rafmacalaba/datause-encoder-data huggingface dataset:
The bottom half of Table 1 displays characteristics and learning statistics of the target child at baseline. Fifty-one percent of target children are female and the average age is 7 years old. Only 17% of target children were not enrolled in school, which is substantially below the average of northern Nigeria where around half of children are not enrolled in primary school (Nigerian DHS 2019). Similar to previous studies implemented in northern Nigeria (Reading and Access Research Activity report 2019), literacy and numeracy levels in our study sample were extremely low. Using modules from the Early Grade Reading Assessment (EGRA), the average number of correct answers in letter recognition was 0.35 letters out of 100. For this first assessment, if the child was not able to give at least one correct answer for the first 10 letters, the exercise was discontinued and the assessment instrument moved on to oral 1.2% of children passage reading. Only target passed from letter recognition to non-word reading. In listening comprehension, where children were asked questions about a passage that was read by an enumerator, children were better equipped, with the average number of correct answers reaching almost 3 out of 5 questions. For letter dictation, on average, approximately one out of five letters was correct.

Considering the overall low literacy rates in northern Nigeria, we also collected Uwezo literacy assessments as their implementation by non-pedagogical experts is simpler than EGRA. This is a household-based initiative that aims to improve literacy and numeracy skills among children in Kenya, Tanzania and Uganda. A local translator used Uwezo assessments and translated into Hausa.<sup>12</sup> The main difference between EGRA and Uwezo is that the number of letters and words tested in Uwezo is fewer, which might encourage the child to provide more responses; and unlike EGRA, the Uwezo assessment is not timed. In our sample, children performed better for the competence of letter recognition using Uwezo assessments, however, the patterns across literacy levels found in EGRA and Uwezo are very similar. Thus, this paper reports EGRA scores to be comparable with other education technology (edtech) studies.

Because of the brain activity that the m-Learning add-on may encourage while playing and learning, we also assess potential spillovers to numeracy outcomes for the target child and sibling (the printed materials provided at the community screenings and the m-Learning apps only included literacy lessons), we used modules from the Early Grade Mathematics Assessment (EGMA) to test both children. Overall, while we observe children doing better at recognizing numbers than letters, the baseline data shows that numeracy skills are very low. On average, 0.43 numbers are recognized out of 20 and 1.6

> 12More details about Uwezo assessments can be found at `http://nada.uis.unesco.org/nada/en/ index.php/catalogue/179` .

18
job, let aside do-it-yourself activities, and the share of informal (at the main job) workers in the shadow output varies considerably across countries and time.

Therefore, negative association between the size of the shadow economy on one hand and GDP per capita and/or GDP growth on the other found in Schneider et al. (2010: Tables 3.1 and A.4.1) for all country groups (developing, transition, high-income/OECD, and total) does not, in principle, contradict to our results<sup>33</sup> . Nor do our results contradict to the ―substitution effect‖ (positive association between short-term fluctuations in unemployment and short-term fluctuations in shadow economy) found by Bajada and Schneider (2009) for OECD countries. Rather, these results together suggest that non-contracted wage employment at the main job on one hand and undeclared earnings in general on the other follow different cyclicality patterns.

For Europe at large, as well as within Eastern and Southern Europe, both _income inequality_ and proportion of _first and second generation immigrants_ in population positively affect the share of informal employees (in Europe at large – also the share of informal selfemployed) in the labor force. In Eastern and Southern Europe, a similar effect is found for the share of local born minority population. This is consistent with theoretical expectations (see Table 2) and available empirical evidence (on inequality: Chong and Gradstein, 2007; Perry et al., 2007; on immigrants: Ambrosini, 2001; Flaquer and Escobedo, 2009; Kahanec and Zaiceva, 2009; Kahanec et al., 2010; Diaz-Serrano, 2010; Hazans, 2011a, 2011b; Say, 2011).

Bivariate correlation between EPL strictness and prevalence of informal dependent employment is positive in Eastern and Southern Europe but negative among Western European and Nordic countries (Figure 1); yet in both country groups we find a positive impact of EPL on the share of non-contracted employees in the labor force when other factors are controlled for; this is consistent with the fact that EPL increases labor cost in the formal sector. However, in Europe at large, EPL strictness reduces informal dependent employment, other things equal. One explanation is that stricter EPL enhances attractiveness of formal jobs, and (in the pooled sample) this outweighs the effect EPL has on cost of formal labor.

