rafmacalaba/datause-encoder-data huggingface dataset:
TABLE 10: TRAINER CHARACTERISTICS ON STUDENT PSYCHOLOGY (SELF EFFICACY) _y = Self Efficacy score of students after training_

|Trainer Characteristics|
|---|

|||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|---|
|||Self<br>Efficacy<br>(1-5)|Self Efficacy<br>(1-5)|Self Efficacy<br>(1-5)|Self<br>Efficacy<br>(1-5)|Self<br>Efficacy<br>(1-5)|
|**hics**|Age|-0.00760<br>(0.00593)|0.000388<br>(0.00512)|0.00504<br>(0.0134)|-0.00435<br>(0.0158)|-0.0210<br>(0.0240)|
|**rap**|Female|0.0744|-0.000104|0.0967|-0.0193|0.356|
|**mog**||(0.129)|(0.111)|(0.227)|(0.238)|(0.463)|
|**De**|Years at school|0.0101|0.0359|-0.0000659|0.0270|0.0656|
|||(0.0222)|(0.0232)|(0.0329)|(0.0283)|(0.0404)|
|**preneur**<br>**Model**|Ever been a business||0.405***|0.330***|0.366***|0.374**|
|**Entre**<br>**Role**|owner||(0.0975)|(0.119)|(0.0829)|(0.150)|
|**e skills**|Digitspan score|||0.0217<br>(0.0480)|-0.0172<br>(0.0571)|-0.0964<br>(0.0861)|
|**itiv**|Raven test score|||-0.0280|0.0175|-0.0328|
|**ogn**||||(0.0261)|(0.0297)|(0.0654)|
|**n-c**|Personal intitative|||-0.334|-0.731***|-0.252|
|**no**||||(0.277)|(0.187)|(0.495)|
|**and**|Error competence|||0.178|0.722***|0.916**|
|**ve**||||(0.218)|(0.218)|(0.340)|
|**Cogniti**|Prosocial orientation|||0.381<br>(0.257)|0.0263<br>(0.221)|0.0754<br>(0.565)|
||How satisfied are you||||0.189**|0.106|
|**tion**|with your job?||||(0.0725)|(0.129)|
|**atisfac**|Given a choice, would<br>still be a teacher||||-0.150***<br>(0.0303)|-0.130***<br>(0.0417)|
|**b s**|Career satisfaction||||0.180***|0.127*|
|**Jo**|||||(0.0507)|(0.0639)|
|**VET**|Years as teacher|||||0.0389<br>(0.0421)|
|**at T**<br>**llege**|Years at TVET Colleges|||||-0.0114<br>|
|**ars**<br>**co**||||||(0.0322)|
|**Ye**|Perceived|||||-0.115|
||entrepreneurial skills|||||(0.164)|
||Average score for|4.215|4.215|4.215|4.215|4.215|
||students at baseline|(0.0264)|(0.0264)|(0.0264)|(0.0264)|(0.0264)|
||Observations|559|559|559|559|559|

# _Notes:_

* significant at 10% level ** significant at 5% level *** significant at 1% level

> (1) OLS regressions sequentially add explanatory variables from columns (1) to (5)
The three channels of impact of school leadership outlined above – resource utilization, teacher behavior, and school cultures – are intrinsically connected. For example, school leaders’ ability to control the allocation of resources may extend to teachers, the largest single area of expenditure in most schools – the selection and management of whom is a key driver of school culture.

# 2.3 Context

Malawi has made impressive achievements in access to schooling since the introduction of free primary education in 1994, but its education system has failed to keep pace with increased enrolments, resulting in overcrowded classrooms and understaffed schools.<sup>4</sup> These extremely poor conditions lead to high rates of grade repetition<sup>5</sup> and dropout. In 2017/18, at the time of the baseline for this study, repetition rates nationwide averaged 32 percent in Grade 1 and 24 percent in all grades.<sup>6</sup> (Ministry of Education, Science and Technology of Malawi (MoEST), 2018) Only 59 percent of students enrolling in Grade 1 remain in school to Grade 5 (ibid.). Only one in three students who enter primary education complete all eight grades (Ravishankar et al., 2016).

