rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Predicted probabilities of civil war: Prio \ Uppsala civil war Predicted probabilities for civil war – aggravating circumstances

|||Anocracy=0|Anocracy=1|Riot in<br>past 5<br>years=0|Riot in<br>past 5<br>years=3|Riot in<br>past 5<br>years=5|
|---|---|---|---|---|---|---|
|Humphreys (2005)|No oil<br>|0.029|0.047|0.029|0.037|0.042|
|oil production per|75<sup>th</sup> percentile<br>|0.029|0.049|0.029|0.038|0.045|
|capita|95<sup>th</sup> percentile<br>|0.036|0.120|0.028|0.125|0.281|
||99<sup>th</sup> percentile|0.113|0.947|0.023|0.987|0.999|
|Ross (2009) oil and|No oil<br>|0.027|0.043|0.027|0.037|0.045|
|gas rents per capita|75<sup>th</sup> percentile<br>|0.027|0.045|0.027|0.038|0.048|
||95<sup>th</sup> percentile<br>|0.034|0.300|0.022|0.144|0.369|
||99<sup>th</sup> percentile|0.114|0.999|0.005|0.997|0.999|

# Predicted probabilities of civil war – mitigating circumstances

|||Low risk of<br>coupd’état|Medium risk of<br>coupd’état|High risk of<br>coupd’état|Military<br>regime=1|<br>Military<br>regime=0|
|---|---|---|---|---|---|---|
|Humphreys (2005)|No oil<br>|0.015|0.030|0.051|0.056|0.029|
|oil production per|75<sup>th</sup> percentile<br>|0.015|0.031|0.051|0.056|0.029|
|capita|95<sup>th</sup> percentile<br>|0.026|0.040|0.054|0.062|0.038|
||99<sup>th</sup> percentile<sup>i</sup>|0.347|0.148|0.069|0.099|0.155|
|Ross (2009) oil and|No oil<br>|0.015|0.031|0.051|||
|gas rents per capita|75<sup>th</sup> percentile<br>|0.015|0.031|0.051|||
||95<sup>th</sup> percentile<br>|0.026|0.038|0.050|||
||99<sup>th</sup> percentile<sup>ii</sup>|0.411|0.128|0.043|||

Note: Predicted probabilities are estimated using models in Tables 1 and 3, for Prio \ Uppsala civil war. All remaining right hand side variables are kept at observational mean.

i Countries in the 99th percentile include: Saudi Arabia, Kuwait, Libya, United Arab Emirates.

ii Countries in the 99th percentile include: Saudi Arabia, Kuwait, Libya, United Arab Emirates, Qatar, Brunei. An increased risk of coup d’état reduces the predicted probability at oil rent levels in between the 99<sup>th</sup> and the 95<sup>th</sup> percentile, for additional countries like: Bahrain, Equatorial-Guinea, Gabon, Iraq, Norway, Oman, Trinidad and Tobago, and Venezuela.

35
scores. The results of the intervention are persistent. In the _regional_ sample, 14 months after the intervention those students from schools assigned to treatment that took the census evaluation improved their scores by 10%, 12% and 10% in _Mathematics_ , _Reading Comprehension_ , and _History, Economics and Geography_ (compared to the control group). Our results shed light on the potential of growth-mindset interventions. Given its per capita ITT cost, this puts the ‘ _¡Expande tu Mente!_ ’ package among the most highly cost-effective educational interventions in the developing world. At the same time, our results highlight that while growth-mindset interventions work, they do not work in all contexts. In _metropolitan_ areas, where schools and classrooms are larger and students have higher scores, our intervention failed to be effective.

The rest of the paper is organized as follows. Sections 2 and 3 describe the intervention and its conceptual pathways (respectively). In section 4, we describe the experimental design and the implementation of the intervention in the field. Sections 5 and 6 present the data and the empirical strategy, and sections 7 and 8 report our main results and robustness checks. Section 8 concludes.

