rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 7. Activity in oil-importing economies**

## **A. Share of advanced economies with increasing/decreasing growth**

## **B. Share of oil-importing EMDEs with increasing/decreasing growth**

<!-- Start of picture text --> Share of advanced economies Share of oil-importing EMDEs<br>100 Increasing Unchanged Decreasing 100 Increasing Unchanged Decreasing<br>75 75<br>50 50<br>25 25<br>0 0<br>GDP Investment Consumption Exports GDP Investment Consumption Exports<br>C. Consumption by fuel type, 2016 D. Contribution of mining investment to U.S.<br>GDP growth and U.S. industrial production<br>growth<br>Percent of Oil Natural gas Percentage point contribution Percent<br>total energy Coal Hydroelectric 0.3 Mining investment 12<br>100consumption Nuclear energy Renewables 0.2 Industrial production (RHS) 8<br>80 0.1 4<br>60<br>0.0 0<br>40<br>20 -0.1 -4<br>0 -0.2 -8<br>-0.3 -12<br>2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017<br>Brazil Mexico Indonesia Advanced economies Turkey India South Africa China 2000 2002 2004 2006 2008 2010 2012 2014 2016 2017<br><!-- End of picture text -->

**C. Consumption by fuel type, 2016**

**D. Contribution of mining investment to U.S. GDP growth and U.S. industrial production growth**

Sources: BP Statistical Review, Federal Reserve Bank of St. Louis, World Bank.

A. B. Aggregate growth rates calculated using constant 2010 U.S. dollar GDP weights. Increasing/decreasing growth are changes of at least 0.1 percentage point from the previous year. Countries with a slower pace of contraction from one year to the next are included in the increasing growth category.

C. Oil consumption is measured in million tonnes; other fuels in million tonnes of oil equivalent. Renewables are based on gross generation from renewable sources including wind, geothermal, solar, biomass, and waste, but not accounting for crossborder electricity supply.

D. Mining investment is real private fixed investment of nonresidential structures for mining exploration, shafts, and wells.

21
The instrumental-variables method to identify the effects of refugees is preferable over the DD method for three reasons that are noted by Del Carpio and Wagner (2015). First, regions bordering Syria are more likely to be affected economically by the war in Syria through trade channels. Second, these regions tend to have lower income than the northern and western regions of Turkey and any ongoing income convergence between regions may influence the results of the DD methodology. Finally, recent reforms -particularly the raising of compulsory education ages from 8 to 12 years- is more likely to affect poorer regions near the Syrian border. However, we control simultaneously for the distance to the closest border crossing of each province, which limits the variation exploited by the instrument to the potential use of different border crossings by refugees coming into Turkey. The instrument is therefore likely to be exogenous as long as the control for the distance to the closest border crossing captures effects from proximity to Syria.

We instrument the number of refugees in a province by a weighted average of distances between 13 Syrian governorates and 81 Turkish provinces. Google Maps was used to calculate the distances between each province and border crossing as well as the distance between each Syrian governorate’s capital and border crossing. We use the same formula as Del Carpio and Wagner (2015) to construct the instrument which is given by equation 2. The main difference is that we use data at the level of 81 provinces corresponding to the NUTS-3 level while Del Carpio and Wagner (2015) uses data at the level of 26 NUTS-2 regions.<sup>3</sup> In equation 2, _Tsi_ is the shortest travel distance between each Syrian governorate and Turkish province in kilometers, _πs_ is the fraction of the Syrian population living in a governorate prior to the civil war in 2010 and _Rt_ is the total number of refugees in Turkey in 2014, the only year for which we have complete information on the number of Syrian refugees in all provinces. Since the closest border crossing from a Turkish province is not necessarily the border crossing on the shortest routes between all Syrian governerates and the province, the IV approach allows us to simultaneously control for the distance to the closest border crossing in the estimations. The specification estimated by 2SLS is 8
of subsidized inputs that a household receives. As such, the quantity of subsidized inputs that a household receives is likely to be endogenous in a commercial fertilizer equation. This implies that, in terms of the empirical models, could be potentially correlated with and/or . yyii yy yyii Failure to control for such correlations could potentially result in inconsistent estimates of SS aa μμ crowding out.

