rafmacalaba/data-use-mentions-extended huggingface dataset:
can, presumably, provide informal childcare services in the absence of formal providers. Indonesia is the fourth most populous country and is one of the fastest-growing economies in the world. However, in 2016, female labor force participation (FLFP) in Indonesia stood at 50.9 percent, lagging other countries in East Asia and the Pacific where the average FLFP is 60.8 percent, and stagnating from 50.2 percent participation in 1990. Prior to the recognition of pre-primary education in the national education system in 2003, only a quarter of children were enrolled in preschools, lagging behind the world’s average of 32.1 percent, the regional average of 43.3 percent, and the OECD average of 73.3 percent (World Development Indicators). We complement the growing body of research on low- and middleincome countries (see Mateo Diaz and Rodriguez-Chamussy 2016 for a survey of studies in Latin 2
in the labor force. Results are shown in Table 13 and confirm our previous finding.

# **6 Conclusion**

This article provides new evidence on the impact of internet adoption on female labor force participation in a developing country. We use individual panel data for Jordan and link the change in labor force participation with a variable indicating internet adoption or continuation between 2010 and 2016. We use the interaction between the distance to 3G cellular towers and a proxy for internet prices as an instrument for internet adoption. We argue that this instrument satisfies the exclusion restriction since areas closer to the towers exhibited similar trends in female labor market outcomes before the roll-out of the 3G technology than areas farther away.

Our results provide new evidence that internet adoption has a positive effect on the labor force participation of women in developing countries. Since the impacts on men are not statistically significant, these findings imply that the internet contributes to reduce gender gaps in labor force participation. This is particularly important in the context of Jordan, where women’s labor force participation is one of the lowest in the world. We also find that internet adoption leads women to change their job search strategy as they start using the web to look for jobs. However, this seems to explain only part of the overall increase in labor participation. Our findings indicate that the decline in gender gaps in labor force participation is accompanied by a reduction in gender-biased social norms, marriage and fertility.

On average, the internet does not have positive impacts on the likelihood of having a job. This, in combination with the large increase in activity, imply that unemployment rates of women increase on average and across groups. Only unmarried women and those with high school education or more experience an increase in employment.

Our findings suggest that the design of policies to promote the adoption of new technolo16
- MICS - by Geopolitical Zone - MICS<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>MICS 2016/2017_NC MICS 2016/2017_NE MICS 2016/2017_NW MICS 2016/2017_NC MICS 2016/2017_NE MICS 2016/2017_NW<br>MICS 2016/2017_SE MICS 2016/2017_SS MICS 2016/2017_SW MICS 2016/2017_SE MICS 2016/2017_SS MICS 2016/2017_SW<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Wave 1-4 and MICS 2016/17.

Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age.

Comparing state level severe education deprivation rates in Figure 9 with state level non-enrollment rate in Figures 13 and 14 shows that high shares of education deprivation do not necessarily imply high dropout rates, the difference between the shares displayed in Figures 10 and 13/14 for those who ever attended school. Rather it seems to suggest, that while children in some states may be less severely deprived of education, it does not inform about the duration of schooling for those non-deprived children, leaving quality all aside. Overall, however, state level school non-enrollment rates in Figures 13, 14 and 15 largely display the same urban rural and north-south pattern as estimates of severe education deprivation in Figures 9 and 10 though with even higher inter-state discrepancies. The highest share of children of school going age not in school is found in Bauchi, where 68 percent of rural girls and 66 percent of rural boys are out of school, meaning only roughly one third of rural children is attending school. It is worth noting though that there is
in the RD-approach is smaller than the survey estimations imply, hence (self-)selection could create a bias in the survey results. In terms of impact on repetition, the study does not find a statistically significant impact.

- (iii) <u>Impact on part time job: Exploring both the SOFES survey (external margin) and the SOFES database</u> (internal margin), we find that SOFES recipients work more often than students without a loan.  The survey data suggest that the likelihood of working on the side increases by 8 percent, while the SOFES database suggests that part-time employment increases by around 30 percentage-points for master students. This is consistent with the hypothesis that students in the SOFES program are still credit constrained, and therefore have to generate income while studying.

<u>(iv) Impact on career choice:</u> There seems to be no clear direction of impact. The two estimation methods and data sources suggest different impacts and are not systematically statistically significant. The RD approach for master students using the SOFES database marginally corroborates the pattern found analyzing the survey data: an impact of student loans shifting away from Economics and Administration toward Humanities and Social Sciences (and perhaps also to Engineering and Natural Sciences). However, some of the RD-estimations for bachelor students contradict this pattern.