# **2.** **<u>The intervention</u>**

The intervention, known as ‘ _¡Expande tu Mente!_ ’ in Peru (‘Grow Your Mind!’; from here onwards, GYM)) was a collaboration between the authors and the Peru Ministry of Education (MINEDU). Within MINEDU, the research team worked in coordination with _MineduLAB_ , the cost-effectiveness laboratory of the Ministry, and the Direction of Secondary Education. The main objective of the intervention was to instill in students the growth-mindset to improve their motivation, perseverance, effort at school, and, ultimately, academic performance (see the theory of change in **Section 3** ). The basic education level in Peru is composed of a pre-school level, six grades at the primary level and five grades at the secondary level. The intervention was designed for the early grades of the secondary level, specifically for those in the first and second grade of secondary (equivalent to the seventh and eighth grades, respectively), a transitional period that students often find challenging and which is the beginning of the path to dropping-out from school.

The key component of the intervention is a 90-minute school session designed to introduce students to neuroplasticity and the concept that cognitive abilities are malleable. It was 5
NIC industry dummies that are consistent across all census-years and restrict the data to only the manufacturing sector.<sup>10</sup> To explore industry heterogeneity, we use three different classifications of industries. First, we follow the procedure in Rajan and Zingales (1998) and estimate an industry’s dependence on external financing using data on US corporations (RZ index). The RZ index is based on the assumption that since U.S. financial markets are developed, sophisticated, have fewer market imperfections and relatively open they should allow US firms to achieve their desired financial structure. Thus assuming that there are technological reasons why some industries depend more on eternal finance than others, the RZ index offers an exogenous way to identify the extent of external dependence of an industry anywhere in the world. The methodology does not require that the US markets are perfect but rather that market imperfections in the US do not distort the ranking of industries in terms of the technological dependence on external financing.

We reconstruct the RZ index for the period 1980-2012 by computing external dependence as (Capital Expenditures-Operating Cash Flow)/Capital Expenditures for each domestic US firm in Compustat. The median value across firms in each industry is a measure of that industry’s dependence on external finance. This index of external dependence is constructed at the SIC 2/3 digit level which is then manually matched to 3-digit ISIC codes that map onto the Indian NIC classification. Appendix C provides the concordance used in the paper. We construct **EFD,** a dummy variable that takes the value 1 if industry’s dependence on external finance is ≥ median value of dependence on external finance across industries and 0 if it was < the median across industries.

> 10 We drop recycling from the manufacturing sector since it is not included under manufacturing in the ISIC classification.

10
|**Table**<br>**Information Source**|**A11: Most trus**<br>**Burkin**|**ted informatio**<br>**a Faso**|**n sources by**<br>**Ma**|**vaccine accep**<br>**lawi**|**tance**<br>**Nig**|**eria**|
|---|---|---|---|---|---|---|
||**Hesitant**|**Willing**|**Hesitant**|**Willing**|**Hesitant**|**Willing**|
|Medical professional|33.5|49.2|32.5|35.0|18.7|31.2|
||(26.9 to 40.1)|(44.3 to 54.1)|(24.2 to 40.8)|(30.2 to 39.7)|(13.7 to 23.8)|(27.9 to 34.5)|
|Scientists and epidemiologists|0.5|0.0|0.0|0.0|0.0|0.4|
||(0.0 to 1.0)|(0.0 to 0.1)|||(0.0 to 0.1)|(0.1 to 0.7)|
|Celebrities and influencers|0.0|0.0|0.3|1.1|2.9|2.1|
|||(0.0 to 0.1)|(0.0 to 1.0)|(0.2 to 1.9)|(0.2 to 5.7)|(1.1 to 3.2)|
|NGO outreach programs|0.2|0.5|0.1|1.9|0.4|1.1|
||(0.0 to 0.4)|(0.1 to 0.8)|(0.0 to 0.3)|(0.4 to 3.4)|(0.0 to 0.9)|(0.5 to 1.8)|
|Other outreach programs|0.1|0.4|1.8|2.4|1.5|0.9|
||(0.0 to 0.4)|(0.0 to 0.8)|(0.0 to 4.3)|(0.8 to 4.0)|(0.0 to 3.0)|(0.4 to 1.5)|
|Local government|3.0|6.6|0.0|0.7|3.2|11.9|
||(0.8 to 5.2)|(4.5 to 8.8)|(0.0 to 0.1)|(0.0 to 1.7)|(1.0 to 5.3)|(9.5 to 14.2)|
|Federal government|1.0|0.0|0.0|0.0|7.1|9.2|
||(0.0 to 2.8)|(0.0 to 0.0)|||(3.2 to 11.0)|(7.4 to 11.0)|
|State government|8.1|5.6|0.0|0.0|5.7|6.6|
||(4.4 to 11.7)|(3.4 to 7.7)|||(2.6 to 8.8)|(5.0 to 8.2)|
|Family and neighbors|4.5|5.9|3.5|4.3|17.1|11.7|
||(1.9 to 7.0)|(3.9 to 7.9)|(1.4 to 5.6)|(2.0 to 6.7)|(12.4 to 21.7)|(9.5 to 13.9)|
|Religious organizations|0.3|1.1|1.1|2.7|5.4|3.3|
||(0.0 to 0.6)|(0.1 to 2.2)|(0.0 to 2.6)|(0.7 to 4.7)|(2.3 to 8.4)|(2.3 to 4.3)|
|Traditional healer|1.0|0.2|0.0|0.0|0.0|0.0|
||(0.0 to 2.9)|(0.0 to 0.4)|||||
|Traditional ruler|0.8|2.1|1.2|2.2|1.4|1.6|
||(0.0 to 1.9)|(0.7 to 3.6)|(0.0 to 2.3)|(0.8 to 3.6)|(0.0 to 3.2)|(0.6 to 2.5)|
|Media|34.1|23.9|37.6|33.3|12.7|10.5|
||(27.2 to 40.9)|(20.1 to 27.7)|(29.8 to 45.4)|(28.7 to 37.8)|(8.4 to 17.0)|(8.6 to 12.4)|
|Other info source|1.4|0.0|0.4|0.4|4.2|1.6|
||(0.0 to 2.9)||(0.0 to 1.2)|(0.0 to 1.0)|(1.0 to 7.4)|(0.5 to 2.7)|
|None|6.5|0.1|11.4|1.6|14.0|2.8|
||(3.8 to 9.1)|(0.0 to 0.2)|(6.2 to 16.6)|(0.6 to 2.6)|(9.4 to 18.7)|(1.7 to 4.0)|
|Trust all equally|5.3|4.3|10.1|14.3|5.6|5.1|

