rafmacalaba/datause-encoder-data huggingface dataset:
### **4.2.2 Calibrating gains from specific interventions and policies to system-level gaps**

What could the macro, systemwide effects of these highly effective interventions be? To answer this, we explore first how many LAYS a given intervention could contribute toward closing the gap to achieve a full and globally benchmarked quality education at scale in a given country—assuming, as mentioned before, that the nationally scaled-up version of the program were as effective as the evaluated version. Of course, this is rarely the case, and this exercise is meant as a calibration rather than as a simulation. An alternative approach would be to apply a “discount rate” to intervention effectiveness as they go to scale. In essence, in this exercise we map micro-LAYS onto macro-LAYS. Figure 8 takes cost-effectiveness into account, making the interventions comparable by showing the gap that each could close at a cost of $100 per child. It shows that highly cost-effective programs like Tusome and ability grouping could substantially narrow the learning gap separating the children in Kenya from their peers in higher-quality systems. Moreover, policies that improve the productivity of each year of schooling, such as targeting instruction to a child’s learning level, can yield up to 4 additional LAYS per $100 in India. This calibration reveals that shifting the marginal dollar of government expenditure from low-efficiency to high-efficiency educational investments could help countries make the most out of the years of education they offer. While this cost analysis is useful for comparing interventions on a common scale, both the cost and the effectiveness of interventions can change at varying scales of implementation.

# **5 Robustness**

In this section, we present sensitivity analyses of our assumptions and parameter choices. We focus on four main areas: the high-quality learning benchmark, scaling of the learning assessments, standard deviations across tests and samples, and status-quo learning trajectories.

## **5.1 High-quality learning benchmark**

We use 0.8 _σ_ as a benchmark for high-performing learning rates. As noted above, this value is an artificial high-performance benchmark chosen because it is stable (unlike benchmarks based on actual performance of leading countries) and non-political. This approach to defining high-quality learning rates is similar to the approach to defining the high-performance benchmark learning level in the World Bank Human Capital Index (Kraay, 2019). We explore three approaches to validating this high-performance benchmark: (a) average annual learning trajectories in high-performance cases; (b) policy-relevant learning changes; and (c) rules of thumb and a range of effect sizes in reviews of multiple studies.

The first approach draws on high-performance learning trajectories. Although there is surprisingly little year-on-year raw data on learning, one notable example where there is longitudinal data is from the Young Lives survey. That survey follows students in India, Vietnam, Peru, and Ethiopia over 15 years and uses learning assessments based on Item-Response Theory (IRT).

24
were out of school. The census identified a population of 3,419 active members across the 149 clubs. The female baseline survey occurred from September to December 2016, 2–5 months before any interventions were implemented, and resulted in a final sample of 3,178 females.<sup>10</sup> The males’ baseline survey took place from December 2016 to February 2017. The sequential nature of the female and male baseline surveys allowed for the female baseline data on social networks to serve as the sampling frame for the male sample. During the baseline survey, females were asked to list males with whom they were friends, males to whom they were attracted, and males with whom they were currently or historically having sex. All of the males listed as sexual partners in _Boys_ communities were selected for survey, and, in _Supply_ and control communities, a random sample of males were selected from these lists. In total, 1,466 males were surveyed at baseline, 787 of whom were in the _Boys_ arm, 376 were in the _Supply_ arm and 303 were in the control communities. The male sample was intentionally split so that half of the male sample would be in the _Boys_ arm and the other half of the sample would be equally split between the _Supply_ and control communities. Figure 1 presents the sample distribution across study arms.

Prior to endline data collection, another census of ELA members was conducted during May 2018. Endline data collection took place between June and August 2018 for both males and females, six months after the end of all interventions. Figure 3 shows the timing of data collection relative to the interventions.

Baseline and endline adolescent surveys collected information on the adolescent’s household (e.g., roster, dwelling characteristics, asset ownership) and about the adolescent’s sexual behavior, SRH knowledge and attitudes, education and time use, health, and socio-emotional skills. STI and HIV testing was also conducted, but prevalence was unexpectedly low at baseline, around 1% for both, so this data is not used in this analysis.

10 Surveys were completed with 92.9% of the total number of females listed during the census. The discrepancy reflects changes in participation in ELA clubs rather than refusals to participate in survey.