For the survey data, it should be noted that the treatment group of SOFES beneficiaries differs from the control group on a series of important characteristics. In accordance with the organization’s objectives: (i) The SOFES recipients tend to come from less favorable backgrounds than non-SOFES students: family income of non-SOFES students exceeds that of SOFES students by 48%, the parents’ education level is higher for nonSOFES students than for SOFES-beneficiaries, and 51% of SOFES beneficiaries attended public upper secondary education compared with 37% for non-SOFES beneficiaries; and (ii) despite the above differences, the SOFES beneficiaries tend to be slightly more talented in terms of grade point average. These factors point to
# Section 3.2. Financial inclusion in the Sahel

Penetration of financial institutions is generally low, and most households do not hold savings, although there is sizeable variation between the five EHCVM countries. Access to financial markets may help households cope with the effects of seasonality through credit or insurance products, while savings may enable households to smooth any losses in consumption that arise in the lean season. Pooling data from across the EHVCM sample, around 28.9 percent of the population live in a household in which any member held an account with a financial institution, and just 16.1 percent of the population live in a household in which any member actually held savings (Figure 4).<sup>18</sup> Nevertheless, financial inclusion differs significantly between the five countries, with more than half of the population of Burkina Faso and Senegal living in a household where someone holds an account compared to less than 1 in 10 in Niger and Chad. This higher financial inclusion in Burkina Faso and Senegal seems to be driven largely by the penetration of mobile banking, the most prevalent form of account in the two countries.<sup>19</sup>

**Figure 4. Share of the population with access to financial institutions and savings, by country**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Regular bank Postal Rural savings Mobile Stored-value Any Any (with<br>association / banking / prepaid savings)<br>microfinance card<br>Burkina Faso Mali Niger Senegal Chad All countries<br>(percent)<br>Share of the population living in a household with such an account<br><!-- End of picture text --> Note: Individual-level sampling weights applied. Statistics capture whether any household member had an account or had savings. Source: EHCVM and World Bank estimates.
an interaction effect that arises from the possible correlation between sectoral gains and population shifts. The formula can be specified as follows: <!-- Start of picture text --> 2 1 )<br>ii2 ii1 ii1 ii2 ii1 ii1 ii2 ii1 ii2 ii1<br>II −II = ∑(II −II )nn + ∑(nn −nn )II + ∑(II −II )(nn −nn<br>Intra-sectoral Population Interaction<br>effects shift effects effects<br><!-- End of picture text --> where i is the informality rate in sector i with an employment share in year t. ii To ensure comparability, we select the years 2006, 2010 and 2016 from all surveys for the II nn decomposition, with the exception of the LFS for which there is only data for 2010-2015. We aggregate sectors into four groups: i) agriculture and mining; ii) manufacturing; iii) non-public services; and iv) public services.<sup>14</sup> The decomposition results suggest that de-industrialization does not explain the evolution of rates informal employment in the past decade. Figure 7 shows that the intra-sectoral effect dominates the change in informal employment for both 2006-2010 and 2010-2016, implying that informality rates have increased in all sectors, whereas the relative employment distribution across sectors has not changed much.<sup>15</sup> In other words, the change in informality is driven by higher incidence of informal employment across all sectors, and not by shifts in employment towards high-incidence sectors.<sup>16</sup> Alternatively, it could be argued that instead of economic activities or sectors, it is changes in demographic characteristics that explain changes in informality. For example, if women are on average more likely to be informal, an increase in informal employment could be driven by a higher share of women among the employed, or by women becoming even more likely to be

> 14 In the ESS, LFS and
<br>st by the time of CFUWB<br>lost from R1 to R2.<br>all sample size.<br>assuming the same wor|25.2<br>ES2 is predicted by aggreg<br>kers aged / assuming the ag|25.5<br>ating modeled jobs lost i<br>e structure of employm|23.3<br>n CFUWBES 1 and<br>ent is replicated|

Jordan female PFPS workers tend to be highly selected among the female working population; pre-COVID, 36
# **Acknowledgments**

We are grateful to the Mato Grosso State Foundation of the Environment (FEMA) for sharing the deforestation data used here and to Dr. Rodrigo Justus de Brito, for advice and assistance. Gabriel Azevedo, Garo Batmanian and Daniel Nepstad provided helpful comments. This work was supported in part by a German Consultant Trust Fund and by the German Research Association (DFG), Graduate Program “Environmental & Resource Economics”, University of Heidelberg.