Even students who remain in primary school experience poor learning outcomes. At Grade 2 level, fewer than 25 percent of students achieve minimum proficiency levels in the Early Grade Reading Assessment (EGRA) conducted by the United Stages Agency for International Development (USAID). At Grade 6, fewer than 25 percent of students achieve minimum proficiency levels in the Southern African Consortium for Monitoring Educational Quality (SACMEQ) assessment in mathematics, placing Malawi near the bottom in the region. Learning outcomes also vary widely between students, even within a single class: in 86 percent of schools, according to the baseline for this study (MLSS, 2018) the gap in knowledge scores was at least one S.D., 115 points. This is equivalent to more than two years of schooling, effectively creating multi-grade classrooms. Scores are lower for girls, over-age students, and students from minority language groups (ibid.).<sup>7</sup> Malawi’s challenges are not unique: analyses of school systems in Sub-Saharan Africa have identified a set of challenges common to countries that have rapidly expanded their primary

> 4 The average class in Grade 1 has about 150 students; and in Grade 2, about 125 students (MLSS, 2018).

> 5 Most Malawian schools require students to repeat grades if they fail to pass an end-of-year examination.

> 6 Grades are known as standards in Malawi, but will be referenced as grades throughout this paper. Grades range from Grade 1-12. Primary Education is from Grade 1-8, and Secondary Education from Grade 9-12.

> 7 Percentage scores in mathematics, Chichewa, and English adjusted using Item Response Theory and converted to knowledge score mean-centered at 500 points with an S.D. of 100 points. See section 2.7 for details.

7
Figure A1: Survey period in context–contd.

<!-- Start of picture text --> LKA SDN<br>100 100<br>0<br>0<br>80 80<br>-20<br>-20<br>60 60<br>-40<br>-40<br>-60 40 40<br>-60<br>-80 20 -80 20<br>-100 0 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>TGO TUN<br>0 80 100<br>-20 0 80<br>60<br>-20<br>-40 60<br>40 -40<br>-60 40<br>-60<br>20<br>-80 20<br>-80<br>-100 0 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>TUR TZA<br>80 50<br>0<br>0<br>40<br>-20<br>-20<br>60<br>-40 30<br>-40<br>-60 40 -60 20<br>-80 -80 10<br>-100 20 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>VNM ZMB<br>0 100 0 70<br>-20 90 -20 60<br>-40 80 -40 50<br>-60 70 -60 40<br>60 30<br>-80 -80<br>50 20<br>-100 -100<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>Stringency of<br>Mobility around<br>Period of interview lockdown<br>transit stations<br>restrictions<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br><!-- End of picture text --> Data from Google (2020) for the mobility trends and Hale et al. (2020) for the lockdown restrictions. The mobility trends are smoothed using 7-day moving averages.

