rafmacalaba/datause-encoder-data huggingface dataset:
data used by PovcalNet. Most CPIs are from the IMF’s International Finance Statistics, while most PPP exchange rates are from the International Comparison Program (PPPs for household final consumption expenditure). More details on the price data used are available in Lakner et al. (2018) and Atamanov et al. (2018). Second, we need population data to <mark>aggregate poverty estimates across regions and globally. We use annual population projections for each country from the World Bank.</mark><sup>9</sup> Finally, to arrive at regional and global poverty rates, we also need estimates for the 3% of the world for which we have no distributional data. In these cases, we follow the aggregation method used by Chen and Ravallion (2010) and deployed by PovcalNet, which assumes regional poverty rates for countries without a poverty estimate.

# 4 Results

This section presents the results from the simulations described above. First, we show distribution-neutral poverty projections towards 2030, both at the global and regional level, focusing on the poverty rate measured at $1.90/day in 2011 PPPs.<sup>10</sup> Second, we explore what would happen if growth or inequality changes in a positive or negative direction, and the trade-off between increased growth and reduced inequality.

## 4.1 Impacts on Poverty: Global and Regional Trajectories to 2030

Figure 5 presents our simulated trajectories for the global poverty rate to 2030 for the three different distribution-neutral growth scenarios: that countries follow their growth patterns of the past 10 years, of the past 20 years, or that they follow the growth projections from the WEO.

All scenarios put the global poverty rate in 2030 in the range of 6-7%. The scenario using historical growth rates from 1997-2017 is a bit more optimistic, due to the high growth rates at the turn of the century. While the different growth scenarios do not matter much at a global level, there are starker differences at the regional level. In Latin America & the Caribbean, using growth rates from the past 10 years results in almost no decrease in poverty – a pattern mostly attributable to poor recent growth in Venezuela -- while the other two scenarios decrease poverty substantially towards 2030. In the Middle East & North Africa, both historical growth scenarios yield increasing poverty rates towards 2030 while using the WEO growth projections suggests that poverty will be halved by 2030. The global poverty rate is largely driven by Sub-Saharan Africa, which in all three scenarios has poverty rates at or above 30% in 2030, while the other regions of the world have rates below 7% (the vertical axis differs across regions in Figure 5).

> 9 The projections and estimates are available at <u>https://datacatalog.worldbank.org/dataset/population-estimates-andprojections.</u>

> 10 See Ferreira et al. (2016) for a description of how the $1.90 international poverty line has been defined.

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# _A new approach – three principles of thinking_

The _World Development Report 2015_ moves behavioral approaches center stage. It develops three principles of thinking and decision making by synthesizing evidence from across the social and natural sciences – including psychology, sociology, anthropology, political science, neuroscience, and cognitive science. The principles are: _Thinking automatically._ Most human thinking is automatic, not deliberative. It is based on what effortlessly comes to mind. Changing what most easily comes to mind can thus be a powerful means to change behavior.

_Thinking socially._ Human thinking is socially conditioned. What others are doing and expecting others to do intimately shapes an individual’s own preferences. Enhancing the salience of others’ cooperative efforts can thus shift preferences. Introducing change initiatives at the group rather than the individual level and cueing an aspect of social identity can also shift preferences.

_Thinking with mental models._ Thinking uses mental models to interpret the environment (Denzau and North 1994; North 1994). Examples are categories, identities, default assumptions, and automatic causal narratives. Many are widely shared social constructions ( _cultural schemas)._ Most are useful, but some are not. What people take to be common sense may be shaped by the political and economic institutions to which they have been exposed. Exposure to different interpretive ideas and role models may change mental models and so make possible sustained changes in behavior.

The three principles encompass psychological, social, and cultural influences on thinking that fall outside rational actor models. By presenting alternatives to standard economic assumptions about decision making and illustrating their applicability to areas including health, savings, productivity, child development, and climate change, the report demonstrates the broad relevance of behavioral insights and the importance of engaging with them in policy-making.

