rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

With Sub-Saharan Africa’s working-age population expected to reach 1.3 billion by 2050, making up 25% of the global labor force (World Bank, 2023), and 20%-30% of youth currently not in employment, education, or training (UNICEF, 2022), there is an urgent need to equip young people with the skills required to secure meaningful employment. Although there is significant heterogeneity across programs, the average effects of technical and vocational education and training (TVET) programs on youth employment have been positive in both higher and lower income country settings (Hanushek et al., 2017; Tripney and Hombrados, 2013). However, low take-up rates significantly challenge the effectiveness of training programs in such settings (McKenzie and Woodruff, 2014). Improving uptake of TVET programs in Africa is thus a potentially important step in introducing skilled individuals into the labor market and reducing youth unemployment.

The range of TVET offerings in Africa is extensive, with participant costs varying significantly (Van Lieshout and Mehtha, 2017). Removing cost barriers has been shown to increase product usage in low- and middle-income countries (Cohen and Dupas, 2010; Kremer and Miguel, 2007) as well as participation in basic education (Duflo et al., 2023). Analogously, reducing costs could boost enrollment in training programs. One tool that has proven effective at shifting behaviors at low cost in some contexts is sending informational text messages (Gr ́acio and Vicente 2021; Rodr ́ıguez and Saavedra 2019), making use of growing mobile phone penetration in low and middle income countries.

This study uses a randomized controlled trial to test the impact of sending text messages (i.e., short message service, or SMS messages) to youth and—in some cases—a trusted contact the youth have identified in order to increase uptake of a youth employment program in Cˆote d’Ivoire. We find that SMS messages that make salient the fact that the training program is free can discourage enrollment. Specifically, we show that the negative impact on enrollment is significant only when both the potential applicant and their contact are sent SMS messages. This finding is consistent with the theoretical literature that suggests 1
13 26%), they do point out to several variables of importance.

The analysis shows that there is considerable variability in the efficiency of the use of classrooms in terms of classes across Bulgarian municipalities. Regional differences are also nonmarginal with the striking result that the efficiency, as defined, is highest in the capital - five municipalities of the City Sofia. Some of the variation is related to demand factors. In rural areas with lower population density there are simply less students than in urban areas, thus making the dass sizes and the use of classrooms less intensive than in urban areas. There seems to be little that policy can do to alter this overall spatial pattern of demand for classes.

Yet, there maybe operational policies at the municipal level that can increase the efficiency in the use of classrooms, without jeopardizing learning objective. For example, in rural areas, the class size is often much smaller, there are fewer shifts, and classrooms are not used as much as in the cities. Consolidating several grades into multi-grade classes in such rural areas could be used to reduce the number of teaching (and non-teaching) staff in those areas, while maintaining the quality of learning, and increasing the use of fixed inputs such as classrooms. To the extent that is possible, there could be savings to tap in those municipalities which need them most: demographically sparse, poor municipalities with weak economic and human resource base. Those savings could then be reallocated to other essential services wiihin the general education, such as teaching equipment and materials, which seem to be notoriously lacking in Balgaria<sup>6</sup> .

Further empirical study of efficiency in general education in Bulgaria and other Central and Eastern European countries is warranted. As these countries move towards a market economy, with the inevitable hardening of budget constraints at all levels, access to essential services such as primary education should be mnaintained, and quality should not be allowed to deteriorate. Until these countries move onto a sustainable growth path, it is likely that large social sectors, such as education, will be under severe pressure to use its resources more efficiently. To that end, it is necessary to

**_61_** World Bank (1994b).
- Newell, Richard G., Brian C. Prest, and Steven E. Sexton (2021). “The GDP-temperature relationship: Implications for climate change damages”. _Journal of Environmental Economics and Management_ 108.

- Pankratz, Nora, Rob Bauer, and Jeroen Derwall (2023). “Climate Change, Firm Performance, and Investor Surprises”. _Management Science_ 69, 7352–7398.

