rafmacalaba/datause-encoder-data huggingface dataset:
matter knowledge are better teachers, it does not prove that teachers are better _because_ they have more subject-matter knowledge. Based on videotaping Indonesian classrooms, Chang, Shaeffer, Al-Samarrai, Ragatz, De Ree, and Stevenson (2013), Ragatz (forthcoming) (and related work) find that teachers with higher levels of subject-matter knowledge also teach differently on average. And interestingly, it appears that the way teachers with more knowledge teach differently, relates directly to the additional knowledge. For example, Ragatz (forthcoming) argues: “techniques such as investigation, open-ended questioning, the use of mathematical language and symbols, and the use of non-routine problems and applications were found more often in classrooms with higher-knowledge teachers. This appears to be in part because the techniques themselves require a greater amount of subject mastery to be conducted effectively.” This finding lends weight to the idea that increases in the level of subject-matter knowledge among Indonesian teachers could lead to improved student outcomes. The student-teacher fixed-effects model evaluates the hypothesis empirically.

The **student-teacher fixed-effects** model attempts to disentangle the subject-matter knowledge of teachers from other, unobserved, teacher attributes, such as general intelligence, motivation, etc. The idea of the model is to only employ variation across subjects for the same student-teacher pair. Would it be, for example, that students with teachers who know more math than science, learn more math than science? The results from the student-teacher fixed-effects model confirms that subject-matter knowledge is important, even if we control for all non-subject specific student and teacher attributes. The point estimates are smaller in magnitude than in the level and school fixed-effects specifications. This is consistent with the idea that part of the reason why teachers with more knowledge are better, is because they are also more skillful along other dimensions. Perhaps naturally, the standard errors are larger for the student-teacher fixed-effects results, which, in combination with the size of the estimated effects, makes that the results are somewhat less robust overall.<sup>27</sup> The point estimates presented in this paper tend to be larger than comparable parameter estimates presented by Metzler and Woessman (2012). The point estimates we present here are closer to 0.2, while Metzler and Woessman (2012) present point estimates of around 0.1.

> 27Parameters for all other other fixed teacher and class attributes (age, education level, etc.) are not identified in the student-teacher fixed-effects model as they do not vary across subjects.

27
75 Figure A7: Event Study: Imports and the Arrival of Covid-19 <!-- Start of picture text --> −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12<br>Month relative to period when treatment first changes (t=0) Month relative to period when treatment first changes (t=0)<br>(a) Extensive Margin (b) Intensive Margin<br>−4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12<br>Month relative to period when treatment first changes (t=0) Month relative to period when treatment first changes (t=0)<br>(c) Extensive Margin, fertilizer only (d) Intensive Margin, fertilizer only<br>6<br>.4<br>.3<br>.2 4<br>.1<br>0 2<br>−.1<br>Effect on Pr(imports>0) −.2 Effect on log(imports) 0<br>−.3<br>−.4 −2<br>.8<br>8<br>.6 7<br>6<br>.4 5<br>4<br>.2 3<br>2<br>Effect on Pr(imports>0) 0 Effect on log(imports) 1<br>0<br>−.2 −1<br>−2<br><!-- End of picture text --> _Notes_ : The charts show De Chaisemartin and d’Haultfoeuille (2022) estimates of the differential effects of the Covid-19 pandemic on both the extensive and intensive margins of fertilizer imports, relative to the last month before the start of Covid-19, using an HS8 product-month level panel. The sample covers the January 2019-April 2021 period (but only coefficients in November 2019-March 2021 are reported) and excludes non-fertilizer HS8 products whose imports were ever banned. In panels A7c and A7d, the sample also excludes all non-fertilizer products, even the ones who were never banned. The treatment variable is a dummy _covidct_ which equals one if period _t_ is later than February 2020 **and** product _c_ was banned in some period starting in May 2021. Never-banned products serve as the control group. The y-axis shows the differential effects of Covid-19 on the probability of nonzero imports (panels A7a and A7c) or on the log value of imports (panels A7b and A7d), where negative values correspond to decreases in probabilities/values. The p-values of the joint significance tests for the placebo estimators are 0.63 (panel A7a), 0.27 (panel A7b), 0.47 (panel A7c), and 0.36 (panel A7d).
reported by de Rezende et al (2012). All studies were from OECD countries. More recently, a systematic review conducted by Serra-Burriel et al (2020) of multi-drug resistant HAIs reported 20 studies (19 OECD countries and China) that estimated the additional costs of AMR. Across the 20 studies identified by Serra-Burriel et al, the average increase in cost was 33%. This increase is a combination of increase in intensity of treatment (e.g., different drugs and more laboratory tests) and increase in hospital length of stay. The former accounted for 6% of the increased cost and the latter 27% of the increased cost. However, none of these studies were from Africa.