40
|(6)<br>0.00964***<br>(0.00216)<br>0.229<br>82,572<br>0.00213<br>(0.00150)<br>0.229<br>82,572<br>0.0204***<br>(0.00472)<br>82,572<br>**)**<br>0.473***<br>(0.0233)<br>412.22<br>82,572<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>by illegal armed groups<br>h, education, and other<br>riculture, services, and<br>ntheses. _∗∗∗_signifcant|
|---|
|Exit<br>(5)<br>0.0123***<br>(0.00202)<br>0.232<br>122,181<br>0.00212<br>(0.00180)<br>0.232<br>122,187<br>0.0402***<br>(0.00766)<br>122,181<br>**Working Age**<br>0.306***<br>(0.0191)<br>257.17<br>122,181<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>) armed attacks<br>transfers to healt<br>GDP share in ag<br>reported in pare|
|(4)<br>0.00961***<br>(0.00133)<br>0.225<br>122,211<br>0.00319***<br>(0.00120)<br>0.224<br>122,217<br>0.0257***<br>(0.00377)<br>122,211<br>**P (% Pop in**<br>0.374***<br>(0.0150)<br>626.99<br>122,211<br>Yes<br>Yes<br>No<br>No<br>No<br>No<br>ar dummies and i<br>tral government<br>1995, viii) 2000<br>municipality are|
|(3)<br>-0.00399**<br>(0.00197)<br>0.608<br>82,572<br>-0.00312**<br>(0.00130)<br>0.608<br>82,572<br>-0.00845**<br>(0.00417)<br>82,572<br>**e: Share of ID**<br>0.473***<br>(0.0233)<br>412.22<br>82,572<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>interactions of ye<br>res in 1995, v) cen<br>lection offces in<br>standard errors by|
|**Dependent Variables (in logs)**<br>Entry<br>(1)<br>(2)<br>**Panel A. Reduced Form**<br>Predicted Infows<br>-0.00912***<br>-0.0111***<br>(0.00131)<br>(0.00197)<br>R-squared<br>0.602<br>0.611<br>Observations<br>122,211<br>122,181<br>**Panel B. OLS**<br>Share of IDP (%Pop in Working Age)<br>-0.00865***<br>-0.00359**<br>(0.00111)<br>(0.00161)<br>R-squared<br>0.602<br>0.610<br>Observations<br>122,217<br>122,187<br>**Panel C. 2SLS**<br>Share of IDPs (%Pop in Working Age)<br>-0.0244***<br>-0.0362***<br>(0.00361)<br>(0.00708)<br>Observations<br>122,211<br>122,181<br>**Panel D. First Stage**<br>**Dep. Variabl**<br>Predicted Infows<br>0.374***<br>0.306***<br>(0.0150)<br>(0.0191)<br>First Stage F-statistic<br>626.99<br>257.17<br>Observations<br>122,211<br>122,181<br>**Controls for all Panels**<br>Year FE<br>Yes<br>Yes<br>Firm FE<br>Yes<br>Yes<br>Year_×_Department FE<br>No<br>Yes<br>Homicides Rates<br>No<br>Yes<br>Confict Controls<br>No<br>Yes<br>Additional Controls<br>No<br>No<br>_Notes:_ Each coeffcient corresponds to a separate regression. Additional controls include<br>in 1995, ii) fatal victims of attacks in 1995, iii) homicide rates 1995, iv) public expenditu<br>expenditures in 1995, vi) number of fnancial institutions in 1995, vii) number of tax col<br>industry, ix) municipal tax income in 1995, and x) night light density in 1995. Clustered<br>at the 1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%.|

33
5 possible drivers of the broader drop in infant mortality. We focus on Kenya because the drop in infant mortality was particularly large in Kenya and because available data on the geographic variation of malaria prevalence make it possible to take into account the level of malaria risk in the analysis.

# **3 The Infant Mortality Decline in Kenya and Possible Drivers**

Methods of calculating child and infant mortality can be classified into ―direct‖ methods and ―indirect‖ methods. Direct methods of calculation use data on the date of birth of children, their survival status, and the dates of death or ages at death of deceased children. Indirect methods require less data—number of children ever born and number living, along with age of the mother—but demand much stronger assumptions. There are three principal types of direct methods, which are described as follows in the _Guide to DHS Statistics_ by Rutstein and Rojas (2006):

1. A vital statistics approach in which the numbers of deaths to children under age 12 months in a particular period are divided by the numbers of births in the same period....

2. A true cohort life table approach in which deaths to children under age 12 months of a specific cohort of births are divided by the number of births in that cohort. This procedure gives true probabilities of death, but has the drawback that all children in the cohort must have been born at least 12 months before the survey to be fully exposed to mortality, thus not taking into account the most recent experience. This requirement of full exposure becomes more limiting the higher the age segment of interest: For under-five mortality rates, only the information on children born five or more years before the survey can be utilized....

3. A synthetic cohort life table approach in which mortality probabilities for small age segments based on real cohort mortality experience are combined into the more common age segments.

