rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Table 3: Effect of girls’ scholarship program on number of test takers<br><!-- End of picture text -->

|outcome||||test tak|ers|||
|---|---|---|---|---|---|---|---|
|units<br>|lev<br>|el<br>|lo<br>|g<br>|%female|asshare<br>|ofenrolled<br>|
|sample|girls|boys|girls|boys||<br>girls|<br>boys|
||®|@|<br>@®|<br>@®|©|©|(7)|
|Panel A: scholarship-eligibl|e schools|||||||
|girls’scholarshipprogram|26.1**<br>|42.6<br>|0.55**<br>|0.39<br>|0.03<br><br>|0.28**<br>|0.13**<br>|
||(3.7)|(21.3)|(0.07)|(0.19)|(0.03)<br>~|— (0.06)|~—-(0.04)|
||02]|[.35}|[.02],|28)|<br>s.45]—|<br>—«.03]|[.02]|
|R-squared|0.10|0.10|0.08|0.09|0.64|0.51|0.55|
|mean outcome|40.3|77.8|3.4|4.1|0.34|0.85|0.88|
|Panel B: all schools||||||||
|girls’ scholarship program|96.1**|112.9**|0.93**|0.69*|0.02|0.23**|0.16**|
||(15.3)|(28.5)|(0.12)|(0.21)|(0.04)|(0.06)|~—(0.04)|
||03]|[05]|~—-[.02]—s|«[.10),—S|«6|1]—SS«.03]|[.02]|
|R-squared|0.28|0.19|0.16|0.09|0.75|0.41|0.54|
|mean outcome|64.4|98.8|3.6|4.2|0.40|0.83|0.85|
|N|188|188|188|188|188|177|177|

Sample is district panel, 1998-2012. All regressions include district and year fixed effects. Regressions for proportion female and share enrolled weighted by number of test takers. Number of observations differs for enrollment share due to districts with missing enrollment data. Standard errors in parentheses, clustered by region. p-values in brackets, calculated from 200 iterations of wild-t cluster bootstrap of Cameron et al (2008) with Webb (2013) 6-point distribution of weights. Significance levels are based on these p-values: * significant at 10%; ** significant at 5%; *** significant at 1%.
Puerto Montt <!-- Start of picture text --> 800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>-100<br>-200<br>May-48 Oct-53 Abr-59 Oct-64 Mar-70 Sep-75 Mar-81 Ago-86 Feb-92 Ago-97 Feb-03 Jul-08<br>Punta Arenas<br>200<br>150<br>100<br>50<br>0<br>-50<br>May-48 Oct-53 Abr-59 Oct-64 Mar-70 Sep-75 Mar-81 Ago-86 Feb-92 Ago-97 Feb-03 Jul-08<br>Precipitation anomaly (mm/month)<br>Precipitation anomaly (mm/month)<br><!-- End of picture text --> _Source:_ Authors’ estimation based on data from the NCDC’s Monthly Climatic Data for the World.

A trend analysis, like the one performed on temperatures above, reveals two mid-latitude stations with a significant negative precipitation trend, while the remaining five stations show no significant trend (see Table 8). This is consistent with the findings in the latest IPCC report (see Magrin et al. 2007, Figure 13.1).

18
# **Tweet Classification - Identifying relevant crowdsourced reports**

We first developed an algorithm to identify whether a tweet is crash related or not, using the truth data set to train the algorithm. We extract features from tweets by extracting n-grams from tweets. We employ a grid search, tuning the models by testing all combinations of multiple parameters. The three main parameters we test are: (1) extracting 1-grams, 1-2 grams or 1-3 grams, (2) not removing any features or removing features that occur in less than/more than 0.01%/99.9%, 1%/99% or 5%/95% of tweets, (3) defining features as the number of occurrences of the n-gram in the tweet or using the Term Frequency - Inverse Density Frequency (TF-IDF) of the n-gram<sup>2</sup> For the Support Vector Machine, we also vary the regularization parameter–which controls how the algorithm weighs misclassification versus simplicity–using 0.5, 1, 2, 10, 100 and 1000.