Epeh and Fenny (2022) report a review of HAI medical costs in Africa, finding 13 publications from 10 African countries. There was significant heterogeneity across the studies in the additional cost and additional length of stay due to HAI. One study from DRC that was reviewed by Epeh and Fenny (2022) – estimating a cost per HAI of US$10.2 (Bukasa et al, 2018) – was given a low-quality rating based on the statistical methods used.

Due to lack of published data available on the costs of treating HAIs, it is necessary to construct the cost based on available cost datasets on cost per hospital bed day from studies conducted by IHME (2014, 2015). This is shown in Table 7, and is done by adjusting unit costs in local currency units in the year of cost data (2010) to the year of this present study (2022) using the GDP deflator, and then applying the PPP exchange rate conversion for 2022 to convert to US dollars in the year 2022.

**Table 7. Cost per bed day in US$ in 2022**

|**Country**|**Health center**|**District hospital**|**Higher hospital**|
|---|---|---|---|
|Angola|49.5|48.1|183.8|
|Congo,<br>Dem.<br>Rep.|7.0|11.1|20.7|
|Ethiopia|11.2|17.8|33.1|
|Kenya|55.6|54.1|206.4|
|Madagascar|6.1|9.6|17.9|
|Mozambique|6.0|9.4|17.6|
|Sudan|9.1|14.4|26.9|
|Tanzania|13.4|21.1|39.4|
|Uganda|10.7|17.0|31.7|

Source: IHME (2014) for DRC, Ethiopia, Madagascar, Mozambique, Sudan and Tanzania; and IHME (2015) for Kenya and Angola.

The estimates in Table 7 are compared with available cost studies from the nine countries. Aerts et al (2022) estimate the costs of neonatal admission for acute bacterial sepsis in Mozambique and South Africa. In Mozambique, the mean hospitalization cost per neonate was I$308 or US$114, and the median cost per neonate was I$275 or US$101.7. Based on the length of stay (mean 16 days, median 15 days), the cost per day was I$19.25 or US$7.1 (mean) and I$18.33 or US$6.8 (median). These unit costs are similar to some countries estimates for health center unit costs in Table 7. Food and accommodation at the hospital are provided to caregivers without charge, so the main direct costs were transportation expenses, which was I$9.58 in Mozambique. The study from DRC had a cost per HAI of US$10.8 (Bukasa et al, 2018).

33
Figure **..** OPENNESS AND GROWTH: PARTIAL CORRELATIONS FOR A SAMPLE OF COUNTRIES, 1960-87 <!-- Start of picture text --> pROolaTIVITV GOWCTH G.S PROMCTIVITV GRDOWTH<br>0.0J 0. ,<br>0 02 , *.'' 5<br>0.0= 0.1<br>o00 . .. ~~~ 0.0<br>-002 .,'. -0.1 _ _<br>-0.02 -0. 0<br>-0.03 - I0NDEX OF TRADE LISERALiEATWU<br>INDEX OF TRADE LIEERAI.IZAIIOIi(OEE<br>PRODUCTIVITI GowTH 0 06 PROOUCTIVITV GROWTH<br>0 0606<br>004 004<br>002 002<br>0 0~~~~~~~~~~~~~~~~~~~~~~~~~~.<br>0 . 0 0 .<br>.~~~~~~~~~~~~~~~~~~~I.<br>-006 00<br>-0 o 06 4 02 0 02 04 0o6<br>BLACK MARKET oaEUIUU , CHANGES N TRADE SHARES<br>0.06k PRCOCCTIVITV GROWTH 6% PROOUCTIVITY GROWTH<br>0 04 .', %"<br>0 00'% 02 . ', ' ,2* ;.<br>002 " '. . ' 2% '<br>.00- . .<br>-006 .6%<br>MOVEMENT TOMAR03 INTERNATIONAL POICES PAICE DISTORTION INoeX<br>PRODUCTIvITV GROWTH WoteCO Tily of the sA .aewe of op_eas de a iUL@mZ at<br>the 3 X Levl in a regreslon of CDP geowth oa eP_e, Lopa<br>004 growth (capital, Labor, education. land) and Odl_tZY d_W<br>vagiebLes. The stinn tvo, the price dLatortios Lades ad<br>the movement toward intecnatLoaal prScoe, aiS nificant At<br>0 02 of the 10 the partial X Leve. comLation %Iwae scatter between pLats GOP present ggowth *1swal a and Op_nes, pictue<br>: ~~~~~~~~fter accountin for otba x feters. Counry d_*"o ere<br>o z_ Liberaligation L cluded in all *ad regressions. hh uses anuaL vlues, ALL values (0(e t the 197-e) tr<br>. ,'avraes for 1960-60, 67-73. 74-81. and 82-O8. The mb of<br>countrtie rauses from 60 (for trad& shams) to t9 (for tie<br>-0.02 LLbSrILgat*u La., 1900-U4).<br>6Sue For black cket premiu. Pick's Ovm7 Yearbooks &ade<br>-0.04 of trade LberaLstlsou 1940.44 * e , M ,aie Niabs<br>(199) I LaI.s of sErd 1i)ermLis 197846. fer 3a3.wiIThin<br>(1091)s bLas acda sexirni,. Kiam. l$GM"* a" TAU , 19<br>-0.06 I_____________________ *oaes Lo cgs". eks.. and - em towardse lainssums I pwit.s<br>K4A I *IA8 ^ aAGAINST zst 4aeut ut AORICULTURU ,_ 34Ao World Dellar Bank. rhu puceo dlavewtiou Index (191* Ls a mdwif J fogwser of<br>_ ~~~~34<br><!-- End of picture text -->
# **_Section 1: Introduction_**