- Pankratz, Nora M. C. and Christoph M. Schiller (2022). “Climate Change and Adaptation in Global Supply-Chain Networks”.

- Park, R. Jisung, Nora Pankratz, and A. Patrick Behrer (2024). _Temperature, Workplace Safety, and Labor Market Inequality_ .

- Ponticelli, Jacopo, Qiping Xu, and Stefan Zeume (2023). _Temperature, Adaptation, and Local Industry Concentration_ .

- Rode, Ashwin et al. (2022). _Labor Disutility in a Warmer World: The Impact of Climate Change on the Global Workforce_ .

- Sautner, Zacharias et al. (2023). “Firm-Level Climate Change Exposure”. _The Journal of Finance_ 78.

- Solon, Gary, Steven J. Haider, and Jeffrey M. Wooldridge (2015). “What Are We Weighting For?” _The Journal of Human Resources_ 50, 301–316.

- Somanathan, E. et al. (2021). “The Impact of Temperature on Productivity and Labor Supply: Evidence from Indian Manufacturing”. _Journal of Political Economy_ 129, 1797–1827.

- Stern, Nicholas (2008). “The Economics of Climate Change”. _American Economic Review_ 98, 1–37.

- Tol, Richard S.J. (2021). “The distributional impact of climate change”. _Annals of the New York Academy of Sciences_ 1504, 179–192.

- Venturini, Alessio (2022). “Climate change, risk factors and stock returns: A review of the literature”. _International Review of Financial Analysis_ 79.

- Weinhofer, Georg and Timo Busch (2013). “Corporate Strategies for Managing Climate Risks”. _Business Strategy and the Environment_ 22, 121–144.

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# **Figure B21: Soybean oil**

<!-- Start of picture text --> Log scale<br>9<br>Log price Permanent component<br>8<br>7<br>6<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.0<br>Medium-term cycle Transitory component<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 62 —_
because respondents tend to include events occurring before the reporting period (Akerlof and Yellen, 1985) leading to an overstatement of the number of events reported for recent periods and an understatement for more distant periods.

Another strand of the literature examines the impact of survey mode in social surveys. Phone interviews may suffer higher rates of nonresponse (De Leeuw 1992;=), and can (but certainly do not always) also yield slightly different responses than in-person interviews (Groves 1990). These differences are often ascribed either to social desirability bias or satisficing. Starting with the former, greater “social distance” due to the physical absence of interviewers may reduce the proclivity of respondents to offer socially desirable responses in phone interviews. But the larger “social distance” could also have an opposite effect by making respondents less willing to confess to socially undesirable and stigmatized behavior (Gregson et al. 2002; Langhaug, Sherr and Cowan 2010). To the extent working is socially desirable but not stigmatized, one might anticipate phone interviews to be associated with lower estimates of days and hours worked. A second potential reason for different responses is survey satisficing; telephone interviewing may increase the likelihood of respondents limiting the amount of cognitive effort they devote to reporting accurately, either because phone interviews tend to move more quickly, because of the threat of interruptions, or the possibility of multitasking during the phone interview. Such survey satisficing could result in both more recall bias and a lower propensity to report work disruptions.

# **3. Experiment Design**

The experiment was designed to examine the impact of the length of the reference period and survey modality on labor market reporting. It included three treatments (table 1). Participants in all three groups were interviewed in-person at baseline and endline and by phone in a three-month follow-up. The treatment groups comprised (a) one group that was interviewed by phone three times a week for 10 weeks (30 interviews), (b) another that was interviewed by phone once a week for 10 weeks (10 interviews), and (c) a third that was interviewed in person once a week for 10 weeks (10 interviews). The questionnaire was identical for both phone and in-person interviews, and the two types of interviews were conducted at the same times. All participants interviewed by phone received cellphones to avoid selection bias associated with phone ownership.