A central message of the WDR is that the causes of underdevelopment are not limited to — the core factors in the standard economic framework poor technology and resources, distorted incentives, and problems of information. Psychological, social, and cultural factors are also core factors. Although people generally believe that they respond objectively to a situation, culture 4
# _Sampling Framework and Random Assignment of the Program_

The sampling framework included three stages. In the first stage, the _collectivités locales_ ( _urban and rural communes_ ) were selected. The selection of _collectivités locales_ was drawn from the universe of urban communes and _communautés rurales_ included in the four selected regions. Two _collectivités locales_ —Commune de Thiès and Touba Mosquée—were excluded from the sampling universe for being larger than the rest (Touba Mosquée was close to 500,000 residents and Commune de Thiès was around 250,000 residents; the population of the next seven largest cities was between 171,000 and 113,000); from the remaining list there was a random selection from the largest _collectivités locales._ The second stage involved sample cluster selection. The selection of clusters or Census Districts was drawn from the universe of clusters. The last stage of the sampling framework involved household selection. The selection of households was randomly drawn among all households within the selected clusters that had at least one child less than two years of age, and was proportional to the number of households per cluster.

The randomized assignment of _comunes_ was done at the locality level, and it included 110 urban and rural (cluster) _villages_ randomly selected (55 for treatment and 55 for control) among eligible _localites_ distributed across 7 of the 11 regions of Senegal. The evaluation design objective was to test whether handwashing behavior can be improved among the poor through the use of promotional strategies, and assess the effectiveness of the program at improving the health of the population at risk of diarrhea, and incidence of sanitation-related diseases. The program targeted a population of mothers and other caregivers between the ages of 19-49 and infants up to 13 years of age, living in urban and rural areas of the country, with the main objective of delivering the information campaign and hygiene promotion contents to the “stewards” of child health within the household.

While the intervention was designed to improve the health and hygiene practices of the treatment population, the intervention also collected data on children’s health and mental development. Taking this information, children’s mental development was 9
GVC participation of a country-sector ( _gvcil_ ) is measured as the import content of exports (as percent of a sector’s exports) for the 28 exporting countries for the year 2015 (latest available). The measure is based on data from the OECD International Input-Output tables and computed following the approach by Borin and Mancini (2019). While the GVC participation measure varies across exporting countries, it is only available at a more aggregate sector level covering 15 TiVA sectors.

To capture the importance of imported inputs for production, we rely on import data from UN Comtrade for 2017 and compute a sector’s share of parts and components (as percent of total imports) in partner countries ( _gvc partnerjl_ ). Parts and components<sup>19</sup> at the HS6-digit product level are determined based on the UN Broad Economic Category (BEC) classification and then further aggregated to the TiVA sector level in order to be consistent with the backward GVC participation measure.<sup>20</sup> We interact the GVC measures with the shock measure in the exporting and partner country to reflect a shock’s transmission through the GVC channel. The upstream shock variable ( _upstream shockilt_ ) is computed based on IPI growth and data from the OECD International Input-Output tables to compute weights.<sup>21</sup> To control for global sector-time trends we use industrial production growth data from UNIDO INDSTAT which are available at the 2-digit ISIC level and cover 60 countries representing more than 90 percent of world GDP and 95 percent of manufacturing value added. Our measure _global outputmt_ is based on manufacturing value added in constant 2015 USD. The global <mark>average is based on the relative contribution of a country (weight) to the world’s total manufacturing value-added. The correlation matrix between independent variables is shown in Appendix 2, while the summary statistics are reported in Appendix 3.</mark>

# **3. Regression results**

Before assessing the heterogeneous impact that Covid-19-induced shocks could have across different sectors, we examine the relationship between aggregate bilateral export growth and supply shocks both at home and in third countries and demand shocks in trading partners. Results are reported in Table 1.

> 19 Parts and components are defined as BEC sectors 111, 121, 21, 22, 42, and 53, as well as the Standard Industrial Classification Sector 65.

> 20 We prefer this measure over the imported input share of exports in the partner country because we are mainly interested in a sector’s dependence on imported inputs, regardless of whether these are used in the partner country’s export or domestic production.

> 21 That is, source countries are limited to 64 TiVA countries.

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Policy Research Working Paper 6431

# **Abstract**

This paper estimates the impact of informality on firm profits using a new firm-level survey designed specifically for this study. The survey was administered to about 1,200 firms with 50 employees or less in Ecuador’s two largest cities, Quito and Guayaquil, plus two main centers of economic activity near the northern and southern borders. The paper’s results confirm that the extent of firms’ compliance with a set of regulatory requirements is linked to the perceived costs and benefits of informality, such as the probability of detection by the authorities and the likelihood of being fined. Nonetheless, taking into account the non-random placement of firms along the formality-informality spectrum and controlling for a large set of firm, owner, and location characteristics, the paper finds that more formal firms tend to be more profitable and have higher output per worker. This impact operates, inter alia, through more formal firms’ ability to obtain improved access to credit and achieve higher sales by issuing receipts to clients.