Decentralization creates new institutions. These institutions, in turn, create new political positions, provide new avenues to accumulate political power and promote the development of new local elites. By design decentralization introduces newly elected elites into preexisting systems of local governance, leading to potential conflicts between new and old local authorities. We argue that potential power-sharing tensions between old and new elites have implications for the success of decentralization policies; an insight thus far insufficiently explored in scholarly and policy debates. Using the case of Sierra Leone, this paper attempts to fill this lacuna by examining how the process of decentralization shapes power relations among different sets of local actors who are both attempting to fill the same governance space, and how the dynamics of these relationships affect the provision of local public goods.

Guided by international organizations, Sierra Leone initiated a protracted process of decentralization in 2000, culminating in the passage of the Local Government Act (LGA) in 2004. The international community supported the devolution of public goods provision to local governments in Sierra Leone believing that it would empower citizens, strengthen local government vis-à-vis the central government, and increase oversight (Sacks and Larizza 2012). Further, international support for decentralization was spurred by the belief that the centralized nature of the Sierra Leonean government was a driving factor in instigating Sierra Leone’s decade long civil war (Jackson 2005).

One of the central components of the LGA was to devolve the majority of central government responsibilities to newly created local councils. This reduced the customary legal responsibilities of hereditary Paramount Chiefs, and challenged the _de jure_ influence of traditional leaders as the primary source of local public authority. In this paper, we investigate how the subsequent types of relationships that have formed between local councilors and Paramount Chiefs have affected local development. Using data from Sierra Leone’s Decentralization Stakeholders Survey (DSS), which surveys all local councilors in the country, and the household level National Public Surveys (NPS), we find that closer self-reported relationships between chiefs and councilors are associated with a decrease in the use of local public health and education facilities as well as worsening perceptions about the quality of local public goods. In contrast, we find that a greater degree of competition between the two groups, defined as a greater number of self-reported inter-elite disputes, is associated with improved 2
# **Figure 2B. Quantile Regression Estimates of the Differences between Countries Depending on the Total Number of Weeks of Full and Partial School Closures (UNESCO Data), All ECA+ Countries**

<!-- Start of picture text --> 0<br>-5<br>-10<br>-15<br>-20<br>-25<br>-30<br>1 2 3 4 5 6 7 8 9<br>Reading achievement decile<br>10 weeks closure 25 weeks closure 50 weeks closure<br>Departure from achievement trend<br><!-- End of picture text --> How do these estimates compare to previous national estimates of learning loss? There are robust estimates of learning loss from 18 ECA countries published prior to PIRLS between 2020 and 2023 (Patrinos 2023). Learning losses range from 0 in Sweden (no school closures) to 0.37 SD in Turkey (40 weeks of closures, the longest in our sample). The average learning loss is 0.17 SDs for those studies; very close to the reading loss of 0.20 SDs recorded in our analysis of PIRLS over time.

Globally, using the same PIRLS data but for all countries – 55 countries and regions, representing more than 16 million 4<sup>th</sup> graders worldwide – the losses are estimated at 0.33 SDs, or just over a year’s worth of learning (Jakubowski et al. 2023). Again, losses are larger for students in schools that faced relatively longer closures and for lower-achieving students. The economic losses may translate into a 0.68 percentage point reduction of GDP growth totaling a global loss of more than $80 trillion.

