rafmacalaba/data-use-mentions-extended huggingface dataset:
will answer is how effective fuel prices are in reducing emission intensity in Brazil. We will analyze both the effect of the general fuel price level and price differentials between fuels on emission intensity. Additionally, we will also understand the evolution and heterogeneity of emission intensity in Brazil and whether the pandemic in 2020 has an impact. As price data are timelier with higher frequency than emission and GDP data, our model could also be used to nowcast emission intensity.

# V.1. Summary Statistics

We transform the source data into our analyzed variables to start with, and **Table 2** summarizes panel statistics of our analyzed variables. We first deflate nominal income and fuel prices to obtain real income and fuel prices. The deflator was calculated from national level GDP at current and constant prices from World Development Indicators, with the deflator equals to 1 in 2010, the starting period of our analysis. We applied these year-specific deflators to the income and prices of each FU in the corresponding year. In other words, the prices and incomes used for our analysis are constant at the price level of 2010. Second, we average the fuel prices of gasoline C, diesel B, and hydrous ethanol to represent the general price level of automotive fuels that captures price information from all major fuels, as well as modelling individual fuel price. To derive the emission intensity (t CO2e per million R$), we divided the aggregate CO2 equivalent (t) emissions from road transportation by real income. The CO2 equivalent considers all GHG emissions from road transportation based on their global warming potential as equivalent to CO2, while 11
for these differences, we expect that the introduction of transfers in PSNP districts would keep demand for goods high mitigating any price impact of lower demand in the wake of a shock. This, however, requires that there were no other reasons for the relationship between weather shocks and prices to be changing differently in PSNP districts, and this is not something we can assume. The results of this analysis cannot determine whether it is the introduction of the safety net that has driven any observed changes.

# **Question 4: Do weather shocks impact local wages for unskilled labor?**

In addition, we examine whether the wage data recorded for unskilled workers in the monthly retail market survey indicates weather shocks having an impact on local labor markets. In some contexts, the negative shock to farm incomes that a weather shock results in increased supply and reduced demand for labor, both of which depress wages (Deveraux 2007). Labor supply increases as households seek to make up agricultural income losses and demand for labor falls as households have reduced ability to hire labor. If local labor markets are limited, as in rural Ethiopia where farms and small business rely on family labor, we may expect both effects to be small. Therefore, we anticipate the negative effect on wages to be negligible in rural Ethiopia, contrary

> 11 Administratively, Ethiopia is divided into nine regions/ _kilils_ (Tigray, Afar, Amahara, Oromia, BenishangulGumuz, Gambella, Harari, Somali and Southern Nations, Nationalities, and Peoples’ Region (SNNP)) and two autonomous cities (Addis Abeba and Dire Dawa). These regions and cities are divided into _zones_ or sub-cities, which are further divided into districts ( _woredas_ ).

9
**Figure 1:  Probability of Survival to Age 5**

<!-- Start of picture text --> Probability of Survival to Age 5<br>RWASLBNPLVUTTJKBGDFSMKHMSTPTUVKGZMHLSYRPSEHNDNICMDATONWSMINDVNMBOLCPVGTMGUYPHLMARJAMBTNSFJIBPRYARM L UZBXKXUKRVZGEOVCTDMAJORLCATUNEGYIDNECUBIHLKACUBALBPERMNGZAFGRDNRUCOLDZAIRQLBNBRAMKDDOMSRBCHNVENSURBRBTHAMDVPLWCRIAZEMNEBWAMEXIRNBLRBGMUSARGTURUPANCBATG R HRVKAZ R KNA H MYSYOULVAUSGRCPOLSYCLSHUNESTPRTCYPLTUTTOSVKSVNCZEISRMLTNZLESPJPNITAKOROMNFRFINGBRBHRCANAUSTWNBEDNKDEUISLSWE A SAUNUTHKG L USASMRDCHEKWTIRLBRNNORARESGPMACLUX QAT<br>MDG ZWEUGATLSSENKENGHACOGMMR NAMGAB TKM<br>MWI KIRYEMTZA PNG SWZ<br>BDI ETHGMB DJIZMBSDN LAO<br>LBR MOZCOMTGOHTIAFG PAK<br>NER GNBGINBFA LSOCMRMRT AGO<br>COD CIV GNQ<br>SSDBEN NGA<br>MLI<br>SLE<br>CAF TCD<br>6 8 10 12<br>Log Real GDP Per Capita at PPP<br>Note: Dataset version 21 Sept 2018<br>1<br>.95<br>.9<br>Probability of Survival to Age 5<br>.85<br><!-- End of picture text --> Notes: Probability of survival until age 5 is one minus the under‐5 mortality rate. Estimates of under‐5 mortality rates are taken from the UN Inter‐Agency Group on Child Mortality Estimation (www.childmortality.org), and supplemented with data provided by World Bank staff. Real GDP per capita adjusted for differences in purchasing power parity is taken from the Penn World Tables (Feenstra et. al. (2015)), with missing countries filled using data from World Bank estimates of GDP at PPP. Graph shows the most recent data for all countries.