Arguably, the most important distinctions between the two parts of Europe are related to the effect of the minimum wage on the share of informal dependent employment, as well as the effect of union density on the prevalence of both types of informal employment. Plausibly, this has to do with very different absolute levels of both minimum wage (e.g. in 2007, nonweighted average was €1306 for the West vs. €322 for Eastern and Southern Europe) and union density (44% vs. 19% in our samples).

> 33 Schneider et al. (2010) use GDP and growth as determinants of the size of the shadow economy in some of their MIMIC models and as indicators in others, noting that ―there is some arbitrariness whether to use a certain variable as causal or indicator‖.

31
|**31**|U.S.|USA ParentCorp Project|(Brotman et al. 2016)|Teacher PD/<br>Curriculum/<br>Parental engagement|Education|Journal article|RCT + PSM|
|---|---|---|---|---|---|---|---|
|**32**|U.S.|USA Pre-K Programs (State<br>level)|(Wong et al. 2008)|<br>Subsidized or free access|Economics|Journal article|RDD|
|**33**|U.S.|USA Tennessee Pre-K|(Lipsey et al. 2018)|Subsidized or free access|Education|Journal article|RCT +<br>HLM|
|**34**|U.S.|USA Tools of the Mind<br>Curriculum|(Barnett et al. 2008)|Teacher PD/<br>Curriculum/<br>Provision of materials|Education|Journal article|RCT|
|**35**|U.S.|USA Tools of the Mind<br>Curriculum|(Blair and Raver 2014)|Teacher PD/<br>Curriculum|Psychology|Journal article|RCT|
|**36**|U.S.|USA Tools of the Mind<br>Curriculum|(Diamond et al 2007)|Teacher PD/<br>Curriculum|Economics|Journal article|RCT|
||**_LMIC - Low-_**|**_& Middle-Income Countries_**||||||
|**37**|Argentina|Argentina Preschool<br>Construction|(Berlinski et al. 2009)|Preschool construction/<br>Curriculum|Economics|Journal article|DID|
|**38**|Bangladesh|Bangladesh Early Years<br>Preschool Program|(Spier at al 2020)|Subsidized or free access|Education|Technical Report|RCT + IV|
|**39**|Cambodia|Cambodia Preschool<br>Construction II|(Berkes et al 2019)|Preschool construction/<br>Community outreach/<br>Parental engagement|Economics|Working Paper|RCT|
|**40**|Cambodia|Cambodia Preschool<br>Construction I|(Bouguen et al. 2018)|Preschool construction/<br>Teacher PD/<br>Provision of materials|Economics|Journal article|RCT|
|**41**|Colombia|Colombia Preschools - HIM<br>& FE Programs|(Andrew et al. 2019)|Teacher PD/<br>Provision of materials/<br>Provision of staff|Economics|Working Paper|RCT|
|**42**|Gambia|Gambia Preschool<br>Construction|(Blimpo et al 2019)|Preschool construction/<br>Curriculum/<br>Teacher PD|Education|Working Paper|RCT|
|**43**|Ghana|Quality Preschool for Ghana<br>(QP4G)|(Wolf 2019)|Teacher PD/<br>Parental engagement|Education|Journal article|RCT +<br>HLM|
|**44**|Ghana|Quality Preschool for Ghana<br>(QP4G)|(Wolf and Peel 2019)|Teacher PD/<br>Parental engagement|Education|Journal article|RCT +<br>HLM|
|**45**|Ghana|Quality Preschool for Ghana<br>(QP4G)|(Wolf et al. 2018)|Teacher PD/<br>Parental engagement|Education|Journal article|RCT +<br>HLM|
|**46**|Ghana|Quality Preschool for Ghana<br>(QP4G)|(Wolf et al. 2019)|Teacher PD/<br>Parental engagement|Education|Journal article|RCT +<br>HLM|
working hours are impacted by adult deaths; the underlying premise is that prime-age deaths weaken support to older adults, both financial (remittances in cash and in-kind) and through labor sharing in farming and household activities. This, in turn, may result in older adults having to work more to sustain their household. We look for evidence that the impact on work varies by length of time from the death shock and by the asset position of the household prior to the shock. We extend our outcomes to also include more direct measures of well-being, including health and consumption.