# **Summary and conclusions**

We estimate the impact of COVID-19 on student reading on standardized tests over time for EU and ECA+ countries. We model the effect of closures on achievement by predicting the deviation of the most recent results from a linear trend in reading achievement using data from all rounds. Scores declined from 2016 to 2021 by an average of 20 percent of a standard deviation, just less than a year’s worth of schooling. This validates the scenarios projected by the World Bank (Azevedo et al. 2021) early in the pandemic. Losses are significantly larger for students in schools that faced relatively longer closures. While there are no significant differences by sex, it is shown that lower-achieving students experienced much larger losses.

13
# **Find the Fake: Boosting Resistance to Misinformation in Jordan with a WhatsApp Chatbot**

# **Game**

With the increasing use of social media, misinformation has proliferated and traveled farther, wider, and faster than ever before, impacting wide-ranging topics including climate change, politics, and vaccines (Vosoughi et al., 2018). Accordingly, misinformation and disinformation are considered among the 32 most severe global risks<sup>1</sup> (World Economic Forum [WEF], 2023), with direct costs estimated at about US$ 78 billion across different countries and sectors (Cavazos, 2019). During the coronavirus disease (COVID-19) pandemic, misinformation emerged as a particularly salient threat to public health given its association with vaccine hesitancy (Loomba et al., 2021; Roozenbeek et al., 2020; Zhao et al., 2023).

To tackle widespread misinformation, researchers have developed several psychological solutions: fact-checks or corrections of specific misinformation claims (i.e., debunking), priming to be cautious of accuracy (i.e., accuracy primes or nudges), and psychological inoculation against misinformation techniques (i.e., prebunking) (American Psychological Association, 2023 [APA]; Lim et al., 2022; Roozenbeek & van der Linden, 2022; Zhao et al., 2023). Evidence suggests that all these solutions can be effective at reducing the spread of misinformation (Lim et al., 2022; Pennycook et al., 2023; Zhao et al., 2023), but teaching common misinformation techniques as a form of inoculation holds the advantage of being both proactive and effective in combatting misinformation. In contrast, although debunking showed better non-misinformation credibility assessment and sharing intentions than prebunking in some research (Lu et al., 2023), debunking requires significant resources to detect and respond to specific claims and there

> 1 According to World Economic Forum (2023), “Global risk” is defined as the possibility of the occurrence of an event or condition which, if it occurs, would negatively impact a significant proportion of global GDP, population or natural resources.

3
**Figure 4:** Major obstacles in the Republic of Congo (percent of companies surveyed)

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> _Source:_ World Bank Enterprise Surveys website custom query and World Bank (2009) Growth remains constrained by the underdeveloped physical infrastructure. A cross-country statistical analysis conducted by the World Bank (World Bank 2010b) shows that the contribution of the infrastructure sector to Congo’s per capita growth over the past decade, at 0.5 percentage points, was considerably lower than in other countries in the region: 0.87 for Nigeria, 0.91 percentage points for DRC, 0.89 percentage points for CAR, and 0.99 for the SSA average. The growth in Congo stemming from infrastructure was due mainly to increased access to mobile telephony while the poor quality and inadequacy of power supply acted as a brake on the economy.

Given this, infrastructure improvements can contribute substantially to growth. Infrastructure has the potential to contribute more than 3 percentage points a year to per capita GDP growth in the future, if access to and quality of infrastructure services were improved to the level seen in Mauritius, the leading African country in infrastructure quantity and quality (World Bank 2010b). This compares with an average of 2.3 percentage points for the whole Africa continent. Upgrading Congo’s infrastructure, in particular in the power sector, is a prerequisite for economic development and the provision of social services.

# 2.3 <u>Government investment and efficiency in the Republic of Congo</u>

As a response to the challenges faced by the country, the Congolese government has embarked on an ambitious investment program to improve access to and delivery of basic services. The authorities have, hence, scaled up investment spending substantially to improve the dilapidated infrastructure, particularly in the power and transport sectors. The investment budget is estimated to reach CFAF 981 billion (approximately USD 2.1 billion) or 14.0 percent of GDP in 2011, representing an increase of almost 67 percent compared to the investment budget of CFAF 589 billion (approximately USD 1.2 billion) or 10.1 percent of GDP in 2010 (Table 3). In comparison, private investment, especially in the non-oil sector has remained limited, averaging less than 5 percent of GDP in the last 10 years.