10
2023<br>(ETH)|

Note: The table summarizes the empirical hypotheses we test in the vaccine survey data, the associated prediction(s) and survey data source. It also indicates the reference administrative coverage rate to which estimates can be compared. The date of the administrative rate refers to the admin data point closest to the time of survey completion. GP = General population, i.e. the entire population irrespective of age; AP = Adult population, i.e. the coverage rate calculated for the population aged 15 and above by assuming no vaccinations among younger persons.

8
export promotion agencies. Many of the policies described here may appear to be untargeted towards any sector, and so one might argue they are not industrial policy per se. Yet, even if these policies can be applied to the whole economy in principle, governments with limited resources make choices about who will be the main beneficiaries, and tailor these policies to address specific constraints in those beneficiaries’ industries. In practice, selected beneficiaries of these policies are often those in sectors with international market growth, weak international competition, technological relatedness, and/or comparative or competitive advantage.

# _Different Rules for Developing Countries_

An export-led development strategy requires access to global markets, and market access requires good relations with trading partners and continued engagement in trade agreements. But while developing countries have benefited from participation in the world trading system, there is an irony. The rules of the trading system are biased against them, both de jure and de facto.

De jure, developing countries still face higher tariffs compared to wealthy countries. Calculations of actual tariff rates include preferences under bilateral and regional trade agreements (such as a free trade area or customs union) and provisions like most-favored nation status. Using the 2017 tariff data from the World Integrated Trade Solution (WITS), which are complete for almost all countries, the pattern that has emerged over time is that for nonagricultural goods (HS Chapters 25–89), value-weighted applied tariffs are 1.4 percent for low-income countries and are 2.1 percent for high-income countries, using the World Bank income classification. They are largest for lower-middle income countries at 2.5 percent. Lowincome countries retain some market access privileges given their income status, but these privileges are not available to middle-income countries. Trade agreements have allowed countries to retain higher tariffs especially in agriculture, which is the comparative advantage of many developing countries. Guatemala’s national development strategy is direct about the issue (República de Guatemala 2023, p. 472): Despite the positive results shown by exports in the last
to measure a country’s statistical _performance_ through objective and comparable indicators. (This challenge is highlighted by a large number of indicators with missing data that we discuss later. Also see Cameron _et al._ (2021) for further discussion on this and the _desiderata_ .) We identify five key pillars of a country’s statistical performance, as shown in Figure 7. These are data use, data services, data products, data sources, and data infrastructure, which can be further disaggregated into 22 dimensions. This figure shows these pillars and dimensions in the form of a dashboard, which can help countries identify areas for development in their statistical system. We briefly describe these pillars below and provide more details on the dimensions of the SPI, including ongoing data work, in Appendix A, Table S1.