12
though on the whole, transport seemed less<sup>of a major constraint</sup> for the outreach activities of MCH<sup>staff, which were numerous.</sup> Two indicators of the effort put into<sup>district-wide activities</sup> arethe share of DHO time spent on<sup>district supervision and the</sup> share of vehicle mileage devoted to health<sup>centre and PHC activities.</sup> The amount of time the DHO said<sup>he devoted to district supervision</sup> varied between 9% and 25%. Most<sup>DHOs spent one day a week.</sup> It therefort seems that, despite<sup>the district-wide responsibilities</sup> of the DHO, he found it difficult to<sup>avoid spending the great</sup> majority of his time based in the hospital.<sup>About 30-40% of</sup> his total time was spent on district administration, and the remainder on hospital clinical duties<sup>(including teaching).</sup> Virtually no other senior staff were involved<sup>in district</sup> supervision: only one sister-in-charge said she did some supervision of maternity units.

The vehicle logbook analysis indicated<sup>that only around 20% of</sup> Vehicle mileage was used for PHC, with<sup>the great majority of</sup> mileage devoted to inpatients, i.e. transporting<sup>patients to</sup> and from hospital, or returning dead bodies<sup>to their homes.</sup> The requirement to have vehicles<sup>available for patient transport</sup> was clearly a major factor in the lack<sup>of transport for other</sup> activities, notably PHC.

20
# **Table A.10: Relation between income growth, poverty, and inequality with interactions**

|Explanatory variables||Dep|endent variable i|s thegrowth of|per capita incom|e(LogYt– Log|Yt-1)||
|---|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|
|Lagged log of per capita income|-0.399***|-0.179|-0.312***|-0.254***|-0.339***|-0.378***|-0.355***|-0.326***|
||(0.099)|(0.273)|(0.063)|(0.052)|(0.091)|(0.068)|(0.062)|(0.057)|
|Lagged log of per capita income * Lagged log of<br>other State spending|0.004<br>(0.007)||||||||
|Lagged log of per capita income * Lagged log of<br>investment spending||-0.009<br>(0.016)|||||||
|Lagged log of per capita income * Lagged log of<br>population density|||-0.008<br>(0.012)||||||
|Lagged log of per capita income * Lagged share of<br>urban population||||-0.003***<br>(0.001)|||||
|Lagged log of per capita income * Lagged share of<br>population with high-school diploma|||||-0.339<br>(0.381)||||
|Lagged log of per capita income * Lagged share of<br>ethnic minority population||||||-0.000<br>(0.000)|||
|Lagged log of per capita income * Lagged Gini<br>index|||||||-0.102***<br>(0.036)||
|Lagged log of per capita income * Lagged poverty<br>rate||||||||-0.038**<br>(0.017)|
|Lagged log of other State spending|-0.043|-0.004*|-0.005**|-0.004*|-0.005**|-0.004|-0.003|-0.005*|
||(0.067)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Lagged log of investment spending|0.006|0.089|0.005|0.003|0.004|0.006|0.000|-0.000|
||(0.008)|(0.145)|(0.007)|(0.007)|(0.008)|(0.008)|(0.007)|(0.007)|
|Lagged log of population density|0.042***|0.043***|0.110|0.062***|0.049***|0.044***|0.034***|0.042***|

51
Fang, H., Eggleston K., Rizzo J.A., Rozelle S., and R. Zeckhauser. 2012. “The Returns to Education in China: Evidence from the 1986 Compulsory Education Law.” NBER Working Paper 18189, Cambridge, MA:National Bureau of Economic Research.

- Fleisher, B., Li, H., and M. Q. Zhao. 2010. “Human Capital, Economic Growth, and Regional Inequality in China.” _Journal of Development Economics_ 92 (2): 215–31.

- Glewwe, P. 2002. “Schools and Skills in Developing Countries: Education Policies and Socioeconomic Outcomes.” _Journal of Economic Literature_ XL: 436–82.

- Glewwe, P., and J. Jacoby. 1994. “Student Achievement and Schooling Choice in Low Income Countries: Evidence from Ghana.” _Journal of Human Resources_ 29 (3): 842–64.

- Grant, M. J., and J. R. Behrman. 2010. “Gender Gaps in Educational Attainment in Less Developed Countries.” _Population and Development Review_ 36 (1): 71–89.

- Glick, P. 2008. “What Policies will Reduce Gender Schooling Gaps in Developing Countries: Evidence and Interpretation.” _World Development_ 36 (9): 1623–1646.

- Güleşçi S., and E. Meyersson. 2013. “‘For the Love of the Republic’ Education, Secularism, and Empowerment.” Working Paper 490. IGIER, Bocconi University.

- Güneş, P.M. 2013. “The Impact of Female Education on Fertility: Evidence from Turkey.” Grand Challenges Canada Working Paper GCC 13-01.

- Handa, S. 2002. “Raising Schooling Enrollment in Developing Countries: The Relative Importance of Supply and Demand.” _Journal of Development Economics_ 69 (1): 103– 28.

