rafmacalaba/datause-encoder-data huggingface dataset:
the school day, with eight impact estimates between them: Three-quarters of those were positive, and half were positive and statistically significant.

This paper proceeds as follows: Section 2 provides definitions for a range of terms associated with the extended school day and instructional time, together with a simple analysis of the relationship between instructional time and outcomes of interest. Section 3 characterizes experiences across Latin America, comparing seven country cases. Section 4 presents a review of existing evaluations, separated by outcome variable. Section 5 presents considerations for cost implications of extending the school day, based on a Uruguayan case. The last section concludes, with a discussion of implications for policy.

# **2. Relationship between Compulsory Instructional Time and Student Learning**

Instructional time has many definitions. First, there is _compulsory instructional time_ (also referred to as “allocated class time”), which is the minimum amount of time that is allocated to instruction. For example, this would not include time for lunch, but might include time for transitioning between classes. Second, there is _instructional time_ , defined as the nonadministrative time that teachers spend in the classroom. Third, within instructional time, there is the amount of time students spend engaged in learning-related tasks, known as _time on task_ (or “academic learning time”). While total time on task is most likely to be associated with student learning (relative to compulsory instructional time or instructional time), these data are also the least readily available, since they require extensive classroom observations in order to obtain nationally representative samples. Alternatively, data for compulsory instructional time are most commonly accessible since they are often defined by legislation.

In order to assess whether a simple relationship exists between instructional time and student learning, we plot the reported compulsory instructional time from the national curricula against reading scores from the Program for International Student Assessment (PISA) in 2012 (see Figure 1). No trend emerges; as the hours of instructional time rise, reading scores rise and fall slightly but show no consistent pattern.

4
# **A RICARDIAN ANALYSIS OF THE DISTRIBUTION OF CLIMATE CHANGE IMPACTS ON AGRICULTURE ACROSS AGRO-ECOLOGICAL ZONES IN AFRICA**<sup>1</sup>

S. Niggol Seo<sup>2</sup> , Robert Mendelsohn<sup>3</sup> , Ariel Dinar<sup>4</sup> , Rashid Hassan<sup>5</sup> .and Pradeep Kurukulasuriya<sup>6</sup> 1 This paper is one of the product of a study “Measuring the Impact of and Adaptation to Climate Change Using Agroecological Zones in Africa” funded by the KCP Trust Fund and conducted in DECRG at the World Bank. We benefited from comments by Richard Adams, Brian Hurd, and Robert Evenson on an earlier draft.

2 School of Forestry and Environmental Studies, Yale University, and consultant to the World Bank; 230 Prospect St. , New Haven, CT06511; phone 203-432-9771; email <u>Niggol.seo@yale.edu.</u> 3 School of Forestry and Environmental Studies, Yale University; 230 Prospect St, New Haven, CT06511 and a consultant to the World Bank; phone 203-432-5128; email <u>Robert.mendelsohn@yale.edu.</u> 4 Development Research Group, World Bank, 1818 H St. NW, Washington DC 20433; phone 202-473-0434; email <u>adinar@worldbank.org.</u> 5 Department of Agricultural Economics, University of Pretoria, and Center for Environmental Economics for Africa; email Rashid.hassan@up.ac.za.

6 Energy and Environment Group, Bureau of Development Policy, United Nations Development Programme, New York; phone 212-217 2512; email: <u>pradeep.kurukulasuriya@undp.org.</u>
MBA training to high school students while varying the intensity of the soft skills students received. After three and half years, students in both groups showed an increase in both soft and hard skills, while only training in soft skills was linked to gains in self-efficacy, persuasion, and negotiation. Further, students who received the skill upgrade had substantially higher earnings and were more likely to start enterprises and have enterprises with higher survival rates. The training also led to larger profits and business capital investments.