This paper is a product of the Poverty Reduction and Economic Management, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at dmedvedev@worldbank.org@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **1. Introduction**

Parental leave has been increasingly proposed as a family policy to facilitate balancing care responsibilities and promoting gender equality in the distribution of care tasks. Unlike maternal and paternal leave schemes that target one parent, parental leave is designed to be used by mothers and fathers to share care responsibilities. Evidence shows that fathers' leave take-up increases their involvement in childcare and housework, and these effects persist after several years (Tamm, 2019; Almqvist and Duvander, 2014; Schober, 2014; Kotsadam and Finseraas, 2011). Moreover, father involvement in childcare has positive effects on a child's cognitive and non-cognitive development (Cano et al., 2019; Lamb, 2010; Cabrera et al., 2007; Amato and Rivera, 1999), on the mental well-being of both mother and child (Twamley et al., 2013), and on gender equity (Evertsson et al., 2018; Coltrane, 1996). However, fathers' leave take-up is still low. Across the world fathers’ use of the benefit, when available to them, is low unless mandated (Karu and Tremblay, 2018).<sup>2</sup> Evidence shows that financial reasons, career penalties (Twamley and Schober, 2019), and fathers' identity and reputational concerns (Weisshaar, 2018; Dahl et al., 2014) are among the take-up barriers to paternity (or parental) leave. Uruguay is no exception. Despite the introduction of a parental leave program in 2013 that allows either parent to work half-time while receiving full-time pay for four months, fathers account for only 2% of total beneficiaries (BPS, 2021).

This paper investigates the effects of an intervention to improve the awareness and knowledge of the parental leave program and increase fathers' intention of taking leave. Together with the Social Security Institute of Uruguay (BPS), we designed and implemented an information intervention delivered via email and text messages (SMS) targeting workers contributing to BPS who were recent parents and/or of reproductive age.<sup>3</sup> Two parallel experiments were implemented, building on insights from behavioral science regarding drivers of behavior and intention formation (Ajzen 1991, Gollwitzer and Sheeran 2006, Michie et al. 2011). The first one focused on female and male workers and tested the effect of an information message (treatment) against no messages, i.e. regular access to existing publicly available information to date (control). The second experiment focused on recent parents. Here we tested the differential effects of three messages: (i) information about the parental leave program (same as in experiment one), and two “information plus” messages, (ii) a message emphasizing the benefits of men's involvement in childcare through the testimony of a father, and (iii) a message emphasizing the importance of planning and sharing childcare between parents.

> 2 For example, according to the OECD Family Database (https://www.oecd.org/els/family/database.htm) in 2021 across OECD countries fewer than 50 men for every 100 live births used parental leave benefits. 3 We consider individuals between 18 and 42 years of age.

2
**Table 10** . **Changes in Self-Reported Food Insecurity from 2007 to 2008**

||**Prevalence of food insecurity (%)**<br>**Population of food insecure (millions)**|
|---|---|
||_48 developing countries (43.3% of developing world population)_|
|2007|29.33%<br>821.4|
|2008|29.28%<br>820.1|
||−0.05 percentage points<br>−1.3 million|
||_47 developing countries excluding India (23.3% of developing world population)_|
|2007|31.51%<br>532.8|
|2008|34.04%<br>575.9|
||2.53 percentage points<br>43.1 million|

_Source_ : Author’s calculations from GWP data (Gallup 2011) .

Although we have explored validity issues in previous sections, another relevant question is whether the GWP results are supported by any other survey evidence. One other reasonably large survey of developing countries that was conducted before and during the crisis is the Afrobarometer survey. A recent working paper by Verpoorten, Arora and Swinnen (2011) explores trends in an Afrobarometer indicator that pertains to a very similar question to the one asked in the GWP and finds a 3-percentage-point increase in food insecurity in urban Africa from 2005 to 2008 and a 2-percentage-point increase in rural Africa. Thus, the overall picture of some deterioration in food insecurity in Africa is common across both the GWP and Afrobarometer surveys. Second, and perhaps most important, the most recent World Bank estimates of poverty trends also suggest that global poverty fell between 2005 and 2008 on every continent (World Bank 2012). Third, the FAO (2012) has revised its estimates of large increases in global hunger in 2009. The most recent estimates show a relatively steady decline in global undernourishment, consistent with both the Gallup and World Bank poverty estimates.