Since statistics have no value unless they are used, the first pillar of the SPI is data use. In order to meet user needs, the statistical system must develop a range of services that connect data users and producers and facilitate dialogue between them. The second pillar of the SPI is therefore data services that are trusted by users. The dialogue between users and suppliers in turn drives the design of statistical products that are to be created including the quality of product needed for the country requirement. This will incorporate accuracy, timeliness, frequency, comparability, and levels of disaggregation. The third pillar of the SPI is therefore data products. In order to create the products required, the statistical system needs to make use of a variety of sources from both inside and outside the government. This includes making use of typical data collection methods like censuses and surveys, but also administrative data, geospatial data, and data generated from the private sector and from citizens. The fourth pillar of the SPI is therefore data sources. For the cycle to be complete, capability needs continuously to be reviewed to ensure that it is enough to deliver the products, services and ultimately data use required. The fifth pillar of the
|(0.042)|
|**Average **|**-0.014**|**0.019**|**-0.023**|**-0.013**|**0.069**|**-0.085**|

Notes: Each column reports the coefficient in the regressions of ln (monthly earnings) on (a dummy for) a type of teachers with controls. The comparison group are other wage workers in columns 1-5 and other public sector employees in column 6. Controls include gender, age, age-squared, urbanicity and (a dummy for) post-secondary education. Sample is restricted to workers with secondary education or more. The survey in Malawi did not differentiate the levels of teachers. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Surveys listed in Table 1.

41
security outcomes. The key parameters of interest are thus stacked in , the interaction effects of being from the affected region and the post-war-onset month dummies. As we are following mm γγ households for several months, unobserved effects (error terms) can be correlated across time as well as across households living in the same district. Hence, we cluster standard errors at district ( _woreda_ ) level. We also consider alternative definitions of conflict exposure and control group assignments as defined above. Thus, we estimate Equations (1) and (2) for the full sample as well as for a restricted sample consisting of households from the highland regions of Ethiopia.

As noted above, the first two approaches consider Tigray as the treatment region and the rest of the country as a comparison group. This assumes all survey households from Tigray have been affected by the war. Given the depth and breadth of the conflict, this is a reasonable working assumption. It is possible, however, that some survey households were completely immune from impacts of the war. With exposure to war so defined, our estimates measure intention to treat (ITT) impacts. If households who were not affected by the conflict were rather erroneously considered to have been impacted by it, these estimates would be smaller than the average treatment effect (ATE) of the war.

We generate more granular measures of exposure to violent conflict using ACLED’s battle events recorded between August 2019 and May 2021, which coincides with our pre-war and postwar-onset HFPS data. Given the intensity of conflict, exposure is computed by counting the number of battles that took place within 20 and 30 km radius around the homestead of households. These allow us to identify the potential heterogeneities in impacts associated with the intensity of exposure to battles. It also serves to probe the robustness of our main results. We estimate the following specifications: _h_ r h 1 _h_ r _h_ r (3) mm YY = αα + αα + φφ BBW WWBBWWBB + ωω where _h_ r stands for the cumulative number
only in one country—Nigeria—do we observe average absence below 30 percent. Being absent from school is about as common as being present in the school but absent from class. The rank correlation coefficient between the two measures is less than 0.5 at the country level, making the school absence rate at best a partial measure of teacher effort (Table 2). This is most starkly illustrated in the cases of Kenya and Tanzania (2014), both of which have relatively low school absence rates (15 percent) but relatively high classroom absence rates conditional on being in school (38–39 percent).

When a large share of teachers is not teaching, unsurprisingly, a large share of classrooms will be occupied by only students. Consistent with the absenteeism findings discussed above, we find, averaging across countries, that one-third of the classrooms were “orphaned” classrooms, where students are present but there is no teacher.

Over time in these countries, the absenteeism rates appear remarkably stable. Chaudhury et al. (2006) estimated a school absence rate of 27 percent in Uganda in 2002–03, which compares to our measure of 30 percent in 2013. Similarly, while absence from school fell by a third in Tanzania between 2010 and 2014, this was largely offset by an increase in absence from the classroom conditional on being in school; the net result being a small decline in absence from class between the two surveys.

