rafmacalaba/datause-encoder-data huggingface dataset:
For the sample starting in January 2010 to January 2020, the average number of protests per month per country is 21. In 34 percent of country-month pairs (2,910 of 8,549), there is no occurrence of protest. Panel A of Appendix Figure A1 presents the histogram of the entire distribution of available occurrence of protests from ACLED (Appendix Table A2 for summary statistics of actual protests). The distribution supports the use of the Zero-Inflated Poisson estimator, which we discuss in the next section.

We construct an alternative measure of protests capturing the intensity of media chatter around protests. The rationale for the news-based measure of protests is first and foremost that it allows to significantly expand the scope of the data on protests over space and time. This measure accounts for the saliency of specific protests. For instance, a protest sparked by a popular figure is likely reported and discussed more extensively in the news media. The news data are from Dow Jones FACTIVA, which is a global repository of over one billion articles published across the world and reported in 28 different languages. For each language, the following words are translated: (i) “street” and (ii) “protests” or “protest”. The search for each language is constructed such that the word “street” and at least one relating to “protest” appear. For example, in English, the precise search query is: _[(“street”) AND (“protest” OR “protests”)]_ .

This search is performed for all countries and all languages in the Dow Jones database, over the period from January 1, 2000 to December 31, 2019. In addition, we gather the number of total articles in the Dow Jones FACTIVA for each country and each month. A measure of normalized article counts is then constructed by taking the ratio between the number of articles mentioning “street protests” and the total number of articles for each country-month pair multiplied by 100. Formally, for country _i_ in month _t_ the intensity of media chatter around protests is as follows: The news-based protest data provide us with a balanced panel for 207 countries from January 1, 2000 to December 19, 2019 (see Panel B of Appendix Table A1 for the list of countries). The coverage is more comprehensive than for ACLED and allows us to exploit time and space variation. Panel B of Appendix Figure A1 presents the histogram of the entire distribution of newsbased protests. In 33.4 percent of country-month pairs (16,568 of 49,678), there is zero news about protest (Appendix Table A2 for summary statistics). The distribution is also skewed toward zero, which supports the use of the Zero-Inflated Poisson estimator.

The evolution of the measure is presented in Figure 2 in monthly frequency (Panel A). We also illustrate the evolution of the measure for countries in the Middle East and North Africa (Panel B). Both panels show a clear uptick in the chatter about protests around the Arab Spring in 2011 and the second wave of protests that started in 2019. The uptick in Panel A around 2003 reflects global protests against the war in Iraq.

4
Another aspect worth considering is the possibility of recapturing lost markets. Environmental concerns have already caused many European country Governments to ban (or consumers to discriminate against) tropical timber<sup>23</sup> . Because there are no sound estimates of the already lost market share for tropical timber we will not consider this potential effect in our calculations. However, the magnitude of this figure does not alter the thrust of the argument developed in this paper. Even if we assume that all the reduction in the import value by European consumers was due to environmental concerns during the period 1987-89 and 199192, it will result in a loss of US$340 million. However, adjusting for the tropical timber price increase during the same period (about 13%), and assuming a price elasticity of demand of 1.7%, the decrease in the European value of imports falls to approximately US$149 million. Including these US$149 million in the potential benefits calculated in the following section will not significantly change the overall potential revenues available to developing countries due to TC.

# **Computation of Likely Commercial Benefits from Timber Certification**

Based on the discussion above, the following is a rather optimistic scenario on how tropical timber certification will likely affect timber revenues from tropical timber producing countries. The assumptions underlying the scenario are that:

- (i) 20% of the European<sup>2 4</sup> market will be affected (market "niche" for certified timber).

- (ii) 10% of the American market will be affected (market "niche" for certified timber).

- (iii) 10% increment of revenues due to the "green premium" affecting the market "niches" for certified timber (i) and (ii) above).

- (iv) In the absence of certification, the whole market "niches" identified above, i.e., (i) and (ii) would be lost.

The figures for this scenario are shown in Table 5.

> 23See Johnson and Cabarle (1993), and the _Economist_ (1993). 24European countries included are the EU12 and EFrA countries.

