rafmacalaba/datause-encoder-data huggingface dataset:
# **V. THE WAY FORWARD: RECOMMENDATIONS FOR POLICYMAKERS**

Our review of the findings to date on financial literacy and financial education programs has suggested a number of practical implications for policymakers going forward. We conclude by highlighting some of the lessons from the literature, and also by noting gaps in our knowledge where future research is needed. We have divided the lessons into those for programs for consumers and those for entrepreneurs, though some lessons apply to both.

## _To improve financial education for consumers:_

**Start with awareness.** The FinScope surveys demonstrate that lack of awareness of financial products and institutions is likely a major barrier to the take-up of financial products, particularly insurance. Moreover, Carpena et al. (2011) indicates that it may be easier to design interventions to improve financial awareness and attitudes than traditional numeracy skills.

**Leverage social networks and peer effects.** Financial literacy interventions have been shown to have positive spillover effects in the social networks of those who participate in the intervention. For instance, Duflo and Saez (2003) find such effects for the colleagues of employees who attended an information session on a retirement plan, and Cai (forthcoming) finds that the friends of rice farmers in rural China who attended an information session on crop insurance were also more likely to take-up the product. Promoting such spillovers would be a smart and costeffective way of spreading the benefits of financial education. Particularly in the developing country context, related work by Conley and Udry (2010) on pineapple farmers in Ghana shows that informational networks are fairly strong, and potentially an important leveraging point for interventions such as financial education.

**Identify and target vulnerable populations.** Most surveys indicate that women, youth, the elderly, and those with lower incomes and educational attainment are less likely to be financially literate. Many surveys also indicate disparities between urban areas and rural areas, and geographical regions, and differences by race, ethnicity, employment status, and a variety of other factors. However, more research needs to be done on how to effectively target those with lower levels of financial literacy.

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pre-trial is consistent with the pre-trial procedure (Section 2): the pre-trial judge presents a road map of the case during the first hearing and may commission additional reports and instruct the submission of more supporting pieces. Taken together, these results from firms’ monthly tax filings show that court cases hurt firms’ economic activity, and that parties adjust their activity levels from the first pre-trial hearing onward.

These results are consistent with the negative implications of having a case in court, as reported by firms in our survey (Table A8). Whether as plaintiff or defendant, firms estimated direct costs of lawyer and court fees to be CFA franc 1.755–3.386 million (approximately USD 3,310–6,390). Nearly all firms (96–98%) further report losses in terms of their own or employees’ work hours; all qualify these losses as large, and those able to quantify them estimate their value at approximately CFA franc 1.3 million (approximately USD 2,450). Perhaps unsurprisingly, given these numbers and the substantial drop in revenues discussed above, 20–24% of firms report having had to take on debt because of their most recent court case as plaintiff and/or defendant. Further, beyond direct and indirect costs, 33–40% of respondents also report that having the case prevented them from making investments. Taken together, these results suggest that negative effects on business activities may persist into the medium- or long-run, and the main reported costs are not one-off but increase with court case duration. We now estimate the economic impacts of slow judicial procedures more formally.

# _Speed of pre-trial_

Should we expect the gains in the speed of justice caused by the reform to have meaningful firm-level economic impacts? We start by showing that the pre-trial duration mandated by the reform is associated with lower economic losses for firms. Interacting _τi,t_ with a dummy for completing pre-trial in under four months in the event study specification (4) shows that the adverse impact of starting pre-trial is concentrated among firms whose cases spend more than four months in these hearings (Panel B, Figure 9). Estimating our difference-in25
also been associated in many areas with higher probabilities of disease incidence and spread. Rising mortality accompanied urbanization in Europe and North America in the first half of the nineteenth century in many cities (Cutler et al., 2006). In the case of education, however, the link is more likely to be one-way: population density is likely to lead to greater productivity in public spending as each school covers more students.

# **C. Modeling Considerations**

A typical empirical model linking an education or health indicator to its determinants takes the following general form:

> where the subscript _i_ denotes country, _M_ refers to the education or health indicator adopted as the dependent variable, and _Z_ refers to a vector of socioeconomic variables such as those discussed in the literature survey above.

