidelines, including adherence to the legal requirements of The Gambia. In particular, the team only accessed de-identified, anonymized and aggregate CDR data.

**Author contributions.** AA and AW processed the aggregate CDR data to produce key statistics, prepared a first draft of the data section, and approved the final version of the manuscript. EK and MM contributed to the analysis, revised the first draft and approved the final version.

**Supplementary materials.**
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