3
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _2_ Low inflation and price stability have historically been associated with improved growth and development outcomes (The World Bank, 2019; Ha et al., 2019b,a). Inflation dis-proportionally taxes the purchasing power of low incomes (Easterly and Fischer, 2001), which is in contrast with the proportional income effects of economic growth itself (Dollar and Kraay, 2002). As such, price stability and low inflation are crucial to achieve the Sustainable Development Goals and price data are critical for economic monitoring.

Food prices specifically play a critical role in determining the immediate ability of households to consume food (Andreyeva et al., 2010; Waterlander et al., 2019), and the capacity of farmers to plan the investments needed for reliable future production of food (Koomen et al., 2015; Andr ́ee et al., 2017; Diogo et al., 2017). Food price inflation thus serves as an important metric to inform economic policy and is closely watched by both economists and humanitarians during times of financial turmoil and economic crisis.<sup>1</sup> The traditional figures on inflation used for these purposes have primarily been produced at an aggregate level using data from few, highly urbanized, areas. As such they do not directly describe price evolution in rural or poverty-stricken areas, or in regions where vast populations are internally displaced and live in fragile situations.<sup>2</sup> For example, food crises are often characterized by great spatial heterogeneity and geographic specificity (Maxwell et al., 2020), while traditional price indicators do not provide insights beyond the major (urban) markets where prices are formally measured.<sup>3</sup> Traditional indicators are also often produced with delay, or not at all, particularly during crises when economic variables deteriorate rapidly.<sup>4</sup> Recognizing these limitations, international institutions have invested heavily

> 1The
protection benefits of the mangrove are derived because mangroves grow in areas that are naturally more sheltered, for example, areas that lay in a steeper continental shelf, we begin by excluding from the analysis areas that have not historically supported mangrove habitats. Specifically, for every cell in the storm surge prone area we exclude cells that have no mangrove, as defined by Spalding et al. (1997), in their

> 4Nordhaus (2006) considers areas with elevation less than 8 meters as vulnerable to storm surge. We use a less stringent definition because Shuttle Radar Topography Mission (SRTM) elevation estimates below 10 meters are not considered reliable (McGranahan et al., 2007). We thank Eric Strobl for providing us with GIS boundaries for global coastal lowlands constructed from STRM data.

7
of the evaluation sample to 78% of the sample. However, after the release of the payment records in late August 2000, it was discovered that in the evaluation sample, many of the households that were supposed to be added to the updated list of beneficiaries had not received any cash benefits since the start of the distribution of program benefits in these localities.

Specifically, in the treatment localities 27% of the total eligible population had not received any benefits by March 2000. After crosschecking this with the PROGRESA administration it was confirmed that the explanation for this was due to the fact that the majority (85.7%) of the households not receiving any benefits, due to some administrative error, had never been incorporated into the program. All of these “forgotten” households were households with a revised eligibility status from non-beneficiary to eligible beneficiary as a result of the revision of the selection process (densification). Given the intricacy of what constitutes an eligible household we have decided to exclude from our sample those households that were later reclassified as eligible and received nonzero cash transfers during the three years covered by the evaluation surveys (October 1997-November 1999).<sup>9</sup> Thus our set of eligible households includes the original households classified as such using PROGRESA’s early selection methods while the set of non-eligible includes all of the “forgotten” households with revised eligibility status that never received any cash benefits.

# **3. The Regression Discontinuity Design applied to PROGRESA**

The RD design applied in this paper is based on the discontinuity in the eligibility way household-specific discriminant scores were calculated.

9 These households were identified from the administrative records of PROGRESA that contained information on the bi-monthly payments sent out to beneficiary households by the headquarters of the program in Mexico City.

7
at either time horizon. This is consistent with the qualitative assessment, which noted that training did not appear to change individual or household decision-making dynamics (ICRW, 2015).