This study uses the Mundlak-Chamberlin device to account for the potential correlation between and , and the control function approach (CF) to account for the correlation between yyii yy and SS (Mundlak, 1978; Chamberalain, 1984). The Mundlak-Chamberlin device is used aa yyii yyii SSinstead of household fixed effects because many farmers in Malawi do not use fertilizer in crop μμ production, so the data take on properties of non-linear corner solutions. The implementation of the Mundlak-Chamberlin device involves the inclusion of a vector of variables consisting of the household-level means of time-varying covariates. The CF approach in this case involves estimating a reduced form model of quantity of subsidized fertilizer acquired and including the residuals from this model as an additional explanatory variable in the commercial fertilizer model. The significance or otherwise of the coefficient on this additional explanatory variable tests and corrects for the potential correlation between and . yyii yyii The CF approach requires an instrumental variable in the reduced form model of subsidized SS μμ fertilizer. The instrumental variable should strongly correlate with but be uncorrelated with yyii yyii in the commercial fertilizer model when the other covariates are controlled for. Following Ricker-SS μμ Gilbert et al. (2011), the number of years that the household head has been living in the community is used as the instrumental variable. This variable is used because it represents a “sociopolitical capital” that could affect the quantity of subsidized fertilizer received by a household (RickerGilbert et al., 2011). Also, after conditioning on other covariates, it is unlikely that the number of 17
6

# **II.** **<u>Data description</u>**

The paper uses three main data sources: (i) articles to construct country-specific newssentiment indexes (ii) asset prices and related high-frequency financial variables (such as trading volumes or liquidity measures) and (iii) capital flows data. We detail them in turn.

# **A.** **<u>News articles and Sentiment measures</u>**

# **1.** **<u>News articles</u>**

Our dataset of news articles was acquired from Factiva, a leading provider of news archives. We focused on articles published by Reuters in English, as it offers the most consistent coverage over time and across countries. Each article contains a timestamp, a title, a main text, as well as topics and locations tags generated by Factiva using a proprietary algorithm. We selected all the articles tagged with either “Economic News” or “Financial Market News”, as well as with at least one of the countries included in our sample. Our dataset contains close to 4.5 million daily articles covering 25 countries - 9 Advanced Economies (AEs) and 16 Emerging Markets (EMs) - from 1991 to 2015.

Summary tables and figures reporting (i) the start (and end) of the news coverage by country (ii) the number of articles by country and (iii) the main topics covered in our corpus are provided in Appendix. Overall, the articles we use cover a wide range of economic news (economic policy, government finance etc..), financial news (commodity markets, equity markets, forex etc..), corporate news and political news. The distribution of topics is similar in AEs and in EMs. Quantitatively, US-related articles represent one fourth of our sample (200 articles per day).<sup>4</sup> The distribution of non-US country tags is more balanced, with an average of 97,000 articles per country over the whole sample (20 articles per day).

# **2.** **<u>News-Sentiment measures</u>**

To measure sentiment, we use pre-existing dictionaries of tonal words. Given the range of topics covered in our corpus of news, we first combine dictionaries of positive and negative words compiled by Loughran and McDonald (2011) for financial texts and by Young and Soroka (2012) for political and economic texts. Next, for each word in a dictionary (e.g. “lose”), we collect all of its inflections (“losing”, “loser”, “lost”, “loss”, etc.) that are present in our corpus and include them in the dictionary, obtaining a list of 7,217 negative words and 3,250 positive words. Table 1 reports the most frequent tonal words in our corpus, illustrating that the most frequent positive and negative words we selected reflect the sentiment typically associated with economic or financial outcomes.

> 4 Note that articles can tag multiple location and topics at the same time. See Appendix for details and next section for an example.
- 37  Szilágyi T. Higher cigarette taxes–healthier people, wealthier state: the Hungarian experience. _Central European journal of public health_ 2007; **15** .

- 38  Yürekli A, Önder Z, Elibol M, _et al._ The economics of tobacco and tobacco taxation in Turkey. _Paris: International Union Against Tuberculosis and Lung Disease_ 2010; **5** .