26
|**Free float,**<br>**%**|**Number of**<br>**firms**|**Percent of**<br>**total **|
|---|---|---|
|0 to 5|90|32.4%|
|6 to 10|37|13.3%|
|11 to 15|22|7.9%|
|16 to 20|20|7.2%|
|21 to 25|23|8.3%|
|26 to 30|12|4.3%|
|31 to 35|13|4.7%|
|36 to 40|9|3.2%|
|41 to 45|5|1.8%|
|46 to 50|8|2.9%|
|51 to 55|11|4.0%|
|56 to 60|3|1.1%|
|61 to 65|3|1.1%|
|66 to 70|6|2.2%|
|71 to 75|4|1.4%|
|76 to 80|3|1.1%|
|81 to 85|1|0.4%|
|86 to 90|2|0.7%|
|91 to 95|2|0.7%|
|96 to100|4|1.4%|
|**Total **|**278**|**100%**|

_Source: EGX, S&P, World Federation of Exchanges, World Development Indicators, Coface; World Bank staff analysis Note for upper right graph: Benchmarked values based on a worldwide regression model that takes into account GDP per capita, population size and density, value of fuel exports to GDP, the poverty gap, and whether the country is an offshore financial center. The confidence interval is plus and minus the standard error of the prediction. Note for lower graph: The graph displays the latest available data and ranges from 2005 to 2008, but is mostly from the period 2006-2008._ The free float is the fraction of shares that is available for trading to a broad audience. Exhibit 3.3 shows that the free float on EGX is limited. More than 30 percent of companies have a free float of less than 5 percent. More than 50 percent of companies have a free float of less than 15 percent. Only 5 percent of firms have a free float of 70 percent or higher. Low levels of free float adversely impact market activity and impede price discovery, while magnifying the market impact of a transaction and hence transaction costs, which further discourages trading. CMA however recently reduced the free float requirement for listed firms from 30 percent to 5 percent. It would be advisable to reconsider increasing this requirement and encourage listed firms to sell additional shares to the market.

Looking at all Egyptian firms reported in Bloomberg―about 200 stocks in 2008―real average value traded was 10.6 times larger in 2007 than in 2003 (Exhibit 3.4). Driven by growing stock prices, volume traded grew strongly as well during this period (correlation coefficient is 0.75), but the increase in 2004 was entirely due to price increases as the number of transactions stayed roughly constant. Volume traded has been falling since February 2008, probably mainly due to international financial deterioration and receding capital flows.

# **Exhibit 3.4 – Drivers of average real value traded**

_Prices and number of transactions are indexed to base year 2003, 2003-07 (left graph) Daily volume traded and average prices in Egyptian Pounds, 2003-08 (right graph)_ 24
**<mark>Table 26: Impacts on Number of Firms from Liberalisation of Barriers in Professional Services No initial rent capture case</mark> % ch** **<mark>ange from benchmark</mark>**

||Domestic &<br>Discriminatory<br>Services|Domestic<br>Services|Unilateral<br>Discriminatory<br>Services|EU<br>Discriminatory<br>Services|Africa<br>Discriminatory<br>Services|Africa-EU<br>Discriminatory<br>Services|Rest of World<br>Discriminatory<br>Services|
|---|---|---|---|---|---|---|---|
|Kenya|0.5|1.7|-1.1|-0.5|-0.1|-0.6|-0.6|
|European Union|49.2|5.1|40.2|43.3|-0.4|42.7|-1.6|
|Africa|13.4|1.7|11.4|-0.5|12.5|12.0|-0.6|
|Rest of the World<br>Source: Authors' estimates.|59.2|6.0|48.2|-1.6|-0.4|-2.0|51.4|

70
# **Figure 3:  Household PM10 Concentrations: Kitchens vs. Living Rooms**

<!-- Start of picture text --> Median PM-10 (24-Hour ug/m**3)<br>400<br>300<br>200<br>100<br>0<br>0 100 200 300 400<br>Kitchen<br>Living Room<br><!-- End of picture text -->

**Table 3: Living Room vs. Kitchen PM10 Concentrations: Lag Relationship**

Dependent Variable: Log Living Room PM10 (1) (2) (3) (4) (5) (6) (7) Log 0.770 0.383 0.366 0.345 0.336 0.328 0.326 Kitchen (253.14)** (37.79)** (36.59)** (34.44)** (33.68)** (32.87)** (32.67)** PM10 L1 0.405 0.121 0.129 0.120 0.121 0.116 (39.93)** (8.97)** (9.64)** (8.99)** (9.07)** (8.72)** L2 0.314 0.123 0.129 0.122 0.122 (31.34)** (9.16)** (9.63)** (9.10)** (9.16)** L3 0.212 0.070 0.075 0.069 (21.22)** (5.23)** (5.61)** (5.17)** L4 0.159 0.051 0.056 (15.93)** (3.84)** (4.16)** L5 0.121 0.030 (12.11)** (2.22)* L6 0.103 (10.28)** Const 1.094 1.001 0.933 0.892 0.862 0.842 0.825 (67.98)** (62.97)** (58.94)** (56.23)** (54.19)** (52.65)** (51.33)** Obs 33,096 33,061 33,015 32,969 32,923 32,877 32,831 R<sup>2</sup> 0.66 0.68 0.68 0.69 0.69 0.69 0.69 Adj. R<sup>2</sup> 0.66 0.68 0.68 0.69 0.69 0.69 0.69 Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% __________________________________________________ 14
educational opportunities. Since school construction in Indonesia weakened the impact of a father's education on his sons' schooling attainment, the policy should be considered eective, even though it failed to inuence the deep-seated institutional matrix in Indonesia in the 1970s and 1980s.<sup>49</sup> The Inpres schools were eective for girls also in the sense that the girls born to uneducated fathers had a much higher probability of primary completion when exposed to the new schools.