24
# **Contents**

|I. Introduction ............................................................................................................................................... 3|
|---|
|II. Introduction to Night-time Lights Data .................................................................................................... 3|
|III. Literature Review .................................................................................................................................... 6|
|IV. Night-Lights Hypotheses ........................................................................................................................ 9|
|V. Characteristics of Night-time Lights Data ............................................................................................. 11|
|VI. Characteristics of the Dependent Variables .......................................................................................... 16|
|VII. Hypothesis Testing .............................................................................................................................. 17|
|VIII. Hypothesis Testing Results ................................................................................................................ 18|
|IX. Conclusions........................................................................................................................................... 24|
|Bibliography ............................................................................................................................................... 26|
line with the significant impacts found on self-employment in the short-term as discussed in the previous section.

Estimates in Panels B-D (Table 3) show that the treatment group tried and realized more projects than the control group up until 4 years after graduation, although ultimately they did not have more projects at the time of the endline survey. Panel B shows that the entrepreneurship track led to more project ideas and more attempts to realize these ideas overall. At the time of the endline survey, 67 percent of the treatment group reported having had a project idea in the past, significantly more than in the control group (58 percent). Overall, 28 percent of the treatment group tried to realize their past project idea, again significantly more than in the control group (19 percent). The higher share of attempts to realize a project in the treatment group led to more projects set-up; with 7 percent of the treatment group reporting having set-up a project, compared to 4 percent of the control group. On the other hand, the additional attempts also resulted in more failures in setting up projects in the treatment group (14 percent) compared to the control group (10 percent).

Panel C shows that the treatment group is not more likely to have an active project at endline, however. As mentioned above, a slightly larger share of program participants has realized a project at some point (7 percent versus 4 percent). However, program participants are no more likely to still have an active project in 2014 compared with the control group (4 percent versus 2 percent), or a project temporarily closed (2 percent versus 1 percent).<sup>17</sup> Finally, Panel D shows that while the program increased the likelihood that individuals had a project idea and tried to realize it, their success rates conditional on having an idea and trying to realize it did not increase. Conditional on having had an idea, significantly more program participants than non-participants (42 percent versus 33 percent) reported having tried to realize their project. Of those who tried to realize their project, about half failed (49 percent in the treatment group and 51 percent in the control group), a quarter realized their project (25 percent in the control group and 19 percent in the treatment group), and another quarter has an idea still under preparation (24 percent in the control and 30 percent in the treatment group). Importantly,

> 17 The absolute numbers also remain small. Of the 340 people who tried to realize their ideas, 77 successfully realized the project at some point (43 projects are still active, 17 are temporarily closed, and 17 are permanently closed), 90 are still preparing their projects, and 169 failed to realize their projects. Out of those who failed, only 59 have a second idea.