The tabulations found in DHS reports use the third approach, which is appropriate for producing aggregate mortality rates and maximizing the use of available data. Using this approach, DHS publications present mortality rates calculated for the five years previous to the particular survey in question.
directly in terrorist activities. So understanding how extremists stand out vis a vis others is an important step in the quest for developing policies that can effectively deal with this issue.

**Figure 1: Worldwide number of terrorist attacks and related deaths**

<!-- Start of picture text --> 35000 35000<br>30000 30000<br>25000 25000<br>20000 20000<br>15000 15000<br>10000 10000<br>5000<br>2006 2008 2010 2012 2014<br>year<br>Attacks Deaths<br>Number of terrorist attacks<br>Number of fatalities due to terrorist attacks<br><!-- End of picture text --> Source: Statistica (http://www.statista.com/statistics/202864/number-of-terrorist-attacks-worldwide/).

To study the individual-level determinants of extremism, we employ the World Gallup Poll data set. It provides information that allows us to identify the full spectrum of views on terrorist activities of individuals residing in 27 developing countries. The question on attitudes toward violent extremism is asked in only a small number of OECD countries.<sup>4</sup> For this reason, the paper focuses on attitudes regarding attacks against civilians in the developing world. This focus is also warranted because there are substantial differences between the economic and social conditions in the developed and developing countries. Employing a variety of estimation techniques, we find robust econometric evidence that the average radicalized individual in the developing world is

> 4 The question on attitudes toward extremism is asked in the following OECD countries: Chile, Estonia, France, Germany, Turkey, and the UK.

3
relationship between the WTP to avoid power outages and GDP per capita, but the WTP is consistently much larger than electricity tariffs (Figure 4).

In addition to the small number of studies, the wide range of results might come from fundamental differences in experience design and different frequency and duration of considered outages. Most of the papers – listed in Appendix (Table 4) – are applied to advanced economies where dependency on electricity is high and power outages are infrequent. In poorer countries, the cost of electricity outages will fall on those with access and relatively high dependence on electricity – for example those whose livelihood depends on it (Pasha & Saleem, 2013).

**Figure 4: Distribution of willingness to pay for avoiding power outages**

<!-- Start of picture text --> 1200<br>US, 1988<br>1000<br>800<br>600<br>Canada, 1989<br>400 Italy, 2005<br>Spain, 2013 Austria, 2013<br>200 Pakistan, 2004<br>US, 2004<br>Mexico, 1999<br>Turkey, 2016 Sweden, 2008<br>0<br>0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000<br>GDP per capita, ppp (USD)<br>Choice Experiment Macroeconomics Contingent valuation<br>(% of hourly GDP per capita)<br>Willingness to pay per hour of electricity unserved<br><!-- End of picture text --> _Source: authors based on World Bank data and estimates from the literature. A country-year is matched to the closest nonmissing value of GDP per capita._ The global cost of power outages for households cannot be accurately estimated. Assessments fall between 0.002 and 0.15% of GDP (in purchasing power parity) per year, which corresponds to a range of 2.3 to 190 billion in 2018 USD<sup>4</sup> . While a precise estimate of the cost of power outages on households is impossible, we propose here an assessment of its order of magnitude. From Table 4 (Appendix), we calculate a range for the willingness to pay per hour unserved in terms of daily GDP<sup>5</sup> . After excluding the

> 4 Here we use the World Bank 2018 estimate for global GDP, ppp, retrieved from https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.CD

5 We do not make any distinction between weekdays and weekends, and between seasons: what boils down to assuming that outages occur at random, independently from the time of the week and the time of the year. The range of WTP can be interpreted as an average WTP over days and season.

9
Figure A1: Distribution of the propensity score <!-- Start of picture text --> 0 .2 .4 .6 .8<br>Propensity Score<br>Untreated Treated: On support<br>Treated: Off support<br><!-- End of picture text -->

# **B Annual Performance Assessment (APA) data and the time path of improvements in project LGAs**

We use information from the Annual Performance Assessment (APA) of the 18 local governments receiving the Urban Local Government Strengthening Program (ULGSP), compiled in the Quality Assurance Reviews (QAR).