- 39  Ciecierski CC, Cherukupalli R, Weresa MA. The economics of tobacco and tobacco taxation in Poland. _Paris: International Union Against Tuberculosis and Lung Disease_ 2011;:8–11.

- 40  Selvaraj S, Srivastava S, Karan A. Price elasticity of tobacco products among economic classes in India, 2011–2012. _BMJ open_ 2015.

- 41  Van Walbeek CP. The distributional impact of tobacco excise increases. _South African Journal of Economics_ 2002; **70** :258–267.

- 42  Lee J-M. Effect of a large increase in cigarette tax on cigarette consumption: an empirical analysis of cross-sectional survey data. _Public health_ 2008; **122** :1061–1067.

- 43  Centers for Disease Control and Prevention (CDC. (1998). Response to increases in cigarette prices by race/ethnicity, income, and age groups--United States, 1976-1993. MMWR. Morbidity and mortality weekly report, 47(29), 605.

- 44  Xu X, Bishop EE, Kennedy SM, _et al._ Annual healthcare spending attributable to cigarette smoking: an update. _American journal of preventive medicine_ 2015; **48** :326–333.

- 45  World Health Organization. _WHO technical manual on tobacco tax administration_ . World Health Organization 2010.

- 46  Lightwood J, Collins D, Lapsley H, _et al._ 4 Estimating the costs of tobacco use. Published Online First: 2000.

- 47  Pichón Riviere A, Bardach A, Caporale J, _et al._ Carga de Enfermedad atribuible al Tabaquismo en Chile. _Documento Técnico IECS_ 2014.

- 48  Fuchs Tarlovsky A, Meneses FJ, others. Are tobacco taxes really regressive? evidence from Chile. The World Bank 2017.

- 49  Mason J, Wheeler W, Brown MJ. The economic burden of exposure to secondhand smoke for child and adult never smokers residing in US public housing. _Public Health Reports_ 2015; **130** :230–244.

- 50  Pichón Riviere A, Bardach A, Caporale J, _et al._ Carga de Enfermedad atribuible al Tabaquismo en Chile. _Documento Técnico IECS_ 2014.

- 51  Coady DP, El Said M, Gillingham R, _et al._ The magnitude and distribution of fuel subsidies: evidence from Bolivia, Ghana, Jordan, Mali, and Sri Lanka. Published Online First: 2006.

- 52  Kpodar K, Djiofack C. The Distributional effects of oil price changes on household income: Evidence from Mali. _Journal of African Economies_ 2009; **19** :205–236.

- 53  Vassall A, Chechulin Y, Raykhert I, _et al._ Reforming tuberculosis control in Ukraine: results of pilot projects and implications for the national scale-up of DOTS. _Health policy and planning_ 2008; **24** :55–62.

23
The gravity model of trade that relates bilateral trade flows between two countries to their economic size and variable trade costs is one of the most successful empirical models in economics (Anderson, 2011). Most studies focus on aggregate bilateral trade flows between countries but a few recent studies have begun to delve into the role of firms for the gravity equation but focusing on single countries only.<sup>47</sup> An exception is Mayer and Ottaviano (2008), who examine data from a handful of European countries. Using measures in the Database at the country-year-destination level, we provide estimates of a gravity equation for our large sample of countries (and their trading partners) that allow us to examine whether the effects of the classical determinants of bilateral trade –economic size and proxies for trade costs (distance and tariffs) along with the level of development– operate through firm export participation or through the average value exported per firm.

We decompose exports from country _i_ to partner country _j_ for any given year (ignoring the year subscript) into the product of nij the number of country _i_ firms exporting to country _j_ (extensive margin) and sij = Xij⁄nij the average exports per firm for firms that export from country _i_ to country _j_ (intensive margin): Xij = nij ∗(Xij⁄nij). The measures corresponding to the three elements in this decomposition are available in the Database at the country-yeardestination level (CYD.dta) and are used in turn as dependent variables in the gravity equations whose estimates are shown in Table 9. Data on bilateral distances is taken from CEPII described in Mayer and Zignago (2011), and data on bilateral tariffs from Kee, Nicita, and Olarreaga (2009).<sup>48</sup> Since OLS estimation is used in Table 9, the coefficient on an independent variable in

> 47 For example Bernard, Jensen, Redding, and Schott (2007) and Lawless (2010) examine bilateral U.S. exports, Bastos and Silva (2012) examine bilateral Portuguese exports, and Eaton, Kortum, and Kramarz (2011) examine bilateral French exports. 48 We calculate the average bilateral tariff faced by origin country A when exporting to destination country B as the simple average of the applied tariffs imposed by B on all HS 6-digit products it imports from A in 2008 (the only year for which we have available data).