In theory, socio-emotional skills-building policies may have particularly high economic returns for women in Sub-Saharan Africa, as women face more barriers to success in the workforce and binding social norms that limit expressiveness, mobility, time, network formation, and occupational choice (Chakravarty et al., 2017). Perseverance and creativity in problem solving may be essential since women bear more responsibility for daily survival and short-term budgeting, and women may be more mentally taxed and have less financial flexibility (Friedson-Ridenour & Pierotti, 2019; Schilbach et al., 2016). Communication, persuasion, and conflict resolution skills may improve women’s ability to navigate home-based barriers as they request and obtain the support of family members, influence fertility decisions, and negotiate the allocation of household assets and responsibilities. At work, higher levels of relationship building skills, teamwork and personal initiative may be required to expand networks, obtain information and find opportunities, develop skills, navigate inclusion in male-dominated occupations, and find allies for both business and emotional support. Conversely, the relationship between socio-emotional skills and economic outcomes could be weaker for women if they have lower expected returns, or they are less likely to cultivate and practice socio-emotional skills due to restrictions on mobility, socialization, and labor force participation. Evidence on the link between gender, socio-emotional skills, and earnings is still scant. A longitudinal study from the US shows that moving an individual from the 25th to the 75th percentile of non-cognitive ability at age 14 to 21 is associated with males’ and females’ wages being 10 and 30 points higher and their probability of employment being 15 and 40 points higher, respectively (Heckman et al. 2006). In this study, women’s economic outcomes are thus more strongly correlated with non-cognitive skills. Nyhus & Pons (2012) found that personality traits reduced the unemployed portion of the gender-wage gap by 12.% percentage points. A set of studies have documented gender differences in the returns to specific skills, though results differ with culture and occupation. Among Wisconsin graduates, men alone were rewarded for antagonism (the opposite of agreeableness) (Mueller & Plug, 2006), whereas management positions in Australia were associated with non-agreeableness regardless of 10
business cycle, decreasing in bad times and increasing in good times. This countercyclical evidence of endogenous changes in RR is in line with the findings for developing countries of Federico _et al._ (2014). This important source of bias due to misidentification will tend to underestimate the negative effects of changes in ECRR policy on output when compared to the traditional time-identifying strategy (much like we observe in Panel A in Figure 5). In contrast, for central bank interest rate policy, the findings shown in Panel B in Figure 10 suggest that endogenous changes in IR are essentially procyclical on impact. This procyclical evidence of endogenous changes in IR is consistent with the typical finding in developing economies from Vegh and Vuletin (2012) and Federico _et al._ (2014). This important source of bias due to misidentification will tend to overestimate the negative effects of changes in IR on output when compared to the traditional time-identifying strategy (much like we observe in Panel B in Figure 5). These findings reinforce our arguments that the mere use of timing assumptions to identify innovations in macroprudential and monetary policies proves to be a poor identification strategy.

# **7 Conclusions**

This paper has brought to light novel evidence on the macroeconomic effects of macroprudential policy. The main contribution of the paper has been to correctly identify innovations in reserve requirements by following a narrative approach (based on contemporary reports from the IMF and Central Banks) that allows us to distinguish between changes in reserve requirements exogenous to the business cycle from endogenous ones. We also push the frontier on the measurement front by creating a novel effective reserve requirement measure that takes into account the structure of deposits as well as on the identification of monetary policy shocks by creating a new central bank interest rate shock series based on Romer and Romer’s (2004) approach. We show that identification via the narrative approach turns out to be critical in assessing the output effects of changes in reserve requirements. In particular, when properly identified, an increase in reserve requirements leads to a _fall_ in output. In contrast, when traditional time-based identification methods are followed, an increase in reserve requirements has no effect on output. We show that the countercyclical nature of endogenous changes in reserve requirements is at the heart of this bias.

32
# **Appendix B Variable Definitions**

Project level variables:

- Distress: Equals 1 if project is canceled, 0 otherwise. A project is canceled if the private party has exited by selling or turning over its shares back to the government, or by ceasing operations, from the PPI database and authors’ collection.

- Contract period: Percentage of contract period elapsed until distress as defined in previous bullet, otherwise until the end of 2018, from the PPI database and authors’ collection.

- Type: _Greenfield_ if the special purpose vehicle (SPV) builds and operates a new facility, _brownfield_ if the SPV takes over an existing asset and either rehabilitates or expands it, _management and lease_ if the SPV takes over the management of the public asset while the ownership and investment decisions stay with the government, from the PPI database.