We explore two main indicators available throughout the period. Disbursement Linked Indicator (DLI) 2 refers to whether “ULGAs have strengthened institutional performance as scored in the annual performance assessment” and is comprised of five sub-indicators (as described in Section 2 of the paper): Improved urban planning system; increased revenues from property taxes; efficient fiduciary system; improved infrastructure, implementation and O&M; and strengthened accountability and oversight systems. DLI 3 refers to whether "Local infrastructure targets as set out in the annual action plans are met by ULGAs using program funds." In Figure B1 below we present the mean score for four of the sub-components of DLI2 - we exclude the sub-component on efficient fiduciary system since maximum scores changed over time. As referenced in the timeline presented above, our first round survey was implemented around the same time of the 4th APA (early 2016), and the follow up around the 6th APA (early 2018). The evolution of mean scores is uneven over time. With the exception of the accountability index, that shows a remarkable increase between the 1st and 2nd APAs and then flattens out, the other sub-components are broadly stable over time, with ups and downs and no indication that all improvement happened in the very beginning of the ULGSP program.<sup>29</sup>

> 29We also present the overall changes in APA subcomponents in Table B1

55
**Figure 39: U.S. Household Disposable Income and Net Worth**

<!-- Start of picture text --> 70 700%<br>60 600%<br>50 500%<br>40 400%<br>30 300%<br>20 200%<br>10 100%<br>0 0%<br>Disposable personal income<br>Net Worth of Households and Non-profit Organizations<br>Household net worth as percentage of disposable personal income (right axis)<br>US$, thousands<br><!-- End of picture text --> Source: Federal Reserve, Flow of Funds Account.

60. Greenspan and Kennedy (2008) show that the increase in housing prices contributed substantially to the consumption boom, primarily via home equity loans and mortgage refinancing cash-outs. Empirical evidence for the link between housing wealth and consumption is found by Bostic et al. (2006), which uses micro-data to estimate the elasticity of consumption to both housing and financial wealth in the United States over the period 1989-2001. They find a substantially larger marginal propensity to consume from housing wealth than from financial wealth. In addition, Case et al. (2005), using panel data for 14 countries, report: ―The importance of housing market wealth and financial wealth in affecting consumption is an empirical matter....[] We find at best weak evidence of a stock market wealth effect. However, we do find strong evidence that variations in housing market wealth have important effects upon consumption. [The results...] support the conclusion that changes in housing prices should be considered to have a larger and more important impact than changes in stock market prices in influencing household consumption in the U.S. and in other developed countries.‖ 61. Equity can be extracted from houses through three channels: (i) free cash available to home sellers, which is equal to the value of existing home sale minus mortgage debt paid off at the time of sale and closing costs; (ii) home equity loans<sup>40</sup> , which are equal to originations minus repayments resulting from other forms of equity extraction, and (iii) cash-out refinancing, which is refinance originations minus repayments of first liens resulting from refinancing. Greenspan 40 Home equity loans are a type of loan in which the borrower uses the equity in their home as collateral. A home equity loan creates a lien against the borrower's house, and reduces actual home equity.

46
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 14. Existing capital expenditure for infrastructure investment, annual average 2001-06**

<!-- Start of picture text --> 2.5 nonfragile<br>Tanzania<br>low-income<br>countries<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>ICT power transport WSS ICT power transport WSS<br>public ODA non-OECD PPI<br>% of GDP<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009.

Tanzania faced an efficiency gap of $0.5 billion per year in 2006, representing resources that could be recaptured by suitable policy and institutional reforms. There is substantial evidence that more could be achieved by making more efficient use of Tanzania’s existing resource envelope. The largest areas of inefficiency identified were underpricing of electricity, power distribution losses, under-collection of the fuel levy, and over-employment in the telecom incumbent (table 12). Since 2006 – the base year for the analysis – Tanzania has captured some US$40 million of this efficiency gap thanks to tariff adjustments and efficiency improvement in the power sector.