Table S2: Example Tweet and Augmented Tweet accident <u>past garden</u> city near thika rd and kamiti rd <u>junction</u> accident <u>past #landmark-name#</u> near <u>#road-name#</u> and <u>#road-name# junction</u> An additional parameter we test is using the original tweet text and, following [6], replacing landmark names and road networks with generalized names (just indicating the presence of a landmark or road). Generalizing landmark and road names helps to reduce the dimensionality of the feature space. Table S2 demonstrates how a particular tweet is transformed into one with general landmark and road names. Table S3 shows examples of the features extracted in regular and augmented tweets where landmarks and roads have been replaced. This augmentation assumes that the occurrence of a road or landmark name contributes equally to the probability of a crash-related tweet.

> 2TF-IDF reflects how important a word or n-gram is to a tweet within the full set of tweets; for example, words such as ‘a’ or ‘the’ that appear frequently will be given less weight. It is calculated as

> _log_ ( _N TweetsN withTweetsN − gram_<sup>)</sup><sup>_×Ntimes_</sup> _N_<sup>_n_</sup> _n_<sup>_−_</sup> _−_<sup>_<u>g</u>_</sup> _grams_<sup>_ramapp_</sup> _in_<sup>_ears_</sup> _a tweet_<sup>_inatweet_</sup>

S7
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<!-- Start of picture text --> Mongolia Total resources share of total wealth 63.3% TFP growth with natural resource 3.9%<br>TFP growth excluding natural resource 4.0%<br>Mongolia: Total Factor Productivity<br>12.00%<br>10.00%<br>8.00%<br>6.00%<br>4.00%<br>2.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐2.00%<br>‐4.00%<br>‐6.00%<br>TFP, with natural resources TFP, excluding natural resources<br>Mauritania Total resources share of total wealth 59.8% TFP growth with natural resource 0.7%<br>TFP growth excluding natural resource ‐1.2%<br>Mauritania: Total Factor Productivity<br>15.00%<br>10.00%<br>5.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐5.00%<br>‐10.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text --> Finally, in Annex III Table A.1 we see the full listing by country of TFP growth over 1996-2006, 2007-2014, and 1996-2014. The sharp impact of the financial crisis is immediately obvious in small or negative TFP growth rates over 2007-2014. Table A.2 lists the contribution of natural resources to GDP growth for our sample of countries over the full period, highlighting the major positive contribution that natural resources make to GDP growth.

# **4. Conclusions and next steps**

We began this study by emphasizing the large reliance of developing countries, and particularly low-income countries, on natural resources as a component of wealth. The traditional approach to TFP growth estimation has, to date, excluded natural resources, with the exception of recent 25
Likewise, individuals may trust those who are more similar to themselves (i.e., the same social, racial, religious or ethnic group), and may trust others they have known longer (Alesina and La Ferrara, 2002; 2005). Hence, we control for religiosity and immigration status. The country and year fixed effects capture any national-level policies or shocks, such as storms, conflict, and economic downturns, and controls for any changes in the overall trend of trust in a country.

# **3. Results**

Table 1 presents the descriptive statistics for trust and food insecurity, as well as mean comparison tests examining differences across economic development rankings.<sup>9</sup> Roughly 53% of individuals trust their national institutions, and roughly 80% trust their friends and family.<sup>10</sup> About 30% of the world’s population experienced food insecurity in 2014-17. The descriptive statistics suggest there are systematic differences between trust and food insecurity depending on economic development. Respondents in low-income countries have higher likelihoods of trust in national institutions, but significantly lower trust in their friends and family. As expected, food insecurity is much more prevalent in low-income countries.

Table 1. Descriptive statistics.

|||Econo<br>|mic development<br>Lower-middle-<br>|rankings (GNI per<br>Upper-middle-<br>|capita)<br>|
|---|---|---|---|---|---|
||World sample|Low-income|income|income|High-income|
|Dependent variables||||||
|Trust in national institutions|0.530|0.566|0.564***|0.439***|0.557***|
||(0.367)|(0.369)|(0.371)|(0.370)|(0.347)|
|Trust in friends and family|0.803|0.672|0.754***|0.832***|0.902***|
||(0.398)|(0.469)|(0.431)|(0.374)|(0.298)|
|Food insecurity variables||||||
|Food insecurity|0.303|0.623|0.348***|0.277***|0.088***|
||(0.460)|(0.485)|(0.476)|(0.447)|(0.283)|
|Number of observations|387,385|68,925|106,018|100,397|112,045|

> 9 Descriptive statistics by regions and economic development and for all control variables are shown in Appendix B. 10 Sample weights provided by Gallup are used to estimate all descriptive statistics and prevalence rates but are not used in the regression analyses.