16
where yf,<sup>9 92</sup> and yf,<sup>9 96</sup> are loan levels of bankfin 1992 and 1996 respectively, and **0 f"992** and gf,1996 are the inverse of the output-oriented measure of efficiency of bank f calculated using **y** DEA in year 1992 and 1996 respectively. Our measure of risk-taking focuses on excessive loan growth and does not take into account that banks also made excessive investments in securities. However, in comparison to their lending operations East Asian banks invested a relatively small amount in securities (see Appendix, Table 4). It is likely that in 1992, banks already took excessive risks. Our risk measure, however, does not include the risk acquired until 1992, and thus only measures additional risk taken during 1992-96.

# 3. **Overview of the East Asian Financial Sectors**

The financial systems of Indonesia, Korea, Malaysia, Philippines and Thailand have long been dominated by commercial banks. In all countries the commercial banking sector is dominated by a few large domestic commercial banks with extensive branch networks. Foreign banks, which are often disadvantaged by a number of restrictions, account for a relatively small amount of bank assets and mostly provide trade finance to multinational firms. Other large groups of financial institutions are the finance companies in Malaysia and Thailand, and the merchant banks in Korea.

Finance companies and merchant banks faced more restrictions and engaged in different activities than commercial banks. Thai finance companies, for example, were not allowed to mobilise demand or time deposits, but were only permitted to issue fixed-term promissory notes and certificates of deposits to fund their businesses. In view of these restrictions, Thai finance companies could not effectively compete for most of the commercial bank mainstream business, and were largely left to engage in more risky activities. Commercial banks had more freedom to choose their business mix and faced similar restrictions across countries. For the above reasons, we exclude finance companies and merchant banks from our analysis and focus only on commercial banks.

The local commercial banks in all five East Asian countries were originally set up by the State, a handful of families who controlled large enterprises (such as rice farms in the case of Thailand), or by trading houses. Despite attempts by the government to diversify the ownership of the local commercial banks, Indonesian and Thai commercial banks are still dominated by a handful of families. All the banks have powerful connections with the local and international business community and with the government. In Korea, the largest banks are typically 10
**When the private sector invests in infrastructure, it tends to choose the optimal organizational forms.** As Figure 6 shows, electricity and transport are leading the way in private sector participation in South Asia. As Figure 7 shows, in transport, the private sector tends to partner with the public sector through PPPs; while in telecoms, it tends to invest by itself (i.e., regulated privatization). When it comes to energy, the private sector chooses to invest mainly by itself, but also through partnerships with the public sector. Many of the PPPs in the power sector in South Asia happen in generation (60),<sup>31</sup> mainly through BOT arrangements – even though PPPs are the optimal organizational structure in transmission, not in generation or distribution.

**Figure 6: There are more Projects under PPPs and Privatization in Electricity and Transport**

<!-- Start of picture text --> Water & San.<br>Transport<br>Telecom<br>Electricity<br>0 50 100 150 200 250 300 350 400 450 500<br>Number of Projects under PPPs and Privatization<br>BGD BTN IND MDS NPL PAK LKA<br><!-- End of picture text --> _Source: World Bank Private Participation in Infrastructure Database._

**Figure 7: Electricity and Telecom Favor Privatization while Transport and WSS Favor PPPs**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>BGD BTN IND MDS NPL PAK LKA BGD BTN IND NPL PAK LKA IND MDS PAK LKA IND<br>Electricity Telecom Transport WSS<br>PPP Privatization<br>Breakdown (%)<br><!-- End of picture text -->

> 31 As of August 2013, according to the World Bank Private Participation in Infrastructure Database there are 69 active projects in the power sector under lease contract, concession, or BOT.