Our interest, however, is in explaining changes in the education or health measure over time. An empirical model for this may be written in the following general form:

> (2) _LogM i_ ( _t_  _n_ )  _LogM it_  _f_ ( _LogM it_ , _Z iavg_ )

where the left hand side refers to the log-change in _M_ over a _n_ -year interval from year _t_ to year _t_  _n_ and, on the right hand side, _Mit_ refers to the initial value of _M_ at time _t_ ; and the subscript _avg_ refers to the average value of the independent variables over the _n_ year interval.

This empirical model differs in three main respects from the conventional approach: (a) it defines the dependent variable as the growth rate of the indicator over time rather than its _level_ at a point in time; (b) it introduces the initial level of the indicator as an independent variable and (c) it uses average values over past intervals for the independent variables rather than contemporary values. These three aspects are discussed further below.

_Formulation of the dependent variable_ . Most studies use the level of education attainment or child mortality at a given point in time as the dependent variable. Some (see Wang, 2002 and Baldaccci et al., 2008) average the indicator over five-year periods to reduce measurement errors. Since our interest is in explaining changes over time, we define our dependent variable as the log-change in indicator levels between 1980 and 2014 for each country in our sample. This is analogous to what is done in the 7
average) composition effects related to total emissions and metal pollution partially compensate the positive scale effect. These results go against the idea of pollution havens. Here, countries with better regulatory frameworks experience relative growth in emissions from dirty industries, while the opposite is true for countries with less stringent frameworks such as Honduras. When allowing for existing trends in emissions, we notice that in all countries, with the exception of Honduras, emissions increase after the agreement through scale effects. Nicaragua is the only country where the agreement significantly reduces the rate of changes in the composition of production. Emissions from manufacturing exports significantly increase the overall emissions and, with the exception of Honduras, the manufacture of exports becomes ―dirtier.‖ This result probably reflects the fact that, although these economies expanded a few of the dirty activities for exports, they also increased imports from other dirty production, reducing domestic production in these segments.

# **Figure 2. Decomposition in total emissions - Baseline**

<!-- Start of picture text --> Pollution decomposition (output) – change in levels Pollution decomposition (output)– change in rates<br>SVL SVL<br>NIC NIC<br>HND Scale HND Scale<br>Composition Composition<br>CRI CRI<br>CA4 CA4<br>-10 0 10 20 30 40 -20 -10 0 10 20<br>Pollution decomposition (export) – change in levels Pollution decomposition (exports)– change in rates<br>CA CA<br>SLV SLV<br>NIC Scale NIC Scale<br>Composition Composition<br>HDN HDN<br>CRI CRI<br>-10 0 10 20 30 40 50 60 -10 0 10 20 30 40<br>Source: IPPS and authors calculations<br><!-- End of picture text --> Results change significantly when we allow for changes in pollution intensity. For almost all countries, the technique effect is the largest component of changes in emissions from production. Under the alternative scenario, the level of emissions in manufacturing production decreases steadily over time (see figure 3). Patterns of emissions from exports are less clear, although the technique effect plays a large role; the sector is still dominated by scale gains due to changes in scale in all countries but Honduras. When we allow for existing trends, we find only a minor expansion of pollution in production and exports 16
from WDI and for Argentina we do not line up the population since the surveys used are not nationally representative.

2) Line up welfare per capita to reflect growth between the year of the survey and 2013. Depending on the country we look at growth estimates of household final consumption expenditure or GDP per capita. We assume that welfare per capita of every household moves at a same growth rate.

3) In order to keep consistency with PovcalNet poverty estimates, we adjust the poverty line in each country to replicate PovcalNet’s poverty rates.

# **Child Population**

Developing countries in this study include all countries except Australia, Belgium, Cyprus, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, Japan, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom and United States. The world’s number of countries, 214, reflects the number of countries according to WDI.