- Sector: _Electricity_ , _natural gas_ , _telecom_ , _airports_ , _railroads_ , _toll roads_ , _seaports_ , _treatment plants_ , _water utility_ , from the PPI database.

- Direct government support: Equals 1 if capital, revenue or in-kind subsidies exist, 0 otherwise, from the PPI database.

- Indirect government support: Equals 1 if payment, debt, revenue, etc. guarantee exist, 0 otherwise, from the PPI database.

- Multilateral support: Equals 1 if a multilateral bank provides financial support, 0 otherwise, from the PPI database.

- Subnational government contract: Equals 1 if local or provincial/state government grants the contract, 0 otherwise, from the PPI database and authors’ collection.

- Physical investment: Total investment in physical assets, in 2019 billion USD, from the PPI database and authors’ collection.

## Macroeconomic variables:

- Annual GDP per capita growth rate (detrended, previous year): Annual GDP per capita growth rate of the country detrended and demeaned using the filter suggested by Hamilton (2018). Previous year’s value. Calculated using the “GDP per capita growth (annual %)” from the WDI.

- Annual Depreciation (detrended, previous year): Depreciation rate of the local currency against US dollar, detrended and demeaned using the filter suggested by Hamilton (2018). Previous year’s value. Calculated using the “DEC alternative conversion factor (LCU per US$)” series, from the WDI.

- Banking Crisis Occurred (previous year): Equals 1 if a systematic banking crisis occurred in the country during the previous year, 0 otherwise, from Laeven and Valencia (2018).

33
# **1 Introduction**

While the population of Argentina descended mainly from the combination of Native, African and European ancestors (see Wang et al., 2008; Corach et al., 2010; Avena et al., 2012; Parolin et al., 2019), it is a common stereotype to see this population as mainly of European origin.<sup>1</sup> This incorrect view of the Argentine population had a correlate on the scarcity of national statistics on the numbers and characteristics of the native population for most of the national history.<sup>2</sup> We use the data from the 2010 National Census (Instituto Nacional de Estad ́ıstica y Censos, 2010), the first census to ask about native identity or ancestry at the individual level, to study the socioeconomic outcomes of the Native people of Argentina.

We compare Native and non-Native Argentines in terms of outcomes related to housing, education, labor market and health. Consistently with evidence of Native disadvantage in other Latin American countries (see Gandelman et al., 2011; Freire et al., 2015), we find that Natives, on average, fare significantly worse on all of these dimensions in Argentina. The magnitudes of the differences are moderate, corresponding on average to 12 percent of the standard deviation of the outcomes. These differences persist even after controlling for location, showing that differences are not only related to geographical differences.

We also find differences in the transmission of education from parent to children. For each level of education of the parents, the children of Natives have, on average, fewer years of education than the children of non-Natives. This is consistent with what is found for other Latin American countries (see Cruces et al., 2012; Berniell et al., 2021). Given the importance of education for economic outcomes and human development (see, for example, Angrist and Krueger, 1991; Lleras-Muney, 2005), the differences in the transmission of education between Natives and non-Natives may help perpetuate the observed disadvantage of the Native population.

We also study the differences in socioeconomic outcomes across Native groups. We find large differences across groups. While some groups obtain average outcomes above those of the non-Native population, other groups obtain outcomes well below. For example, the group with the highest average years of education, the Charr ́ua, has educational

> 1 The Mexican poet Octavio Paz wrote “Mexicans descend from the Aztecs, Peruvians from the Incas, and Argentines... from the ships.” Argentine president Alberto Fern ́andez echoed this stereotype by saying “Mexicans come from the Indians, Brazilians from the jungle, but us Argentines come from the ships, and these were ships coming from Europe...”

> 2 This was also the case in other countries in the region. Until recent censuses, the statistical offices of many Latin American countries did not systematically gather data on ethnicity or race (see Loveman, 2014; Freire et al., 2018).

2
Ministry of Finance and National Planning (2009) Financial Report for the Year Ended 31<sup>st</sup> December 2008, Republic of Zambia: Lusaka Ministry of Finance and National Planning (2008) Financial Report for the Year Ended 31<sup>st</sup> December 2007, Republic of Zambia: Lusaka Ministry of Finance and National Planning (2007) Financial Report for the Year Ended 31<sup>st</sup> December 2006, Republic of Zambia: Lusaka Ministry of Health (2009) Annual Health Statistical Bulletin 2008, Republic of Zambia: Lusaka.