28
<!-- Start of picture text --> 200% 192.3%<br>175%<br>150%<br>125%<br>100%<br>75%<br>50%<br>25% ae% 15.7% 15.2% 13:0% 12.9% 11.7%<br>fe)<br>2017 2006 2008 2010 2013 2015 2015<br>Guinea Rep. of DRC Sao Tomé & Niger Angola Rep. of<br>Chalco, CP! Congo Eximbank Principe = Eximbank CDB Congo<br>& Henan — Eximbank Gov. Nigeria Trafigura<br><!-- End of picture text -->
# **4.4 Results Using Alternative Country Samples**

In Table 6, we retain the Poisson model with interaction terms as the baseline and exploit the possibility for variation across country groups to examine in greater detail the links between logistics performance and trade in production networks.

First (columns 1-3), we limit the estimation sample to countries in the World Bank’s East Asia and Pacific (EAP) region. The reason for doing so is that production networking is widespread and particularly well developed in that region. We would therefore expect to see stronger evidence of the role that logistics can play when we limit consideration to the set of countries that are most actively engaged in this type of production. In column 1, we limit the sample to EAP exporters, but include all importers. In column 2, we consider only EAP importers, but include all exporters. Column 3 limits the sample to EAP exporters and EAP importers only, i.e. intra-regional trade.

In all three cases, we find continued strong evidence of the importance of logistics as a determinant intra-network trade relative to trade in final goods: the import side interaction term is positive and 5% significant in each of the three regressions. The export side interaction term is only negative and statistically significant in one regression (column 2), and is statistically insignificant in the other two. Interestingly, for the case of intra-regional trade, the data suggest that the impact of logistics performance is only statistically significant in the case of parts and components, thus highlighting again the importance of this area for regional production networks. It is important to be cautious in interpreting this result, however, due to the greatly reduced sample size that results when only intraregional trade flows are considered.

Another potentially informative split in the data is between South-South and South-North trade. For definitional purposes, we take the “North” as including all high income countries (OECD members and others), while the “South” consists of all other countries (i.e., all World Bank low and middle income 17
OZIER - REPLICATION REDUX – p. 24 of 41 Dunn/Bonferroni style of correction simply involves multiplying the p‐value by two; other corrections that take into consideration correlations between outcomes generally require smaller corrections.<sup>23</sup> Allowing for both the test of the coefficient 0.0387 (yielding a conventionally insignificant p‐value of around 0.2) and the test yielding of the coefficient 0.0848 (yielding a conventionally very significant p‐ value of less than 0.00001) to be considered, then, is there a significant result –after correcting for performing two tests—that agrees qualitatively with the original paper? There is. The p‐value associated with the 8.48 percentage point increase in school attendance, when doubled, is still significant by the conventional standards of both economics and epidemiology.

This is the first place where drawing a conclusion from the replication exercise proved to be divisive. MK’s own replication files include all these calculations, old and new. Following the corrected data construction, but without any adjustment to analytical decision‐making in light of the data, one could conclude that the 7.5‐percentage‐point finding drops to insignificance. Allowing for such adjustments, either via a minimum mean‐squared‐error approach or a multiple‐testing‐adjustment approach, one would conclude that the 7.5‐percentage‐point finding is now an 8.5‐percentage‐point finding, and is stronger than before.

Either way, everyone agrees that the study shows (and the corrected data can be replicated to show) an effect of deworming treatment on direct recipients of medication, on children in the same school, and on children within three kilometers; this is true both in terms of moderate‐to‐heavy worm infections and in terms of school attendance. In fact, in the published version of the pure (verification)

> 23 The intuition behind this correction is that, whatever vanishingly small probability there is of estimating a very statistically significant effect when one really isn’t there, the odds of finding it in either of two separate attempts are about twice that, if the tests are not correlated with one another. More recently developed (and more statistically powerful) corrections are also possible, but this is perhaps the simplest and most conservative. Olive Jean Dunn published on this correction in 1957 and 1958.  Her work builds on, and refers to, Carlo Emilio Bonferroni’s work in the 1930s, but to the best of my knowledge, his work did not include the correction itself. Stigler (1980) suggests that, in statistics, discoveries are named after someone other than their discoverer; but not having been able to quickly discern who actually proposed this correction, I mention them both.