- Jaeger, D. A. and M. E. Page. 1996. “New Evidence on Sheepskin Effects in the Returns to Education.” _Review of Economics and Statistics_ 78 (4): 733–40.

Jensen, R. 2012. “The (Perceived) Returns to Education and the Demand for Schooling.” _Quarterly Journal of Economics_ 125 (2): 515–48.

39
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COVID-19. These observations lead to the related question of how deep will output losses be and how long might it take countries to recover.

To address this question, I first review past recoveries from a variety of crises (including _conglomerate_ cases), along the lines of Reinhart and Rogoff (2009, 2014) and Reinhart (2018). Second, I focus on recoveries from the wave of crises in the 1980s in EMDEs and how that experience compares to the prevailing outlook (based on the October 2020 WEO) for the postCOVID-19 recovery. Outcomes of and the outlook for inflation are briefly discussed.

# **_100 crises_**

The point of departure is a minimalist definition of economic recovery that focuses on how long it takes real per capita GDP to get back to its pre-crisis level. It is silent on possible effects of the crisis on potential output. The measure of the depth of the recession is standard and focuses on the peak-to-trough decline in real per capita GDP.<sup>20</sup> We also tally the number of years it takes to reach bottom in each episode. While the view that recessions associated with financial crises are deeper has gained ground in recent years, academic opinion is more divided on how to characterize recoveries from crises.<sup>21</sup> The duration measure is the number of years it takes to reach the _prior peak_ in real per capita income. Fatás and Mihov (2013) and others note that this definition does not capture a return to potential output or to some predetermined trend. Of course, normalizing by population does allow for some time variation in underlying trend output. In contrast to NBER dating of turning points, our approach treats any renewed downturn that takes place before the economy reaches the prior peak as a “double dip” and part of the same cycle (our dating is based on the global peak to date). The severity index, shown in the last column of Table 2, is given by

> 20 See Barro and Ursua (2008) for a related discussion of the measurement of output collapses.

> 21 For a discussion of these and other issues relating to the measurement of economic cycles, see for instance, Fatás and Mihov (2013), whose analysis is informed by both the modern literature and the early work pioneered at the National Bureau of Economic Research (NBER).

15
Our empirical analysis will focus on private sector dynamics over the business cycle. We will also examine the impact of private sector activity following the government trade, fiscal, and monetary interventions.

# **4. Data and summary statistics**

The data set is comprised of 5,941 unique firms in Cote d'Ivoire from 1976 through 1997, which covers the complete census of formal, private sector firms in the country.<sup>10</sup> The data was handcollected from paper filings at the "Registrar of Companies for the Modern Enterprise Sector", and does not include a sample bias of exited firms. These firms are required to pay the full range of taxes, and may benefit from bank finance and state technical assistance.

This data set appears to be consistent with the estimated overall private sector in Cote d'Ivoire, which includes, on average, about 800 large, formally registered enterprises (with at least 50 employees), several thousand formally and informally operating medium-sized enterprises (10-50 employees), and several hundred thousand informal microentrepreneurs, which mostly generate self-employment (Reuda-Sabater and Stone, 1992) **.** The structure of the data is an unbalanced panel of about 37,000 firm/year observations. That is, there is detailed information tracking firms over time and the data set includes firms that enter over the course of the sample period, as well as firms that exit during the sample. The variables of interest are information on sector classification at the two-digit sector level and firm ownership, collected at the time of registration, and annual data on firm employment, collected from required annual filings.<sup>11</sup> This data provides the first illustration of firm dynamics in a lowincome country, across sectors and time.<sup>12</sup> This includes entry and exit rates, as well as job creation and destruction rates at new versus incumbent firms.

> 10 The database excludes the primary sectors and state-owned firms.

> 11 All registered companies are required to submit annual filings to the Registrar of Companies with financial and employment information. Our panel includes only two missing firm/year observations.

> 12 Related work in Colombia and Ethiopia includes only the manufacturing sector.  For Colombia see, for example, Eslava et al. (2009), (2010); for Ethiopia see, for example, Shiferaw (2007), (2009), and Bedi and Shiferaw (2009).

9
divorcees. The latter is the key reason for households with female heads.

Intimations of rising female headship in many parts of the developed and developing world have heightened fears of large future increases in poverty, given the well-documented economic disadvantages faced by women (Chant 1997a, 2008; Buvinic and Gupta 1997).<sup>5</sup> A considerable number of studies, often focusing on a single country, have investigated whether households headed by men or by women are poorer. The most common practice has been to make simple comparisons of mean household per capita consumption or poverty measures, or by regressing log consumption per capita on controls and a dummy for the head’s gender.