Ministry of Health (2001) National Strategic Plan 2001-2005, Republic of Zambia: Lusaka Sahn,D. and S. Younger (2000) Expenditure Incidence in Africa: Microeconomic Evidence. Fiscal Studies, vol. 21, no. 3, pp. 329–347.

Fiszbein, A. and N. Schady (2009) Conditional Cash Transfers: Reducing Present and Future Poverty. World Bank. Washington DC.

Republic of Zambia (2011) Sixth national development plan 2011 – 2015: Sustained Economic Growth and Poverty Reduction, Available at: siteresources.worldbank.org/INTZAMBIA/Resources/SNDP_Final_Draft__20_01_2011.pdf.

van de Walle, D. 1998. "Assessing the Welfare Impacts of Public Spending." _<u>World Development</u>_ 26(3): 365–79.

World Bank, 2012a, Social Protection Databases. Available at: <u>http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTSOCIALPROTECTION/EXTSAFET YNETSANDTRANSFERS/0,,contentMDK:22986320~menuPK:8117027~pagePK:210058~piPK:21 0062~theSitePK:282761,00.html</u> World Bank, 2012b, Data Indicators. Available at: http://data.worldbank.org/ World Bank. 2011, "Zambia: Country Economic Memorandum." Policies for Growth and Diversification. 2004. Report No. 28069-ZM.

World Bank, 2010. _Zambia, Impact Assessment of the Fertilizer Support Program: Analysis of_ - _effectiveness and efficiency_ , Washington DC. Available at: http://www <u>wds.worldbank.org</u> World Bank, 2008, Social Protection Atlas, Human Development Network, Washington DC.

World Health Organization 2011 The Abuja Declaration: Ten years on. WHO, Geneva. Available at: <u>www.who.int/healthsystems/publications/abuja_declaration/en/index.html</u> 39
with Si,t stock holdings, Bi,t bond holdings, Hi,t real estate ownership, Di,t household debt, and p and r the corresponding prices. Both stock prices pstocks,t and house prices phousing,t experienced sharp drops. The S&P 500 index lost 17% of its value between January 2006 and January 2010. The Case Shiller national house price index (resp. 20-city house price index) dropped 19% (resp. 28%). The sharpest drop in total household net worth (−19%) happened between 2007 and 2008. Both housing (−15.5%) and non-housing wealth (−22.7%) declined, with housing at about half of households' net worth.

The average decline in housing wealth masks considerable heterogeneity across space, racial and ethnic groups, and initial income levels. The heterogeneity is reflected in differences both in the households' portfolio composition and in the distribution of price drops. The spatial heterogeneity in housing wealth losses was partly driven by housing supply constraints, with stronger increases during the boom and declines during the bust in the least elastic metropolitan areas (Glaeser, Gyourko, and Saiz 2008); recent evidence also points towards land hoarding as a mechanism pushing house price volatility upwards in metropolitan areas with elastic housing supply.

Within metropolitan areas, the adverse effects of the decline in housing wealth are most strongly felt in specific neighborhoods rather than spread more or less evenly across space. This spatial segregation of negative welfare impacts is driven by at least two mechanisms: first, foreclosed homes increase the supply of housing in the neighborhood, thus lowering the price of nearby properties (Hartley, 2014). Second, homeowners are less likely to invest in the maintenance and upkeep of foreclosed homes, leading to externalities on neighboring properties (Gerardi, Rosenblatt, Willen, and Yao, 2015). Further, careful empirical analysis has identified economically significant impacts of vacancies on crime in the vicinity of foreclosed properties (Cui and Walsh, 2015). Foreclosure externalities reinforce a dynamic of interacting segregation and inequality.