34
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# **FIGURE 3**

# **The Relationship Between Inequality in 1987-88 and Subsequent Inequality**

<!-- Start of picture text --> 0 .05 .1 .15 .2<br>Village Inequality in 1987-88 (Mean Log Deviation)<br>1990-91 1995-96 1997-98 1999-00 2001-02 Fitted values 45 degree<br>.4<br>.3<br>.2<br>.1<br>Village Inequality (Mean log Deviation)<br>0<br><!-- End of picture text --> _Notes:_

- 1) This graph plots village-level inequality in subsequent time periods against initial inequality in 198788.

- 2) We show the 45-degree line along which villages would be if inequality were unchanged over time.

- 3) We also show the fitted values of a regression of inequality in the final period (2001-02) as a function of inequality in 1987-88.
Overall, one may conclude that networks, as constructed and interpreted in this paper, are all about specificity. The fact that the province is the most relevant layer favors an interpretation of spill-overs as being informal rather than institutional (the associations are national and there tend to be only few province-level activities). Moreover, the temporal specificity points to a potentially time-sensitive nature of the information embedded in such networks – it appears to be more relevant to an export’s survival whether the firm’s peers were active that same month than if they were at any other time in the same year. Lastly, the product specificity is indicative of the level of detailed knowledge which is required in order to be successful overseas.

# 5.3 **Competing Risks Model**

As outlined before, the end of an FPD is not a desirable outcome for the firm, and policy is interested in prolonging it for obvious reasons. There are, however, occasions when an FPD is not merely ending but rather transformed into something better: a customer can start asking for a more complex product and terminate orders of the current one. The introduction of the concept of evolution, rather than death, naturally lends itself to a competing risks analysis. Table 5 presents the results for a three-choice, multinomial model: continue exporting (censored observations), terminate, and terminate by replacement with an upgrade. Both models replicate specification (4) which was presented in Table 2<sup>14</sup> . Model (1) defines upgrades as subsequent FPDs by the same firm to the same destination with a higher PRODY, whereas model (2) provides an alternative definition: the subsequent FPD must involve a product with a higher average selling price (adjusted by weight).

The coefficients report a change in the odds of experiencing the particular event (death or upgrade) with respect to the reference category, which in both cases is “continuation”. One can see that the variables of interest affect the choice differently in the majority of cases. In the few instances where the difference in coefficients is not statistically significant (as indicated by the column _diff_ in each of the two models) this is attributable either to the effect being the same (rendering the choice irrelevant) or to a low statistical significance in one or the other of the two outcomes. The two models for the two definitions of upgrades (PRODY and price) paint a similar picture despite these definitions being very different. This can be interpreted as a sort of robustness check for the choice model and its construction.

The sign of the impact tends to be the same for both outcomes (death and upgrade), an effect which is akin to a “trap” or a “gilded cage”, whereby survival is enhanced but upgrading is stifled. In particular, this is true for the coefficients of _peers_ , where one interpretation can be that peer pressure acts as a deterrent to change. Also, familiarity

> 14 Taking unobserved heterogeneity into account is not possible with _mlogit_ in Stata.