The third domain we examine is subjective well-being and mental health. We measure subjective well-being today and anticipated subjective standard of living in 5 years’ time on a Cantril ladder, and mental health using the MHI-5 index of Veit and Ware (scored so that higher scores indicate better mental health). Respondents show a great deal of optimism about the future, seeing themselves as being on step 5.0 out of 10 on the life ladder currently, but expecting to be on step 8.0 in five years’ time. Training increases both current and future subjective well-being by 0.2 to 15
**Figure 1: Illustration of field damage as indicated by satellite imagery compared to ACLED data**

_Note:_ The data are for July 4-17, 2022, in Novosilka city in Donetsk oblast. The boundaries of settlements are in white; fields that sustained damage from fighting are in red; and conflict activity reports about conflict activity as reported by the Armed Conflict Location and Event Data (ACLED), the methodology for which is described in more detail by Raleigh et al. (2010).

25
||||||
|food services<br>Information and|38.8|3.6|19.0|5.0|7.9|26.0|
|communication|0.0|10.4|3.5|18.8|7.5|7.5|
|Other services|15.1|3.8|15.1|0.0|0.0|0.0|
|Total<br>Source: CMMHH 2021.|23.3|5.8|16.7|11.6|8.0|18.7|

_Table 5-11 Georgia: Percentage of Permanent Formal Private Sector Jobs Lost by Sector, Model Predicted and Survey-Measured Projections, CFUWBES1 (July 2020)_

||Econometric<br>Model Projections|Survey<br>Measured|No. Of<br>observations|
|---|---|---|---|
|1. Chemicals|6.0|30.3|7|
|2. Food, drink, and tobacco|7.7|13.2|98|
|3. Garments<br>4. Hotel, restaurants, and|33.4|44.4|8|
|transportation<br>5. Machinery, electronics, and|27.4|39.9|99|
|construction|23.9|18.9|54|
|6. Metals and non-metallic minerals;||||
|Plastic and rubber|8.4|5.2|40|
|7. Wholesale, retail, and other<br>services|15.0|8.2|164|
|8. Wood, paper, publishing, and<br>printing|26.8|17.5|14|
|Total|19.0|18.1|484|

Sources: LFS
732***|0.813***|-0.081***|
||(0.025)|(0.010)|(0.024)|(0.024)|(0.011)|(0.024)|
|Model 2|0.732***|0.788***|-0.056***|0.732***|0.793***|-0.061***|
||(0.024)|(0.012)|(0.022)|(0.024)|(0.013)|(0.023)|
|Model 3|0.732***|0.786***|-0.054**|0.732***|0.793***|-0.061***|
||(0.024)|(0.012)|(0.021)|(0.024)|(0.013)|(0.022)|

Note: Y1 is the observed schooling rate of children whose parents have disabilities. Y0 is the counterfactual schooling rate that children would have if their parent not been disabled. ATT=Y1–Y0 is the effect of parental disability on children’s schooling rate.

* significant at 10%; ** significant at 5%; *** significant at 1%

Figures in parentheses are standard errors. Standard errors are corrected for sampling weights and estimated using bootstrap (non-parametric) with 500 replications. Source: Estimation from the 2006 VHLSS.

We estimated a probit with instrumental variables regression including interactions between parental disability and different characteristics of children including gender, urbanity, ethnicity and age. The instrumental variables for the interactions are the interactions between these interacted characteristics and the instruments for parental 13
17.7 billion.<sup>3</sup> Within this agenda, the report asserts the need for six additional surveys, at the scale of a Demographic and Health Survey or a Multiple Indicator Cluster Survey, per IDA-eligible country from 2016 to 2030 in support of SDG monitoring. Each survey is priced at USD 1.3 million, and the total budget is estimated at USD 600 million for 77 IDA-eligible countries.<sup>4</sup> While these estimates have led to public debate on the role and cost of data for development, there is widespread recognition that the countries and the international development partners, including international organizations, civil society, and academia, must be committed to an agenda to strengthen country statistical capacity and to produce the required data based on international best practices, including emerging, validated, innovative and cost-effective data collection solutions.

Linked closely to the 2030 Agenda for Sustainable Development, in 2013, the World Bank Group (WBG) adopted the _Twin Goals_ of ending global extreme poverty by 2030 and promoting shared

> 2 United Nations Economic and Social Council (2016) provides the final list of SDG indicators in the Annex IV of their report.

> 3 The GPSDD (2016) estimate is an update of the original United Nations Sustainable Development Solutions Network (2015) estimate of USD 14.2 billion.

> 4 It is not clear whether the cost of USD 600 million captures both survey implementation and technical assistance expenditures.

2
24 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

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