9
Table A4. Contributions of global shocks to alternative measures of global inflation: Robustness checks

||||Struc<br>|tural Shocks<br>||
|---|---|---|---|---|---|
|Sensitivity|Proxy variables|Oil<br>Price|Global<br>Supply|Global<br>Demand|Global<br>Interest rate|
|Core CPI|Baseline<br>Global Factor Measures<br>(Simple average of G7)<br>Global Factor Measures|6.9<br>[0.5 14.5]<br>11.9<br>[1.7 25.0]<br>17.9|41.1<br>[8.3 77.3]<br>41.9<br>[11.7 65.9]<br>38.5|25.7<br>[1.9 55.6]<br>25.5<br>[2.9 55.8]<br>23.2|26.4<br>[2.5 54.3]<br>20.7<br>[1.9 45.4]<br>20.4|
||(US variable)<br>Alternative narrative approach|[0.8 32.9]<br>13.7<br>[1.8 28.7]|[8.0 71.3]<br>40.9<br>[8.7 73.4]|[4.6 43.1]<br>18.1<br>[1.0 35.5]|[1.0 44.6]<br>27.3<br>[3.5 54.6]|
||Baseline|38.0<br>[12.4 68.9]|22.5<br>[3.0 42.2]|22.9<br>[3.4 48.5]|16.5<br>[1.5 35.7]|
|Producer price index|Global Factor Measures<br>(Simple average)<br>Global Factor Measures|39.2<br>[10.9 73.7]<br>30.1|19.0<br>[2.1 40.2]<br>21.6|27.6<br>[3.7 61.0]<br>26.0|14.2<br>[0.9 28.5]<br>22.3|
||(US variable)|[11.4 51.4]|[3.8 41.3]|[2.3 53.0]|[1.8 47.3]|
||Alternative narrative approach|37.8<br>[12.5 68.5]|21.5<br>[2.7 41.6]|22.3<br>[2.8 47.9]|18.4<br>[1.6 37.1]|

Note: This table reports variance decompositions of global core CPI and PPI inflation (in percent) based on various types of robustness exercises as explained in the paper. Variance decompositions are based on median from 1000 successful Bayesian draws. The numbers in the brackets are 16-84 percentiles draws. “Baseline” indicates the variance decompositions of global inflation based on the global FAVAR model that consists of oil price growth, and the global factors of inflation, output growth, and interest rates extracted from the dataset of G7 countries. “Alternative narrative restriction” indicates that narrative restrictions are imposed on the historical contribution of oil price shocks on oil prices.

54
discussing how different welfare estimation methods differ for different parts of the welfare distribution. This is important because, as we stated in the introduction, this paper is mainly concerned about poor people and poor countries. Moreover, by shifting the analysis to the distribution of welfare, we can also borrow from welfare economics and the very rich literature on distributional analysis and use instruments such as stochastic dominance or pro-poor curves to refine our assessment of the difference between welfare estimation measures. This is what this section is about. We will start by introducing a possible strategy to estimate standard errors at the social and quantile levels and we will finish by introducing pro-poor curves for comparing welfare estimation methods.

# **5.2.1 Statistical inference (Society)**

The questions we address are the following: Are the differences between means of welfare estimation methods statistically significant? And can these statistical tests help us to discriminate between welfare estimation methods? Most analysts work with sample surveys and welfare estimates such as _LV_ or _CS_ will contain a sampling error. For each sampled household _i_ , one can estimate the net impact on wellbeing: ∆ _νi_ = _f_ (∆ _p_ 1 _, ...,_ ∆ _pk_ ; ∆ _m_ = _tri_ ) where _tri_ refers simply to the transfer to the individual _i_ . Since the required information to assess _νi_ comes only from the sampled households _i_ , the variable ∆ _ν_ will be _i.i.d_ . In general, the sample size of national household surveys is large and, based on asymptotic theory and the case of large sample size, the distribution of any estimator will converge to the normal distribution. We need, therefore, to start by finding a proper approach to the estimation of the standard error of the difference between means of welfare estimation methods.