25
NEW ZEALAND

# 2. Projections **NRR-1** by 2030

|**_To_**<br>**PfpUatimn**|IW.<br>ratio|Perfiod|**Growth**<br>rate|**SO**<br>**CII**|Cm|**TFR**|_Nlo_|'O4<br>**Fmtes**|**tR**|u1000|
|---|---|---|---|---|---|---|---|---|---|---|
|1990<br>3394|50.4|1990-94|0.67|15.4|8.1|1.92|72.9|78.9|10|12|
|1995<br>3509|50.3|1995-99|0.65|14.5|6.0|1.92|73.9|80.0|8|11|
|2000<br>3625|49.6|2000-04|0.59|13.8|7.9|1.94|75.0|81.1|7|9|
|2005<br>3734|49.2|2005 09|0.51|13.1|8.1|1.97|75.7|81.8|6|8|
|2010<br>3830|49.4|2010-14|0.46|12.8|8.2|1.99|76.5|82.5|6|7|
|2015<br>3919|52.2|2014-19|0.43|12.6|8.3|2.02|77.2|63.3|5|6<br>|
|2020<br>4004|55.2|2020-24|0.38|12.5|8.1|2.04|78.0|84.1<br>|4<br>|6<br>|
|2025<br>4081|59.2|2025-49|<br>0.14|12.0|10.6|2.07|79.6|85.8|3|4|
|2050<br>4224|71.4|2050-74|0.00|11.9|i'.9|?.07|81.0|87.6|2|4<br>|
|2075<br>4225|72.3|2075-W|0.03|11.8|11.5|2.06|81.8|**U.6**|2|3|
|2100<br>**420**|73.5|<br>2100-24|0.03|11.7|11.4|2.06|82.4|89.3|2|3|
|2125<br>4287<br>2150<br>4303|74.1<br>74.6|2125-49|0.01|11.6|11.5|2.06|62.6|89.6|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75+<br>e za 1 ~~~70-74 § E<br>65-69<br>a0-64<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>RRRERS BEE2gl 25-29 05 B<br>20-24<br>15.19<br>10-14<br>5-9 I<br>0-4j<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 10<br>4. Comparison of Country with Region and Income Group, 1985-90<br>TFR eO Growth rate<br>.~~~~~ .. . - -|....<br>Country Region Incom groW Country Region Income grai Country Region Income group<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income Group, 1985-90

87
expansion in household survey data collection for developing countries since the 1980s. And coverage deteriorates in the last year or two of the series, given the lags in survey processing.

Two guides to the reliability of our estimates are to count the number of surveys by year and to measure the coverage rate. Figure 3 gives the number of surveys; we give the three-year moving average centered on each year (given that having a survey last year or next year can help greatly in estimating poverty this year). For comparison purposes, we also give the numbers of surveys used by Chen and Ravallion (2004). By this measure, our estimates around the mid 1990s onwards are clearly the most reliable while our estimate for 1981 is the least reliable. We have a cumulative total of only 18 surveys up to 1983, though the number doubles by 1985. By contrast we have a total of 480 surveys after 1993. Naturally the number of surveys drops off in the last year or so, given the lags in availability; there has been a marked improvement in the coverage of recent surveys, though this partly reflects our unwillingness to make an estimate yet for 2006 (as we still only have seven surveys for that year, at the time of writing).

Most regions are quite well covered from the latter half of the 1980s (East and South Asia being well covered from 1981 onwards).<sup>29</sup> Unsurprisingly, we have weak coverage in Eastern Europe and Central Asia (EECA) for the 1980s; many of these countries did not officially exist then. More worrying is the weak coverage for Sub-Saharan Africa in the 1980s; indeed, our estimates for the early 1980s rely heavily on projections based on distributions around 1990. Table 1 gives the average survey year by region for each reference year.

By comparing Table 1 with the corresponding table in Chen and Ravallion (2004) we can see how much the lags in survey data availability have fallen. Like the present paper, Chen and Ravallion (2004) reported results for a reference year that was three years prior to the time of writing (namely 2001, versus 2005). Table 2 gives the average lag by region (where zero means no lag for the latest reference year). The overall mean has fallen by one year (1.6 to 0.6 years); for East Asia (the lowest mean lag for 2001), the average lag is down to almost zero; for SSA (the highest lag in 2001), the lag has also fallen appreciably, from 4.0 to 1.5 years, and MENA is now the region with the highest mean lag.

Note that the lags in Table 2 reflect both the frequency of surveys and our access to the data. Based on our observations in assembling the data base for this study, we would conjecture

> 29 China’s survey data for the early 1980s are probably less reliable than later years, as discussed in Chen and Ravallion (2004) where we also describe our methods of adjusting for certain comparability problems in the China data, including changes in valuation methods.

17