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# Table 1

_Data collection from studies vs. interviews_

|From impac|t evalua<br>only|Data c<br>tion reports|ollected<br>After int<br>impl|erviews with<br>ementers||
|---|---|---|---|---|---|
|Number of<br>indicators|Per<br>ind|centage of<br>icators|Number of<br>indicators|Percentage of<br>indicators|Total<br>indicators|
|Overarching<br>aspects<br>15.6||60%|20.8|80%|27|
|Content<br>11.3||43%|17.4|67%|10|
|Delivery<br>9.4||36%|17.7|68%|14|
|TOTAL<br>25.50||50%|38|74%|51|
|For interviewed programs o|nly:||50|98%|51|

27
teachers with pedagogical support (Kraft and Blazar 2016). Thus, in the third group (Coaches Group henceforth), teachers received the same short-term training and structured curriculum unit as the Sequence Group, with the addition of individual tutoring from pedagogical coaches. Coaches worked with teachers on a weekly basis to promote the full understanding of the nature of the activities proposed in the curriculum unit, and provided extra support, explanations, and feedback depending on each particular teacher’s needs. The literature shows that coaches seem to increase the fidelity of implementation and improve teacher and student performance (Kretlow and Bartholomew 2010). <u>Kraft, Blazar, and Hogan (2016)</u> estimate that coaching raised student performance on standardized tests by 0.15 standard deviations and improved instructional practice by 0.58 standard deviations based on effect sizes reported in 44 studies that used experimental or quasi-experimental designs. This effect compares favorably when contrasted with the larger body of literature on teacher training (Yoon et al. 2007; Garet et al. 2011).

Comparing these two treatment groups with the Control Group allows us to confidently establish the marginal effect of complementing training sessions with either just a structured curriculum unit or additional coaching effort. The first set of results clearly suggests that there is a gain in terms of learning. Specifically, students in the Sequence Group and Coaches Group learned between 55 percent and 64 percent of a standard deviation more than those students in the Control Group, respectively. This is equivalent to an average increase in student achievement from the 50th to the 66th (70th) percentile, approximately, for a student moved from the control condition to the structured curriculum condition (coaches condition). The marginal costs of doing so are also relatively low compared to the benefits. Complementing training sessions with a structured curriculum unit costs (per student) 0.84 dollars per 0.1 standard deviations; in other words, it costs 0.84 dollars to move a child from the 50th to the 53th percentile approximately, whereas complementing this intervention with additional coaching effort costs (per student) 2.28 dollars per 0.1 standard deviations.

5
Learning from Developing Country Power Market Experiences: The Case of Philippines and geothermal generation over the past decade, and the rapid penetration of other renewable sources in recent years (wind, biomass and solar).

<!-- Start of picture text --> Hydro<br>16%<br>Geothermal Solar<br>8% 4%<br>Other renewables<br>Fossil‐fuel power 7%<br>plants Biomass<br>69% 1% Wind<br>2%<br><!-- End of picture text --> Figure 5‐8 Share of renewable generation capacity by technology in Philippines, 2017.

Source: Department of Energy.

Generation capacity additions in Philippines have been primarily in coal‐based power plants. Modest additions of wind, solar and biomass power plants have been completed in the recent years in Philippines, reaching 6% of total dependable generation capacity with 1,243 MW in 2017, primarily driven by Feed‐in‐ Tariffs. Non‐hydro renewable installed capacity is dominated by 1,752 MW of geothermal power plants (see Table 5‐4). Investment in renewable energy generating assets are incentivized by the Feed‐in‐Tariff scheme and other measures adopted by Philippines by enactment of 2008 Renewable Energy Act. As a result, Philippines has seen rapid penetration of other renewable sources over the past few years, with wind, solar and biomass totaling 1,243 MW in 2017, up from 93 MW in 2013.

Table 5‐4 Evolution of renewable installed capacity in Philippines.

|**Year**|**Hydro**|**Geothermal**|**Solar**|**Wind**|**Biomass**|
|---|---|---|---|---|---|
|**2005**|2,725|1,683|1|9|0|
|**2010**|3,021|1,351|1|20|20|
|**2017**|2,369|1,752|700|383|160|

Source: Department of Energy Power generation from renewable resources in Philippines has fallen from 32% of total output in 2005 (18 TWh) to 25% in 2017 (23 TWh, see Table 5‐5). Most of renewable energy generation continues to be produced by hydro and geothermal power plants, amounting to 11% and 10% in 2017, respectively; while wind, solar and biomass each contributed about 1% to total power generation in Philippines during 2017 (see Table 5‐5 **Error! Reference source not found.** ).

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