Most observers have concluded that FHHs are poorer than MHHs (Buvinic and Gupta 1997; Chant 1997a, 2008). Yet, the evidence is far from conclusive on this point and little consensus has emerged in the literature. Buvinic and Gupta (1997) review 61 studies covering countries in Africa, Asia, and Latin America and the Caribbean, to conclude that on balance, the evidence supports the hypothesis of higher poverty among FHHs. Lampietti and Stalker (2000) review 58 World Bank Poverty Assessments carried out since 1994 and conclude that FHHs are on average poorer in 43 percent of countries examined.<sup>6</sup> Using survey data for Africa, Asia, and Central America, Quisumbing et al. (2001) find that FHHs are poorer than MHHs only in 2 of 10 countries (Ghana and Bangladesh). There are a number of reasons for the lack of agreement, including inconsistent definitions of headship; considerable diversity among FHHs and differences in how well-being is measured and in particular, how the distinctive demographics of FHHs are taken into account.

There is no universally accepted definition of headship.<sup>7</sup> Household-based surveys came to collect information on heads as a natural way to anchor relationships among household members and provide a framework for describing family structure. Such surveys typically rely on self-reported headship status. The DHS interviewer’s manual states that “the person who is identified as the head of the household has to be someone who usually lives in the household. This person may be acknowledged as the head on the basis of age (older), sex (generally, but not necessarily, male), economic status (main provider), or some other reason. It is up to the respondent to define who heads the household.” (ICF International 2012, p. 26).

Dissatisfied with survey definitions, many analysts have imposed their own definitions. It

> 5 “ ” The concept that women are overrepresented among the poor is known as the feminization of poverty .

> 6 The disadvantage is found only for de jure FHHs in 17%; in 26 percent no difference is found and in the remainder (14%), higher poverty for MHHs. 7  See Rosenhouse 1989; Handa 1994; Kishor and Neitzel 1996; Ayad et al. 1997; Bruce and Lloyd 1997; Buvinic and Gupta 1997; Chant 1997a, b, 2008; Quisumbing et al. 2001.

5
# **II. The Questions, the Surveys, and Raw Responses**

We use responses to questions posed by the WBES in 2017 and 2018 to representatives of a total of 3,430 firms in Argentina, Bolivia, Ecuador, Paraguay, Peru, and Uruguay. The respondents were business owners and top managers in a sample of officially registered firms with at least five employees in the manufacturing and services sectors. The samples are designed to be nationally representative, using a stratified survey design.<sup>7</sup> Newly designed questions were fielded on the effectiveness of various methods of preventing or resolving problems when implementing agreements, i.e. methods of enforcing agreements.<sup>8</sup> Two composite questions were posed to respondents, one about agreements with suppliers and the other about agreements with customers, each question having six subquestions. The composite questions only differ in whether asking about suppliers or customers. We chose to ask about supplier- and customer-relations separately because firms might employ very different types of strategies when managing upstream relations than downstream ones. Respondents were presented with a ‘show card’ with a Likert scale of responses and the following was read aloud: _When making agreements with_ **_[suppliers][customers]_** _, please indicate to what degree each of the following is_ **_effective in resolving or preventing_** _problems._ Each one of the following was posed separately (without numbering):

1. Personal relationship and trust

2. Mutual interest in maintaining business relationship, without involving others 3. Paid, private dispute resolution 4. Assistance of government officials 5. Intervention of other third-parties (excluding paid, private dispute resolution and government officials) 6. Legal system The response scale (displayed on a ‘show card’) was:

## Not at all Slightly Moderately Very much Extremely

The exact wording of the questions in Spanish and English is included in Appendix A.1. Nuances raised in the translation process are discussed in Appendix A.2.

When constructing questions to be administered in a long survey and addressed to firms of all types, in different institutional settings, both conceptual and practical difficulties immediately arise. A first difficulty we faced in question design was whether to ask firms about their relations

> <sup>7</sup> Full details of the methodology can be found at http://www.enterprisesurveys.org/methodology. Stratified random sampling was used, with strata based on firm size, geographical location, and economic sector. The data includes sampling weights. All results are obtained using these weights and thus refer to the entire population of pertinent establishments in the six countries. The WBES universe covers formal, private, non-agricultural, non-extractive firms with five or more employees.

> <sup>8</sup> Note that for simplicity we use firms interchangeably with establishments, which is the survey's unit of analysis. In fact, the phrase "top managers or owners of these establishments" is probably more accurate, but too cumbersome.

5