In Tables 1 and 2 the number of children in the World and in developing countries include three fifths of UNDESA’s population in the 15 to 19 age bracket (we assume that the population in the age bracket is uniformly distributed by age). No such assumption is maintained for the child population in the GMD sample. For 24 countries, UNDESA’s population is unavailable and we use WDI’s population instead. For these countries, UNDESA population totals for children are estimated using averages across all other countries in the same region and income group.

# **China**

China is a special case because the World Bank does not have access to individual level records from the Chinese Household Budget Survey (HBS), which is the source of official Chinese poverty statistics. The World Bank’s international poverty estimates for China are instead based on an approximate distribution derived from grouped data, which cannot be used for poverty profiling. This study therefore utilizes household level data from the 2013 Chinese Household Income Project Survey (CHIPS), made available to the public by Beijing Normal University. The poverty rate for urban and rural China, derived from the 2013 HBS, is then applied to the CHIPS data to generate profiles of the extreme and moderate poor in China.

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# Figure A2: Alternative Assignment Scenarios for Informal Expenditure

## (a) Budget Shares (Probability Scenario)

## (b) Slopes (Probability Scenario)

<!-- Start of picture text --> CD<br>BI<br>TZ<br>NE BF TD BJ SN CG BI CD CL<br>MZ RW ST KMCM PG NE TD BJ ST CG CRBR<br>BO MA MZ BF TZ KMCMBOPG TN CO ZA<br>EC RS RW SZ<br>TN CO PEME MXBR SN MA DO ME UYMX<br>SZ DO EC<br>CR RSPE<br>CL<br>UY<br>ZA<br>5 6 7 8 9 10 5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD<br>(c) Budget Shares (Robustness Scenario) (d) Slopes (Robustness Scenario)<br>CD<br>BI TZ<br>NE BFTD BJ SN CG CL<br>MZ RW ST KMCMBOPGMA TN EC RS BR BI CDNE MZ BFTD TZBJ ST KM BO CG TN CRBR<br>CM<br>RW PG SZ COMEZA<br>PE MX MA<br>ME<br>CO SN DO<br>SZ DO CR EC RS UYMX<br>PE<br>UY CL<br>ZA<br>5 6 7 8 9 10 5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD<br>5<br>100<br>80 0<br>−5<br>60<br>−10<br>40<br>−15<br>Informal Consumption Share 20 Informal Consumption Slope<br>−20<br>0<br>−25<br>5<br>100<br>80 0<br>−5<br>60<br>−10<br>40<br>−15<br>Informal Consumption Share 20 Informal Consumption Slope<br>−20<br>0<br>−25<br><!-- End of picture text --> This figure is the equivalent of figure 3 for the two alternative assignments of store types to formality. The Probability scenario uses the observed probability of VAT payment by store type in the Mexican census as the formality probability of each store type across countries (see Figure 1). The robustness scenario differs from the central scenario by assigning specialized stores to the informal sector, in addition to maintaining corner stores, non brick and mortar and self-production in the informal sector. Panel (a) and (c) show informal budget shares, on log per capita GDP. Panel (b) and (d) show the the slope of informality Engel curves on log per capita GDP. The bars correspond to the 95% confidence interval of the slope coefficient. GDP per capita is in constant 2010 USD (Source: World Bank WDI).

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2020) observe null effects on student outcomes. Reviewing studies from 20 predominantly middle-income countries, Anand et al. (2023) finds an average impact on learning of 0.04 S.D.. However, there is a lack of experimental evidence on the impact of interventions to improve school leadership skills in low-income countries, particularly in Sub-Saharan Africa. Three studies included in Anand et al. (2023) are conducted in low-income countries. However, two of these do not report impacts on student learning or progression (Devries et al., 2015; Lassibille, 2016). The study which does report learning impacts, Blimpo et al. (2015), finds null effects .

There are reasonable grounds to expect such training to be effective in low-income contexts. In particular, while high-income countries typically have formalized merit-based promotions for head teachers, and middle-income countries including Colombia, Peru, Ecuador and Mexico have established similar systems in recent years (Commission for Quality Education for All, 2016), in most low-income countries, including Malawi, promotions are made on a discretionary basis and reflect seniority more than merit ; furthermore, the overwhelming majority of head teachers and deputy head teachers receive no training on entering their roles (Mulkeen, 2010). This suggests that there are substantial potential benefits for a well designed and implemented program of leadership training to raise the skills of head teachers, and in so doing, improve school outcomes. There is therefore a strong need for rigorous experimental evidence on the impact of such interventions in low-income settings.