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about 3 months of cognitive development, for example—stunting seems to be a weaker mediator of caregiver education than parenting behaviors are.<sup>10</sup> We explore additional associations and mediating factors in Table 5. First, we include baseline test scores collected 11 months prior to the main outcomes of this study (which would also have the effect of controlling for unobserved factors already captured in those scores). While the magnitudes fall, the relationships with parenting typically remain significant, suggesting that the effect of parenting is cumulative (and not “one-shot”).<sup>11</sup> We also hypothesized that parenting may have differential effects on child development, depending on children’s nutrition status. To test the hypothesis that the association of cognitive parenting and child outcomes is weaker for stunted children, we looked in column 3 of Table 5 at the effect of parenting, separately for stunted and non-stunted children and with baseline controls. We focused on cognitive parenting because of the notable role of nutrition status in cognitive development; we thus hypothesized that cognitive parenting and subsequent child development would be especially sensitive to nutrition status. While no significant interactions between parenting and child nutrition status were observed for executive function and socioemotional problems, effects of cognitive parenting differed by stunting for language and early numeracy outcomes. The magnitude of the cognitive parenting effect is about 35% weaker for effects on language and 54% weaker for effects on early numeracy, showing that the positive effect of cognitive parenting is weaker for stunted children. We repeated the same exercise

> 10 The fact that wealth gradients remain large even after controlling for anthropometrics and parenting should be interpreted with care. As our measure of parenting is only self-reported and based on recall, it is possible that it does not capture the full dynamic of parenting throughout the children’s life time. Our measure of parenting could for instance be imperfectly correlated with past parenting (during pregnancy or breastfeeding). Our measure of parenting may also imperfectly measure the quality of parenting. Longitudinal and external measures of parenting would be necessary to enhance our measures.

> 11 Note that in Table 5 we control for both parenting and stunting. The effects of each are relatively unchanged.

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mentation increases, the use of the coping strategies (both in terms of their frequency and severity) falls.<sup>16</sup> To illustrate using results from Table 4b column (2), moving from the 25th to 75th percentile of land fragmentation decreases CSI by -2.22, the equivalent of going hungry so one’s children can eat for a day. This negative correlation retains its significance across the various measures of fragmentation, suggesting it is a combination of the number of parcels, deviation in parcel size and distance traveled that is driving the narrative.

# _Instrumental Variable Estimation_

Our core results are premised on the assumption that given the history of land reforms in Ethiopia, together with norms regarding inheritance, land fragmentation is uncorrelated with components of the disturbance terms – such as unobserved farmer ability – that might have a direct effect on food security. We also noted that most, but not all, land obtained by our sample came from either government officials or through inheritance. But because some holdings were acquired in other ways, there may be a lingering concern that our measures of fragmentation are correlated with the disturbance term. In tables 5 and 6 we therefore use the number of parcels inherited or received from the government as an instrumental variable for land fragmentation, similar to the identification strategy used by Veljanoska (2016) and Ali, Deininger, and Ronchi (2018).

The exclusion restriction is satisfied under the assumption that land redistribution was orthogonal to farmer ability and that this arbitrary allocation was perpetuated by legal and cultural constraints. The first stage regression in tables 5a and 6a confirms the instrument’s relevance. The second stage regressions in tables 5b and 6b find results similar to Table 4 in sign and significance, allaying our concerns of bias. In columns (1) and (2) these coefficients are of 16. i.e. eating less preferred foods is less severe (Weight=1) than going a whole day and night without eating (Weight=4).

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# **Table A.9. Predicted Poverty Rates Based on Imputation, from NPS 2019/20 to TA 2 using 2019/20 Poverty Line, Tanzania (percentage)**