5
# **Voter Response to Natural Disaster Aid: Quasi-Experimental Evidence from Drought Relief Payments in Mexico**

## Alan Fuchs

and Lourdes Rodriguez-Chamussy<sup>1</sup> Keywords: Disaster Spending, Voting, Regression Discontinuity, Political Accountability. JEL Codes: D72, H84, I38, O38

> 1 Fuchs: World Bank (Email: afuchs@worldbank.org); Rodriguez-Chamussy: Inter-American Development Bank (Email: mlrodriguez@iadb.org)
|Per capita consumption|-0.001***<br>(0.001)|-0.001***<br>(0.001)|-0.001***<br>(0.001)|-0.001***<br>(0.001)|(0.201)|(0.016)|||
|---|---|---|---|---|---|---|---|---|
|Household share males|0.240<br>(0.281)|-0.862***<br>(0.334)|-0.040***<br>(0.012)|-0.727***<br>(0.280)|-1.575***<br>(0.126)|-0.221***<br>(0.016)|-1.381***<br>(0.235)|<br>-0.080***<br>(0.012)|
|Household share|-0.982**|0.975***|0.029*|0.941***|-1.130***|-0.108***|-0.126|-0.020|
|females|(0.473)|(0.214)|(0.015)|(0.313)|(0.208)|(0.017)|(0.290)|(0.014)|
|Xuhat||-1.136***<br>(0.316)|||-0.363***<br>(0.123)||-0.564**<br>(0.221)||
|Constant|-1.074**|-2.148**|0.110***|-1.501**|-1.566***|0.104***|-0.496|0.101***|
||(0.423)|(0.844)|(0.030)|(0.692)|(0.341)|(0.041)|(0.508)|(0.031)|
|Marginal effect of IBC|0.001***<br>(0.001)||||||||
|Marginal effect of IDH||0.082***<br>(0.014)||0.015***<br>(0.004)|0.068***<br>(0.020)||0.071***<br>(0.017)||
|Observations|23,983|23,983|23,983|23,983|23,983|23,983|23,983|23,983|
|R-squared|0.171|0.126|0.072||0.202|0.181|0.221|0.116|
|McFadden R2||||0.186|||||
|LR chi2||||2015***|||||
|Degrees of freedom||||29|||||
|Log-likelihood||||-4416.60|||||
|Governorate of<br>origin*governorate<br>of residence FE||Yes|Yes|Yes|Yes|Yes|Yes|Yes|

26
Meanwhile, MMs represent the mean outcome across all appearances of a particular feature, averaging across all other features. They are the differences in the outcome of interest caused by the presence of a specific attribute, all other attributes being equal. We report MMs to reduce concerns about how researchers’ choices of reference categories might impact the substantive interpretation of the results—particularly among subgroups (Leeper et al. 2020; Ratkovic 2021).

Second, we estimate conditional marginal means (CMMs) to measure subgroup preferences for the policy options. We assessed the extent to which respondents hold either economic or humanitarian priorities in two ways. On the one hand, prior to the experimental treatment we measured respondents’ levels of agreement with five statements that covered a range of economic and humanitarian stances: for example, _“protecting the most vulnerable should be given priority even if that reduces economic growth.”_ This was measured on a scale from 1 indicating “strongly disagree” to 5 indicating “strongly agree.” The full battery of statements appears in <u>Table A2</u> and the corresponding summary statistics in Figures <u>A3</u> and <u>A4. We randomised the order of these statements by respondent. Then, after recoding</u> responses so that higher values indicated humanitarian priorities, we assigned respondents whose mean response across the five statements was greater than 3 as holding more humanitarian priorities. By contrast, we assigned respondents whose mean response were below 3 as holding more economic priorities. On the other hand, we measured respondents’ views as to whether the situation in Venezuela was primarily an economic or humanitarian problem using a single question (see Table A2 Appendix).

Third, we also consider the extent to which social contact with Venezuelans matters for Colombians’ policy preferences. Using a series of three questions measuring self-reported _meaningful_ social contact—rather than chance or superficial exposure to a large group—we created an index that allowed us to code respondents as having either “high” or “low” levels contact (Clayton et al. 2021).<sup>13</sup> The questions asked about daily interactions, trust in individuals, and social interactions (i.e., gathering for dinner). The precise questions and construction of the index are reported in the Appendix in Table A2 and the summary statistics in Figure A5. The order of these questions was also randomised across respondents.

> 13 To test whether our results with respect to contact are sensitive to the type of contact in question, we repeated the analysis using a more conventional measure of migrant density. This measure involved calculating the proportion of migrants as a share of the total population of each state (department). Then, using respondents’ reported location at the state-level, we split the sample into two groups: those living in states whose proportion of migrants was below the median of all states (coded as 0), and those living in states with migrant proportions above the median (coded as 1).

16