Suppose that we want to estimate the mean difference between _LV_ and _PV_ , the upper and lower bounds of our welfare estimation methods. Let ∆ denote the estimator of the mean _PV,LV_ difference, _N_ the simple size and _I_ the number of goods so that where _μx_ denotes the average of _x_ . For the case of Cobb-Douglas preferences, we have that _x_<sup>_b_</sup> _i_<sup>=</sup><sup>_xa_</sup> _i_<sup>_/_(1 +</sup><sup>_dpi_) where the initial price is assumed to be equal to 1.In this case, for multiple</sup> price changes we have
ranks among the top three explanatory variables for 5 of 7 outcomes (DRI, EIU, GAD, WMO, and CPIA). This indicator consists of four sub‐indicators at the DB‐41 level of disaggregation. But these sub‐ indicators turn up in the top 25 percent of explanatory variables at the DB‐41 level for only 6 of 20 possible cases for these five outcomes. This rate of 0.30 is only marginally higher than the unconditional probability of appearing in the top‐25 percent. More systematically, as we did with GII we test the null hypothesis that falling in the top 25 percent of explanatory variables at the highest level of disaggregation is statistically independent of falling in the top 25 percent at lower levels of disaggregation. Across all 7 outcome variables we never fail to reject this null hypothesis (the lowest p‐ value for the null of independence that we observe is 0.64). This indicates that the DB dataset has the same problem of instability across levels of disaggregation that we saw with GII.

# **5.  Robustness and Caveats**

We began this paper with the observation that realizing the promise of disaggregated governance indicators to identify reform priorities requires knowledge of the partial effects of these potentially very many variables on outcomes that policymakers might reasonably care about. For a given outcome and set of indicators at a given level of disaggregation, we find that the BMA methodology used here yields useful results by identifying a fairly small subset of indicators that are robustly partially correlated with the outcomes of interest. However, we have also documented that these ostensibly sensible results for a given outcome and level of disaggregation break down when we consider other closely‐related outcomes and greater levels of disaggregation. Disaggregated indicators that are important predictors of one outcome are not systematically also important predictors of other very similar outcomes. Recall for example that the median pairwise correlation across our 7 measures of corruption was high at 0.61 for 7 outcome variables used with GII, and 0.68 for 7 outcome variables used with DB, yet we could not reject the null hypothesis that and indicator being included in the top‐25 percent of explanatory variables ranked by importance for one outcome was independent of appearing in the same top‐25 list for other outcomes. And looking across levels of disaggregation, we found no evidence that being in the top‐25 list for a given outcome at one level of disaggregation was a good predictor of being in the top‐25 list at another level of disaggregation.

One might first reasonably wonder how robust this key instability finding is. For example one might wonder whether this strong instability is driven by the somewhat arbitrary cut‐off value that we have been using to identify the set of important explanatory variables. After all, in Tables 4A‐4C, many 24
# **6 Conclusion**

This paper examines the economic impacts of a large-scale marine pollution disaster on the employment and earnings of a local fishery community. The _Formosa_ incident, in which toxic wastewater was discharged into the ocean and damaged an entire ecosystem in the central – coast of Vietnam in 2016, presented a special case study for how the affected communities – saltwater fishers coped with the negative shock. We combine a novel satellite data capturing night-time light detected from fishing boats with the fishery earnings and labor-market information provided by the labor force surveys. We show that the disaster reduced incomes by as much as 46 percent for the immediate period after the _Formosa_ breakout. We further provide evidence indicating potential coping mechanisms. Upstream fishers who live closer to safe fishing grounds were likely to travel there and continue fishing, as shown by the intensified fishing activities in those regions after the incident took place. In contrast, downstream fishers, who live far away from safe waters, experienced more dramatic impact on average earnings. In terms of the labor-market responses, we find that these individuals substitute work hours in fishery with working secondary, non-fishery jobs. Both coping mechanisms are shown to help mitigate the income losses, even though far from entirely. We also find that the income damage to fishery diminished over time, even though it remained sizable and statistically significant at the end of 2016. Finally, we discover a positive spillover income effect on the freshwater fishery industry, which produces seafood’s substitute products.

Examining the impact of _Formosa_ on the affected population, and how these victims cope with the extreme shock, is relevant to the design of different assistance policies. We show that, even on average, the impact of _Formosa_ was not uniformly distributed among the victims. It is also evident that _Formosa_ did not just affect saltwater fishery in the four provinces located within the contaminated zone, but also the nearby regions and other industries as well. These findings should be factored into any top-down incentives to compensate and subsidize individuals and households.