As of 2006, the inefficiencies of Tanzania’s water and power utilities create annual losses of $90 million, the bulk of them associated with the power sector. At that time, system losses were the primary source of inefficiency in the power sector, while non-revenue water was the main source of inefficiency in the water sector, suggesting in both cases the distribution networks are over-stretched and poorly maintained. In macroeconomic terms, the operational inefficiencies of TANESCO amount to 0.5 percent of GDP, a significant amount yet somewhat lower than in other low income countries. Since 2006, TANESCO has achieved important reductions in distribution losses bringing the cost of operational inefficiencies down to 0.2 percent of GDP, which is substantially better than the peer group. In the case of the water sector, the value of the operational inefficiencies is miniscule in GDP terms and substantially better than in the low-income peer group (figure 15).

28
scope of this paper, but we provide suggestive evidence for a decline in positive selection in Section V.

Moving down in Table 2, we find a negative income effect as expected. Security of income (male salaried employment) also reduces women’s labor force participation, and we find a particularly strong negative effect of education of the household head. The size of the effects declines over time, suggesting that women have become less responsive to income insecurity and to overall socioeconomic status of the household. Household head education effects remain, however, quantitatively large.

**<u>Table 2. Estimation Results (Average Marginal Effects)</u>**

|**Pr(Labor force)**|**1987 **|**1999**|**2004 **|**2009**|**2011**|
|---|---|---|---|---|---|
|_Own education (Ref. = I_|_lliterate):_|||||
|Literate|-0.050***|-0.063***|-0.051***|-0.030**|-0.026*|
||(0.009)|(0.011)|(0.013)|(0.014)|(0.014)|
|Primary|-0.072***|-0.074***|-0.062***|-0.025**|-0.027*|
||(0.008)|(0.011)|(0.015)|(0.012)|(0.014)|
|Middle|-0.067***|-0.084***|-0.100***|-0.060***|-0.064***|
||(0.011)|(0.009)|(0.013)|(0.013)|(0.011)|
|Secondary|0.086***|-0.020*|-0.048***|-0.056***|-0.041***|
||(0.013)|(0.010)|(0.014)|(0.014)|(0.014)|
|Graduate|0.324***|0.217***|0.144***|0.131***|0.151***|
||(0.020)|(0.018)|(0.023)|(0.019)|(0.022)|
|Log income|-0.034***|-0.017***|-0.032***|-0.028***|-0.022***|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Male salaried emp.|-0.022***|-0.032***|-0.019**|0.002|-0.005|
||(0.007)|(0.008)|(0.009)|(0.009)|(0.010)|
|_Household head educati_|<br>_on (Ref. = Illiterat_|<br>_e):_||||
|Literate|-0.067***|-0.045**|-0.040**|-0.025|0.017|
||(0.012)|(0.020)|(0.017)|(0.018)|(0.019)|
|Primary|-0.107***|-0.078***|-0.053***|-0.035**|-0.057***|
||(0.012)|(0.012)|(0.015)|(0.014)|(0.018)|
|Middle|-0.126***|-0.115***|-0.078***|-0.072***|-0.083***|
||(0.013)|(0.011)|(0.014)|(0.014)|(0.015)|
|Secondary|<br>-0.170***|<br>-0.150***|<br>-0.121***|<br>-0.095***|<br>-0.103***|
||(0.013)|(0.013)|(0.014)|(0.015)|(0.015)|
|Graduate|<br>-0.166***|<br>-0.154***|<br>-0.110***|<br>-0.084***|<br>-0.126***|
||(0.016)|(0.014)|(0.018)|(0.017)|(0.019)|
|_Social group (Ref. = Hi_|_ndu non-SCST):_|||||
|SCST|0.089***|0.054***|0.036***|0.059***|0.033***|

21