What do these results imply for the amount of instruction time that students receive? To answer this, the surveys first recorded the scheduled time of a teaching day—after break times—according to school records. Averaged across schools and countries, this comes to 5 hours and 27 minutes. We then multiply this number by the proportion of teachers absent from class. If 10 teachers are supposed to teach 5 hours and 27 minutes per day, yet 4 teachers are absent from either the school or the classroom at any one time, then the scheduled teaching time is reduced
status while maintaining the same mediator value, and the overall Average Treatment Effect (ATE).<sup>12</sup>

# **5 Results**

In this section, we present our findings on differences in content sharing in return and non-return areas and areas with and without IDPs. In Section 5.1, we assess differences in the prevalence of text topics and image clusters.<sup>13</sup> We add a time element by running a mixed effects model on topic prevalence pre- and post- return to determine if return coincides with changes in discussion in Section 5.2. In this section we also consider quantity of displacement from and return to a region and how this is associated with discourse. Finally, we consider the role which social media information may play in return decisions using mediation analysis.

Overall, we see that areas with return discuss local governance, job opportunities, and non-operational war issues more and differences are statistically significant at the 99% level accounting for Bonferroni correction for multiple hypothesis testing.<sup>14</sup> In the `Location Mentions` dataset in Figure 3, the topic related to the economy is nearly 75% more prevalent in areas with both returnees and IDPs as compared to those with neither (+0.011 points) and the one related

> 11Higher month cutoffs lead to similar results. Two was chosen as the cutoff to minimize loss of data while keeping the violent events quantile measure meaningful.

> 12When including interaction terms, the mediation package creates confidence intervals using QuasiBayesian estimation and robust standard errors (Imai et al. (2011)).

> 13 `Researcher Coded` analysis is in Appendix 9.1.

> 14The 99% significance level applies for all results discussed in the remainder of this section using Bonferroni correction for multiple hypothesis testing unless otherwise noted.

14
*_0.2_**|**_41.8_**|**_10.2_**|
|Kenya|24.9|48.4|26.6|0|42.4|57.6|0|_57.7_|0.2|33.7|8.4|
|Ethiopia|56.4|0|0|44|100|0|0|_30.3_|0|56.4|13.4|
|_Chad border to Djibouti_|**_9.4_**|**_23_**|**_53.1_**|**_15_**|**_43.4_**|**_56.6_**|**_0_**|**_61.3_**|**_24.5_**|**_7.4_**|**_6.9_**|
|Sudan|8.9|18.8|72.3|0|41.1|58.9|0|_74_|15.4|10.6|0|
|Ethiopia|13.9|42.2|11.1|33|33.6|66.4|0|_41.1_|58.9|0|0|
|Djibouti|0|0|0|100|100|0|0|_0_|0|0|100|

Source: AICD calculations. * Denotes portions of the TransAfrica Highway in EAC.

** No data for Sudan available for this route.

Data from 2006 illustrate the significant variation in traffic across key regional corridors. There is heavy traffic around the northern corridor that connects Kigali, Kampala, Nairobi, and Mombasa. This corridor routinely carries more than 1,000 vehicles per day. The central corridor between Bujumbura Dar es Salaam, and the corridor connecting Addis Ababa to Djibouti, see moderate traffic of
3 that the ratio of urban mean to the rural mean has been rising since around 1970 (the early observations were volatile, and should probably be discounted), though with signs of levelling off and even decline since 2000. Controlling for MRP rounds, time trends in the ratio of real urban-to-rural mean consumption are similar pre- and post-1991, with the ratio increasing at about 0.14% per annum in both periods. So the higher growth in the post-1991 period has come with generally rising consumption inequality, especially within sectors, but also to some extent between them.

Datt and Ravallion (2011) also found higher trend rates of poverty reduction in the post1991 period, but the differences (compared to the pre-1991 period) were only statistically significant for the headcount index (nationally and for urban areas). Our new series provides a more statistically robust indication of acceleration in progress against poverty post-1991 for both sets of poverty lines (Table 1). Over the 55-year period as a whole, the exponential trend decline (for the higher line) was 1.3% per annum for the headcount index, rising to 2.3% and 3.0% for

> 39 The standard errors are 0.1% and 0.3% respectively.

> 40 The standard errors are 0.05% and 0.2% respectively.

12
9 schooling begins _<u>a</u>_ <u>, and the probabilities for schooling</u> _πs_ ( _·_ ) and work _πe_ ( _·_ ).