|<br>**Method**|||**T**<br>|**A 2**<br>|||
|---|---|---|---|---|---|---|
||**Model 1**|**Model 2**|**Model 3.1**|**Model 3.2**|**Model 8**|**Model 9**|
|1) Normal linear regression model|**16.4**<sup>**a**</sup><br>(2.4)|**16.5**<sup>**a**</sup><br>(2.5)|25.9<br>(2.5)|**18.3**<sup>**a**</sup><br>(2.1)|**16.9**<sup>**a**</sup><br>(2.5)|**18.3**<sup>**a**</sup><br>(2.6)|
|2 Eiil ditibti f th t|**16.6**<sup>**a**</sup>|**16.2**<sup>**a**</sup>|26.3|**18.3**<sup>**a**</sup>|**16.7**<sup>**a**</sup>|**18.5**<sup>**a**</sup>|
|) mprca sruon o e error erms|(2.4)|(2.5)|(2.5)|(2.0)|(2.5)|(2.6)|
|_Control variables_|||||||
|Food expenditures|||Y|Y|||
|Utilities: water, kerosene, lighting|||||Y|Y|
|Household assets & house characteristics||Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|
|R<sup>2</sup>|0.39|0.54|0.90|0.90|0.54|0.43|
|N1(base survey)|1,179|1,179|1,179|1,179|1,179|1,179|
|N2(target survey)|701|701|701|701|701|701|
|**True poverty rate in TA 1**<br> <br>**Note**: The standard errors in parentheses are adjusted for c<br>linear regression model with the theoretical distribution o<br>cluster random effects. Model 3.1 employs non-standar<br>expenditures, household size and age in both surveys are|omplex survey design<br>f the error terms and<br>dized variables. Mode<br>transformed to norma|. All estimates ar<br>Method 2 uses th<br>l 3.2 employs<br>lity using the Bo|1<br>(1<br>e obtained with<br>e empirical dist<br>variables in TA<br>x-Cox method b|7.7<br>.9)<br>the population we<br>ribution of the err<br>2 standardized b<br>efore standardiza|ights. Method<br>or terms. Both<br>y those in NP<br>tion. The impu|1 uses the normal<br>methods employ<br>S 2019/20. Food<br>ted poverty rates|

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Model 3.1 employs non-standardized variables. Model 3.2 employs variables in TA 2 standardized by those in NPS 2019/20. Food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. The imputed poverty rates for TA 2 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2019/20 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.5.

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**Table 7: FPM transfers and preschool supply**

|||Reduce|d form|||IV<br>|||Obs.|
|---|---|---|---|---|---|---|---|---|---|
||Enroll|ment|#cen|ters|Enroll|ment|#ce|nters||
|Panel A: Municipal supply||||||||||
|Thresholds 1-7|3.750**|5.230***|0.232***|0.200***|4.666**|6.765***|0.289***|0.258***|21168|
||[1.551]|[1.424]|[0.082]|[0.061]|[1.926]|[1.684]|[0.101]|[0.072]||
|Thresholds 1-4|4.016***<br>[1.415]|5.019***<br>[1.221]|0.187**<br>[0.084]|0.192***<br>[0.060]|5.333***<br>[1.879]|7.064***<br>[1.571]|0.249**<br>[0.111]|0.270***<br>[0.078]|18365|
|Panel B: Private supply||||||||||
|Thresholds 1-7|0.831|1.467***|0.000|0.000|1.035|1.898***|0.000|0.001|21168|
||[0.702]|[0.386]|[0.011]|[0.007]|[0.874]|[0.458]|[0.014]|[0.008]||
|Thresholds 1-4|1.045*|1.118***|-0.002|0.071*|1.387*|1.573***|-0.003|0.008|18365|
||[0.559]|[0.375]|[0.011]|[0.041]|[0.743]|[0.484]|[0.015]|[0.008]||
|Municipality fixed-effects|No|Yes|No|Yes|No|Yes|No|Yes||
|State fixed-effects|Yes|No|Yes|No|Yes|No|Yes|No||