Earlier work by Ainsworth and Dayton (2003) and Dayton and Ainsworth (2004) examines the short-run impacts of adult death on the elderly using the first four years of the same panel dataset we use. The authors find that the body mass index (BMI) of older individuals drops just before an adult death but then recovers to the previous level afterwards. We extend the work of these studies by focusing on long-term impacts (up to 13 years after an adult death), and by analyzing changes in labor supply of the elderly in addition to health outcomes. We also construct indicators for prime-age adult mortality which reflect the complexities of household composition. These include measures of coresidency over time and deaths of biological children who may never had resided with the parent at baseline.

We find that prime-age deaths occurring while the deceased adult was living with the elderly respondent are associated with increases in working hours of older women. For older men, labor increases are associated when the deceased was not a household member at the time of the death, but with less precision. The impacts are more pronounced for when the deceased is a younger prime-age (15-29 years) co-residents. For non-co-residents, the labor increase is associated with older prime-age (30-49 years) coresidents. The impacts are mainly for deaths in the distant past, suggesting that shorterrun studies may not capture the full extent of the consequences of adult mortality for survivors. Older adults with greater assets are able to buffer the labor supply response for deaths of co-resident members. Most health indicators were not worse for older adults who experience a death shock in the household (similar to the findings of Ainsworth and Dayton, 2003, and Dayton and Ainsworth, 2004), although more distant deaths were associated with an increased probability of acute illness of surviving adults. We find no 5
**Table 1: Data description**

|||**Utility s**<br>|**ample**<br>||**Tran**<br>|**sport sample**<br>|<br>|
|---|---|---|---|---|---|---|---|
|**Year**|**Electricity**|**Gas**|**Water**|**Total**|**Infrastructure**|**Services**|**Total**|
|1991|16|2|0|18|3|3|6|
|1992|19|3|0|22|5|4|9|
|1993|25|4|1|30|5|6|11|
|1994|28|6|2|36|5|8|13|
|1995|30|8|3|41|7|9|16|
|1996|46|12|4|62|9|10|19|
|1997|50|15|7|72|11|14|25|
|1998|51|16|7|74|10|14|24|
|1999|73|21|8|102|18|19|37|
|2000|80|21|7|108|20|22|42|
|2001|84|20|8|112|22|23|45|
|2002|82|20|8|110|19|23|42|

Source: Datastream.

As was to be expected in the case of developing countries, the quality of the data is an issue. All necessary data were not available for all companies for all years, and there may therefore be gaps of one or more years in the time series of leverage ratios. This makes the aggregate sample somewhat unbalanced. As a result, although the number of companies in the sample increases steadily over time, there can sometimes be substantial changes in the identity of companies in the sample in each year. Although this does not affect the longerterm trend, it might increase the volatility of average sectoral levels of leverage from year to year.

When analyzing the impact of the 1997 Asian crisis, this volatility needs to be controlled, and therefore a balanced sample was constructed. This balanced sample covers the period from 1997 to 2002, and only considers companies that have a full time series of data for those years. For utilities, 54 companies fulfill this criterion, including 37 electricity companies, 14 gas distribution companies and 3 water companies. For the transport sector, the balanced sample contains 20 companies: 8 transport infrastructure and 12 transport services providers.

The countries covered in the sample include Argentina, Brazil, Chile, Mexico, Peru and Venezuela for Latin America; China, India, Indonesia, Malaysia, Pakistan, the Philippines, South Korea and Thailand for Asia; and a residual group of important countries in the international investment world for infrastructure, including the Czech Republic, Russia, South Africa and Turkey. All data were collected in domestic currency.

Table 2 describes the data types collected for these countries, and their definitions for the purposes of the calculations of the leverage ratios. While leverage can simply be defined as the ratio of the value of a firm’s debt to the combined value of the firm’s debt and equity, practical estimations can be more complicated, as several definitions of debt and equity can be used.