We obtain the population growth rates _gb_ and the mortality functions _δ_ ( _·_ ) from United Nations (2019). We use the medium variant forecasts over the 2020 _−_ 2050 period, assuming that ̄ _a_ = 100. Thus, _δ_ ( _a_ ) = 1 for _a ≥_ 100.<sup>16</sup> We assume schooling starts at age _<u>a</u>_ = 6. Agents enter the work force after age _aw_ = 18 unless they are still in school. We then set the schooling transition probabilities _πs_ ( _·_ ) so as to exactly match the schooling distribution for the relevant country group, based on data from the I2D2 database, the World Bank database of more 1,000 household surveys that we use to estimate the returns to education and experience (see below).

We make the following assumptions regarding the probability of working _πe_ ( _·_ ). We assume that agents may start work once they complete schooling, or reach 18 years of age, whichever is later. At that point, a share _lp_ of agents participate in the labor force. Those that participate face youth unemployment with probability _uy_ each year until they are 24. Above 24 they face unemployment each period with probability _u_ , until they retire at age _R_ = 65. We assume unemployment and participation are equal across schooling and age groups. We measure _lp_ , _u_ and _uy_ for each group using data from World Bank (2020b). Data are based on population-weighted means.

Now we turn to the selection of the returns to schooling and experience. We model _rs_ ( _·_ ) and _re_ ( _·_ ) as step functions so as to capture salient features of the data with few parameters, in order
, if they do apply; collateral requirements; and the loan amount received, conditional on successful application.

To measure differences by gender at the firm level, most studies use the gender of the firm owner, sometimes even restricting to sole proprietorship firms (e.g., Alesina et al. (2013)). Other studies define a threshold of female ownership such as considering a firm as female-owned when at least one of the owners is a woman (e.g., Aterido et al. (2013)).<sup>6</sup> However, female ownership does not necessarily reflect the participation of women in the decision-making process, especially in large firms with many owners (Piras et al., 2013). In order to capture the role of women in managing the performance of a firm, we take advantage of the information on the gender of the firm’s top manager available in the WBES.

> 5 WBES micro-data, including a comprehensive cross-country harmonized dataset, are publicly available at https://www.enterprisesurveys.org/en/enterprisesurveys. We use the last comprehensive dataset released on June 2023 containing information for the period 2008-2023.

> 6Piras et al. (2013) highlight that restricting the analysis to sole proprietorship reduces the sample size and the types of firms. In addition, using a threshold to classify a firm as female-owned implies a degree of arbitrariness.

4
water source, improved sanitation facility and secondary school attendance over four waves of DHS data.<sup>28</sup> The trend in the share of the population with access to improved sanitation and the share enrolled in secondary school – shown in Panel c and Panel d of Figure _8_ – are particularly striking. There was a remarkable improvement in all these indicators between 2003 and 2008, but virtually no change between 2008 and 2018, in line with the evidence emerging from this paper of a slowdown in household welfare improvements and poverty reduction in more recent years.

# _<u>7.4. Changes to PPP conversation factors</u>_

Changes to the PPP conversion factors used for international comparisons could alter estimates of the poverty headcount rate at international poverty lines. This paper has used PPP conversion factors based on price data collected in 2011. However, Jolliffe et al. (2022) show that using new PPP conversion factors, created using price data collected in 2017, would have a large impact on estimates of the poverty headcount rate in some countries, including Nigeria. In order to understand these implied differences in the poverty estimates, further analysis is needed to examine how and why the new 2017 PPP data affect the conversion of the welfare vector from local currency units to international US$. This is left for future work.

# 8. Conclusion

Since the last two official household consumption surveys in Nigeria cannot be compared, this paper estimates a poverty trend for the country using two alternative approaches. First, the paper uses sectoral GDP data to backcast household consumption and hence poverty from the 2018/19 NLSS to 2009. Second, the paper uses a survey-to-survey imputation approach, constructing a model for consumption using data from the 2018/19 NLSS and imputing into several waves of the GHS. Despite having very different foundations, these two approaches produce remarkably similar results. Far from poverty dropping by 17 percentage points – as making the invalid comparison between the 2009/10 HNLSS and 2018/19