_Notes_ : Reduced form regressions relate the relevant outcome to theoretical transfers. In the IV regressions, theoretical transfers serve as instrument for actual transfers. All regressions include a three order population polynomial and year fixed effects. Robust standard errors clustered at the municipal level in brackets. *, **, *** represent significance at the 10%, 5% and 1% level, respectively.
**<u>Table 3. Predicted Poverty Rates Based on Imputation, from NPS 2020/21 to TA 2 using 2020/21 Poverty Line, Tanzania (percentage)</u>**

|**Method**||||**T**|**A 2**||
|---|---|---|---|---|---|---|
||**Model 1**|**Model 2**|**Model 3.1**|**Model 3.2**|**Model 8**|**Model 9**|
|1) Nrml linr rrin mdl|**21.9**<sup>**a**</sup>|**21.9**<sup>**a**</sup>|28.4|**20.6**<sup>**a**</sup>|**22.5**<sup>**a**</sup>|**23.4**|
|oa ea egesso oe|(2.7)|(2.8)|(2.7)|(2.3)|(2.9)|(2.9)|
|2) Emirical distribution of the error terms|**21.7**<sup>**a**</sup>|**21.9**<sup>**a**</sup>|28.7|**20.2**<sup>**a**</sup>|**22.4**<sup>**a**</sup>|**23.3**|
|p|(2.7)|(2.8)|(2.8)|(2.3)|(2.9)|(2.9)|
|_Control variables_|||||||
|Food expenditures|||Y|Y|||
|Utilities: water, kerosene, lighting|||||Y|Y|
|Household assets & house characteristics||Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|
|R<sup>2</sup>|0.40|0.54|0.85|0.86|0.55|0.44|
|N1(base survey)|4,644|4,644|4,644|4,644|4,644|4,644|
|N2(target survey)|701|701|701|701|701|701|
|**True poverty rate in TA 1**<br>|||21<br>(2|.0<br>.2)|||

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Model 3.1 employs non-standardized variables. Model 3.2 employs variables in TA 2 standardized by those in NPS 2020/21. Food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. Imputed poverty rates for TA 2 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2020/21 deflated to 2022 prices using the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.6.

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enumerators nor the respondents see the structure of the sub‐modules, but the assigned items are asked in a categorically meaningful way (like cereals, fruits, etc).<sup>5</sup> The data is used to construct four outcome measures. The surveys collect information on quantities in terms of (i) number of consumption items and (ii) kilograms. The quantities can be used to construct measures of (iii) monetary and (iv) caloric food consumption scaling the quantities with data on average prices and energy levels.<sup>6</sup> Though we are mainly interested in evaluating the impact of the nudges on the total consumption value ‐ both in terms of money and food intake ‐ these variables are difficult for respondents to falsify because these are _second‐order_ values that are calculated as a function of other variables, including consumption quantities and calories or prices that are in turn deflated. All of this adds noise to the answer provided by the respondent, and they depend in part on variables over which the respondent has no control. The consumption quantity in kilograms is a more direct measure of the quantity consumed as expressed by the respondent, and may lead to more accurate estimation of the impact of the nudges. Finally, counting the number of items may lead to an even more accurate measure, since the variable does not undergo any cleaning at all and is taken at face value. Furthermore, omitting an item is likely to be the easiest and quickest way for respondents to reduce the true value of the household’s consumption.<sup>7</sup> Poverty amongst IDP households is high, and 9 in 10 IDP households across South Sudan live under $1.90 USD PPP (2011) per capita per day in 2017. IDP households in the sample interviewed for the experiment consume on average 333 SSP (2017) per capita per day. IDP households reported on average 6.63 core consumption items. These figures represent about 20 percent of core items asked to the households. Figure 2 visualizes that 39 percent of households report consumption below the recommended daily intake of 2,100 kcal (R) and 16 percent below the subsistence level of 1,200 kcal (S) (Ravallion & Bidani, 1994).

> 5 Due to the survey method applied CRS surveys contain the core consumption module and one additional consumption module. The share in imputed consumption is on average 99.9 percent. IDP surveys contain due to the previously outlined time constraints only core consumption items. However, by design these items capture the lion’s share of consumption (on average approx. 94 percent of total consumption in more comprehensive CRS surveys).

> 6 For a description of the caloric intake measure, please consult the appendix.

> 7 Note that the number of consumption items is not reported per capita as it does not increase proportionally with household size.

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