# **References**

> Al-Majed, Abdul Aziz, Abdulrauf Rasheed Adebayo, and M. Enamul Hossain, “A sustainable approach to controlling oil spills,” _Journal of Environmental Management_ , 2012, _113_ , 213 – 227.

> Belasen, Ariel R. and Solomon W. Polachek, “How Disasters Affect Local Labor Markets: The Effects of Hurricanes in Florida,” _Journal of Human Resources_ , 2009, _44_ (1).

> Burkhauser, Richard V., Kenneth A. Couch, and David C. Wittenburg, “Who Minimum Wage Increases Bite: An Analysis Using Monthly Data from the SIPP and the CPS,” _Southern Economic Journal_ , 2000,

23
storyline did not focus on these issues. However, there may have been indirect impacts on these measures as a result of improved financial behavior elsewhere. Our results do not find significant differences except in feeling confident about solving financial problems.

The non result on savings is, in fact, encouraging as it provides some evidence against survey bias – that individuals were not simply providing answers that please the surveyors, else we would have seen a positive treatment effect here also.

# _6.5 Seeking Financial Advice_

Finally, we turn to an important feature of the soap opera – a public call to action through the National Debt Mediation Association (NDMA). As explained in previous sections, the NDMA was highlighted prominently in the storyline and their toll free number was broadcast at the end of each episode which featured an NDMA representative helping the main soap character.

We examine quantitative outcomes in two separate ways. First, we obtained daily call volume data from the NDMA call centers and observe a huge spike in incoming calls immediately following the episode where the NDMA was introduced into the soap storyline. Figure 2 plots the daily call volume, with the red vertical line representing the date of the soap feature. The call volume immediately jumped from an average of 120 calls per day to over 500, a more than 300 percent increase.

Unfortunately, we cannot classify and match callers to our sample and hence can only present a before-after picture. However, our second intermediate survey, conducted just a short while later included a question on consumer awareness of financial advice outlets. The regression analysis confirms a significant increase in awareness of formal avenues for financial advice (such as the NDMA). Specifically, as shown in column 1 of Table 9, compared to an average of 69% in the control group, nearly 80% respondents in the treatment group stated that they would seek financial advice from a formal source such as the NDMA if it was needed.

Surprisingly, however, this effect dissipated in the long run. Columns 2-4 of Table 9 shows that while the average proportion of respondents in treatment and control who would seek formal 22
- 29 -

**<u>Table 13. Transport Cost Estimation</u>**

||OLS<br>Coef.|Std.Err.||IV<br>Coef.|Std.Err.||
|---|---|---|---|---|---|---|
|RAIL|-0.019|(0.011)|*|-0.058|(0.060)||
|lnY|0.000|(0.002)||0.000|(0.001)||
|lnAGE|0.006|(0.004)||0.008|(0.005)|*|
|EDU|-0.002|(0.002)||-0.002|(0.002)||
|GLBL|-0.014|(0.008)|*|-0.011|(0.009)||
|FORN|0.000|(0.000)||0.000|(0.000)||
|Constant|-2.748|(0.031)|***|-2.755|(0.029)|***|
|Obs.|331|||331|||
|R squared|0.7558|||0.7468|||
|F stat.|251.26||||||
|Wald chi||||6498.2|||
|No. of dummy vari|ables:||||||
|Industry|4|||4|||
|Region|16|||16|||
|Exogeneity test:|||||||
|Chi2 (1) stat.||||0.446|||
|Overidentifying res<br> Chi2(3) stat.|triction test:|||12.806|***||

The dependent variable is the transport costs per km. Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at 10, 5 and 1 percent, respectively.

# **VI. CONCLUSION**

In theory, rail transport has the advantage for large-volume long-haul freight operations. Because of the significant amount of fixed costs required, railway transport exhibits large economies of scale. It is a common view that 1 million net tons a year are needed to justify economic viability of railways. Africa possesses significant railway assets. However, many rail lines are currently not operational because of the lack of maintenance.

1. This paper recast light on the impact of rail transportation on firm productivity, using micro data collected in Tanzania. Only a small share of surveyed firms is currently using rail freight services to export or import goods through the Port of Dar es Salaam. To avoid the