5
# **_Table 3_** _: Correlations between the ECT and socioeconomic outcomes_

||Worked<br>last week|Hours<br>worked<br>last week|Main<br>source of<br>income<br>increased<br>or stayed<br>same|Main<br>source of<br>income<br>decreased<br>or stopped|Other<br>source of<br>income<br>increased<br>or stayed<br>same|Other<br>source of<br>income<br>decreased<br>or stopped|Inability<br>to buy<br>staple<br>foods|Able to<br>financially<br>support<br>child's<br>return to<br>school|Child<br>identified<br>as mal<br>nourished|Small children<br>given vitamin A<br>supplementation|
|---|---|---|---|---|---|---|---|---|---|---|
|||||||**1. ECT-R1: R**<br>|**ound 1(sho**<br>|<br>**rt-term) outc**<br>|**omes**||
|ECT-R1|0.050|5.72*|0.135**|-0.135**|0.192***|-0.396***|0.000|0.025|||
||(0.058)|(2.947)|(0.060)|(0.060)|(0.066)|(0.061)|(0.050)|(0.053)|||
|Observations|569|569|562|562|392|392|533|512|||
|||||**2.**<br>|**ECT-R1: Roun**<br>|**d 2(medium-**<br>|**term) outco**<br>|**mes(ECT-R2**|**beneficiaries e**<br>|**xcluded)**|
|ECT-R1|0.126*|8.576**|0.025|-0.025|-0.164**|0.064|0.030|-|0.024|-|
||(0.065)|(3.690)|(0.056)|(0.056)|(0.077)|(0.071)|(0.039)||0.071||
|Observations|702|702|682|682|503|503|640||208||
|||||**3.**|**ECT-R2: Roun**|**d 2(medium-**|**term) outco**|**mes(ECT-R1**|**beneficiaries e**|**xcluded)**|
|ECT-R2|0.180***|15.340***|-0.145|0.145|-0.265***|0.290***|-0.068***|-|-0.059*|-0.063|
||(0.060)|(4.461)|(0.094)|(0.094)|(0.077)|(0.092)|(0.021)||(0.034)|0.066|
|Observations|459|459|442|442|328|328|433||187|305|
|||||||**4. Pooled o**|**utcomes of E**|**CT-R1 and EC**|**T-R2**||
|ECT-R1 and<br>ECT-R2|0.121**|10.448***|0.173***|-0.173***|0.156**|-0.294***|-0.031||||
||(0.056)|(3.270)|(0.062)|(0.062)|(0.061)|(0.070)|(0.034)||||
|Observations<br>_Source_: Authors’|908<br>calculations|908<br>from CIMS (20|893<br>20)|893|627|627|810||||

_<mark>Note</mark>_ <mark>: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. Inverse probability weighted regressions. For each estimation, overidentification tests shows that the IPW model balances the covariates. Full set of results available in Online supplementary materials S2 to S6.</mark> 22
19 DEINDU (change 1980-2000), their interactions with the 8 city size categories, _p_ , and the controls _X_ of model (10) (except urbanization, which was used as a control in the first-step residualization procedure). We use the FUAs’ populations in 2000 as weights: PCC _a,c_ = _α_ + _β_ MFGSERV _c_ + _γ_ NRXGDP _c_ + _δ_ DEINDU _c_ +Σ<sup>9</sup> _p_ =2<sup>_ζp_MFGSERV</sup><sup>_c∗_1(CAT</sup><sup>_a_=</sup><sup>_p_)+</sup> Σ<sup>9</sup> _p_ =2<sup>_θp_NRXGDP</sup><sup>_c∗_1(CAT</sup><sup>_a_=</sup><sup>_p_) + Σ9</sup> _p_ =2<sup>_λp_DEINDU</sup><sup>_c∗_1(CAT</sup><sup>_a_=</sup><sup>_p_) +</sup><sup>_Xcκ_+</sup><sup>_μa_</sup> (11) We consider two PCC measures capturing urban tradables – MFG+FIRE – and urban non-tradables – NTR2, which includes _domestic wholesale & retail trade_ and _other services_ . We plot the obtained correlations for each PCC measure-size category in Figures 4(a)4(b). Figure 4(a) shows that MFGSERV countries have higher shares of MFG+FIRE employment in their cities than NRXGDP and DEINDU countries for _all_ population size categories of cities. The patterns for the employment share of non-tradables, NTR2, are different. MFGSERV countries have the lowest shares of NTR2 across all employment city sizes (Figure 4(b)). By contrast, DEINDUSTR countries have the highest employment shares of NTR2 across _all_ city sizes. In NRXGDP countries, the employment share of . NTR2 increases with city size and is largest in the case of the _largest cities_ This analysis suggests that the whole urban system in the deindustrializing countries has experienced a shift from jobs in manufacturing to jobs in nontradable services, while in resource-rich countries only larger cities have substantially higher shares of nontradable services. In both cases, larger cities have much higher shares of urban nontradables than smaller cities, but for all city sizes these shares are much larger in the deindustrializing countries than in the resource-rich countries.