Housing-related wealth losses were also more acute among low-income and minority borrowers. Such losses are in great part explained by the highest fraction of minority households using highcost mortgage loans, even controlling for credit score and other measures of creditworthiness (Bayer, Ferreira, and Ross 2016). This descriptive fact is the consequence of both an adverse match of lenders to borrowers, and a worse treatment of minority borrowers by lenders with 25
MALAWI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 8. Illustrative investment targets for infrastructure in Malawi**

||**Economic target**|**Social target**|
|---|---|---|
|ICT|Fiber-optic links to neighboring capitals<br>and submarine cable|Universal access to GSM signal<br>and public broadband facilities|
|Irrigation|Develop 162,000 hectares of small-scale irrigation|n.a.|
|Power|700 MW new generation|Electricity coverage of 17.1%<br>(75.9% urban and 1.1% rural)|
|Transport|Regional connectivity by good quality 2-lane paved road<br>National connectivity by good quality 1-lane paved road|Rural network gives access to 80% agricultural<br>production<br>Urban population within 500-meter paved road|
|WSS|n.a.|Sustain MDG target for water<br>Achieve MDG target for sanitation<br>Clear sector rehabilitation backlog|

_Source:_ Mayer and others 2008; Rosnes and Vennemo 2009; Carruthers and others 2009; You and other, 2009. n.a. = not applicable.

Meeting these illustrative infrastructure targets for Malawi would cost $0.6 billion per year through 2015. Capital expenditure would account for 80 percent of this requirement. The two highest price tags are associated with the power and water sectors, each requiring about $190 million annually, which is about as much as the other three sectors put together. The power sector need is associated with providing 700 megawatts (MW) of new generation capacity to meet demands over the next decade, as well as boosting electrification to 17 percent overall. The water-spending needs relate to sustaining the MDG target level for water as population expands, attaining the MDG target for sanitation and clearing the sector’s rehabilitation backlog (table 9).

**Table 9. Indicative infrastructure spending needs in Malawi, 2006–15**

|Sector|CAPEX|O&M|Total needs|
|---|---|---|---|
|ICT|29|12|41|
|Irrigation|59|1|61|
|Power|176|14|190|
|Transport|45|39|84|
|WSS|144|44|188|
|Total|471|113|564|

Sources: Mayer and others 2008; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009. Derived from models at http://www.infrastructureafrica.org/aicd/tools/models.

_Note:_ O&M = operations and maintenance; CAPEX = capital expenditure.

Malawi’s infrastructure spending needs look particularly high relative to the country’s GDP, since they would absorb 20 percent of GDP for a decade. Infrastructure investment alone would absorb 15 percent of GDP, comparable to what China invested in infrastructure during the mid-2000s. Although these numbers are very high, they are still below the average 22 percent of GDP that other low-income, nonfragile African countries would need to spend.

20
direction of such biases. Table 1 shows the means of some relevant variables from the Literature Review database used in this analysis. More than half of the papers included in the review used surveys to evaluate R&D additionality, and one out of six papers used administrative records for the same purpose, while close to 1 out of 5 used more than one source of data to assess impacts. With regards to the type of data arrangement used for the evaluation, around 51 percent of surveyed papers used a panel structure, and 27 percent used pooled cross section (or time series) for the analysis. Overall, many papers used panel data structure in order to quantify for contemporary or time-dependent factors that may play a partial role in determining the magnitude and sign of the impacts. The estimation methods used to assess impacts relied mostly on quasi-experimental approaches (35 percent of all studies), including propensity score matching and regression discontinuity, and around 11 percent of the studies relied on micro-simulation techniques to build a statistically reliable counterfactual. More than 40 percent of studies used structural models or instrumental variables to assess impacts, which are indicative of the small proportion of papers left that prospectively designed and evaluate R&D programs by using randomized assignment into the comparison groups.

Table 1. Database Summary Statistics (IE)

||Data Used for<br>Analysis|Type of Data|Methodology|
|---|---|---|---|
|Survey|56.76|||
|Admin. Records|16.22|||
|Mixed|18.92|||
|No Data Used|5.41|||
|Not Specified|2.7|||
|Panel||51.35||
|Cross-section||16.22||
|Pooled Cross<br>Section||27.03||
|Not Specified||5.41||
|Quasi-experimental|||35.14|
|Simulation|||10.81|
|Structural model/IV|||43.24|
|Qualitative/other|||10.81|

17