This study aims to fill this lacuna in the literature by testing the impact on student progression and test scores of a two-year, multi-phase intervention in Malawi to strengthen leadership skills for head teachers, deputy head teachers, and sub-district education officials (known as Primary Education Advisers, PEAs). We implement an intervention specifically tailored to the school environment in Malawi. The intervention consists of two phases of classroom training along with follow-up visits, implemented over two years. It focuses on skills relating to school organization in a resource-constrained environment, particularly: making more efficient use of resources available at the school; motivating and incentivizing teachers to improve performance; and curating inclusive school cultures which meet the needs of all students including overage students, students with special needs, low-performing students, and girls. We conduct a randomized controlled trial, with 1,198 schools in the treatment group and a further 1,198 in the control group. The interventions are designed by a partnership of the Government of Malawi, University of Oxford, University of Malawi, and an international consulting organization, along with the World Bank; and implemented by the government and University of Malawi with support from the consulting organization.

For data, we employ a large-scale, nationally representative, independent survey of conditions, practices and learning outcomes in schools, administered by an independent research 2
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information it provides. It gives a clearer, more accurate picture of the trading patterns of countries and, especially of regions, than does the trade share measure. Using these two indices, we will take a look at current trading patterns and how they have evolved over the last two decades with the focus on regional/multilateral patterns and shifts.

# **Trade Measured in Shares**

> A snapshot of East Asia's average trade shares for 1998-2000 (Table 4.1), reflects both the global and intraregional nature of East Asia's trade. (See Table S.6 in appendix for separate import and export shares.) All but the smallest nations conduct a

> significant amount of trade with the EU and the U.S. (between 10 and 30 percent share of their total trade). But intraregionally, the trade shares of individual countries are even

> larger with ASEAN; in this case, it is the smaller countries that have the larger share. If

> the expanded ASEAN-Plus-Three (APT)<sup>2</sup> 1 is considered, the shares rise significantly for

> all countries, primarily because of the high level of trade with Japan (representing 10 to

> 28 percent for most countries), except for Hong Kong in which case it is trade with China that pushes up the share with APT.

4
_DRD_ . At a death rate for the poor that is 20% above the mean, the impact of differential mortality on the headcount index is about 0.04 percentage points. It rises to about 0.2 percentage points when the death rate for the poor is twice the mean. (The upper bound, when all deaths are among the poor, is 0.7 points.) These impacts may look small, but recall that they are the impacts in one year. So they should be compared with annual rates of poverty reduction. The Table 1 also gives the impacts expressed as a percentage of the annual rates of poverty reduction from 1990 to 2001. The impact at _DRD_ =1.2 represents 7% of the annual average rate of reduction in the headcount index over this period. Naturally the impacts are even higher for higher values of _DRD_ , rising to 15% for DRD=1.5 and 30% for _DRD_ =2.

Annual rates of poverty reduction naturally depend on the start date. Beginning instead with the Chen-Ravallion estimates for 1981, the annualized rate of poverty reduction for $1 a day is about one percentage point per year — about ten times the estimated impact of differential mortality in 2001 for _DRD_ =1.5. However (as Chen and Ravallion argue), the early 1980s were an unusual period, given the rapid poverty reduction achieved by China in that period (associated with its successful agrarian transition); see Figure 1. Starting the clock at 1984 instead, the annualized rate of poverty reduction in the developing world is similar to the trend since 1990. However, there has been a slowdown in the overall rate of poverty reduction (in percentage points per year, though not proportionately) since the late 1990s. If instead one focuses on the five year period from 1996 to 2001 then the trend drops to one third of a percentage point per year. Differential mortality at _DRD_ =1.2 accounts for about one eight of the trend, rising to over one half at _DRD_ =2.

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