**Robustness: Panel.** We obtain the employment shares for 184 country-years and 62 countries from IPUMS and run panel regressions with country and year fixed effects (FEs). We regress the selected sectoral labor share in urban areas in time period _t_ on MFGSERV in _t_ , NRXGDP in _t_ -10, DEINDU between 1980 and _t_ (= 0 before 1980), the urban share in _t_ , and log population in _t_ and its square. We include countries with at least three years of data and cluster the standard errors at the country level. Since the sample size reduces to 124 observations, we cannot include more lags.

Most panel results are consistent with the long-difference results. We find significantly lower shares of employment in MFG and MFG+FIRE in the urban areas of DEINDU countries relative to MFGSERV countries (Web Appx. Table D.4). While these shares
only the second downbreak, whereas the restricted approach would locate the starting date of the slump at the first downturn, where the positive trend is interrupted. This is not just pure conjecture. Supplementary Appendix S3 (available at _The World Bank Economic Review_ website) discusses the results obtained from identifying economic slumps by ‘inverting’ the episodes of sustained growth from Berg et al. (2012), who apply a variant of the Bai-Perron approach, and compares them to those identified by our two-break model.

Based on this discussion, we define slumps according to three criteria. First, a slump is a _departure from a previously positive trend_ . Second, a slump must begin with _negative growth in the first year_ . Third, all slumps should be significant _regime switches_ and not just ordinary business cycle fluctuations. What precisely constitutes a significant regime switch will vary and depend on the country’s own, idiosyncratic growth process—a feature which we argue is desirable. We focus on growth in GDP per capita, since we are primarily interested in the welfare consequences of slumps and not in aggregate output _per se_ (although we also report results for aggregate output).

We capture these criteria in the following partial structural change model: where _yt_ is the log of GDP per capita at time _t_ , _β_ is a time trend, _γ_ 0 is the coefficient on an intercept break occurring together with a trend change ( _γ_ 1) after the first break at time _tb_ 1, _γ_ 2 is a second trend change occurring after the second break at time _tb_ 2, **1** ( _·_ ) is an indicator function selecting the regime, _p_ is the optimal lag order determined by the Bayesian information criterion (BIC) to parametrically adjust for the presence of serial correlation, and _{εt}_ is a martingale difference sequence.

Eq. (1) formalizes the notion that the evolution of GDP per capita around a slump is a simple function of time split into three different growth regimes: (1) a _pre-slump regime_ from the beginning of the time series of a country until time _tb_ 1, (2) a _slump-recovery regime_ 7
insurance, crop yield insurance and commodities futures and options tend to be complementary in efficient risk financing strategies.

# **7.  Conclusions**

A comprehensive review of the data that are available and credible is a fundamental step in the design and rating of any insurance scheme. Individual coverage is typically preferred by farmers. However, the data needed to derive accurate insurance rates for individual farmers are almost never available. In the event that consistent data are lacking at the individual level, two options are available. One may wish to use aggregate data in an attempt to measure individual contract parameters. An alternative approach is to consider coverage based upon observable indices that reflect as close as possible the loss incurred by the farmer. Examples include area-yield insurance and weather-based insurance.

The availability and credibility of data are also a central issue in production risk modeling. In cases where a significant amount of reliable data is available, one may choose to adopt nonparametric approaches to modeling yield risks. More common, however, is the case where data are limited. In such cases, parametric methods may be preferable. The choice essentially involves a tradeoff between bias and efficiency.

Ratemaking procedures are also based on data availability. When historical losses are available, rating procedures are based on the historical loss cost ratios. In contrast, the rating of a new insurance scheme in the absence of historical losses usually relies on simulated losses derived from crop yield models.

This paper has laid out recommendations on the design and rating of viable agricultural insurance policies. They rely on recent statistical and actuarial developments as well as empirical work based on the U.S. experience. They provide Bank staff and policymakers with technical guiding principles for the development of viable agricultural insurance programs in developing countries.

Finally, prior to any considerations regarding the contract design, one must investigate the demand for the insurance plan, i.e., willingness and ability to pay on the part of those targeted by the insurance plan. This issue has not been addressed in this paper and is left to subsequent work. An actuarially sound crop insurance scheme will fail if farmers have limited interest (e.g., because of risk management alternatives) and/or limited financial resources. This raises the tradeoff between actuarial soundness and effectiveness of the insurance plan for protecting against individual risks.

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