# (9) Conclusions

Exploiting a dramatic expansion of primary schools in Indonesia that doubled the number of primary schools in ve years, we provide evidence on an important policy question relevant for most of the developing countries: does public investment in primary school construction improve intergenerational educational mobility of the disadvantaged groups such as girls and children born to uneducated parents? We take advantage of a large data set from the full count census 2000 and rely on a credible identication scheme developed by Duo (2001). Our empirical specications are based on recent theoretical analysis that suggests that the intergenerational educational mobility curve can be concave or convex, with the workhorse linear model as a special case.

We nd that Inpres schools made the mobility CEF less concave (or more convex), except for girls' years of schooling for which there is no signicant eect on the shape of the mobility curve. This led to substantial improvements in relative mobility of the most disadvantaged children (fathers with no schooling) at the primary completion level irrespective of gender, but also reduced relative mobility among the more educated households. The new schools thus resulted in a stronger persistence in the educational advantages enjoyed by the most educated segment of the society.

The eects on the educational opportunities of children beyond the primary level are, however, dramatically dierent across genders. While the eects on sons' completed years of schooling are qualitatively similar to that on primary completion, surprisingly, there are no signicant eects on girls. We explore the mechanisms behind the puzzling discord between the eects at the primary level versus competed years of schooling for girls. We nd that the

> 49Note that schooling rank of a child can also be treated as an inherited circumstance even though the literature on IOP does not usually use ranks as circumstances. From this perspective, if a policy weakens the correlation in ranks between parents and children, this clearly implies improvements in mobility.

29
(2)

**_Wp 1,_** _= TJ,ifT + Dj,I/D + uj,_

The primary variable we include in Tt, the vector of trade related<sup>industrycharacteristics,is</sup> tariffs. In addition, to address potential concerns about omitted<sup>variable bias, we alsoexperiment</sup> with other controls in Tj1, such as lagged import penetration,<sup>laggedexport to output share,and</sup> interactions of the above variables with exchange rates. The vector<sup>Djt consists of aset of</sup> industry and time indicators, which we include in our more complete<sup>specifications.' 8</sup> Since the dependent variable in the second stage is estimated, we estimate<sup>(2)with</sup> weighted least squares (WLS), using the inverse of the variance of<sup>the wagepremiumestimates</sup> from the first stage as weights. This procedure puts more weight<sup>on industries withsmaller</sup> variance in industry premiums. We also account for general<sup>formsof heteroskedasticityand</sup> serial correlation in the error term in (2) by computing robust<sup>(Huber-White) standard errors</sup> clustered by industry.

# **4.2 Results**

Prior to discussing our regression results, let us provide some information<sup>on the wage</sup> premiums. First, most of our estimates of industry wage premiums are highly<sup>statistically</sup> significant, which confirms that industry affiliation plays an important<sup>rolein determining</sup> worker wages. Second, our results suggest that the structure of<sup>Brazilianindustry wagesdoes</sup> not change substantially between 1987 and 1998. The year-to-year<sup>correlationin industry wage</sup> premiums are very high, with the correlation coefficient usually<sup>exceeding.9.Thisfindingis</sup> surprising given the results from previous studies on Mexico and Colombia<sup>during trade</sup> liberalization episodes (see Robertson (2000), Goldberg and Pavcnik<sup>(2001)).Thosestudies</sup>

> Is We consider the use of individual wage data and worker characteristics<sup>in the first stage aplus.AsGaston and</sup> Trefler (1994) point out, by conditioning our industry wage premium<sup>estimatesonindividualcharacteristicsin the</sup> first stage, the relationship between tariffs and wages in the second<sup>stagecannot be driven by observable differences</sup> in worker composition across industries.

21
Table S1.3: CORRELATIONS BETWEEN HAVING TRANSFERS FROM EMPLOYERS AND TERMS OF LABOR CONTRACTS

||_W_|_omen’s labor con_|_tracts:_||_Men’s labor cont_|_racts:_|
|---|---|---|---|---|---|---|
||Hourly<br>wage|Range of<br>wage income|Range /<br>hourlywage|Hourly<br>wage|Range of<br>wage income|Range /<br>hourlywage|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Interlinked contract|-0.277***|-88.121*|-8.370|-0.705***|-101.017|-0.947|
||(0.099)|(47.645)|(8.051)|(0.154)|(74.337)|(8.341)|
|Observations|6358|6358|6358|7988|7984|7984|
|Mean level of outcome|5.743|1218.879|221.179|9.643|2271.537|252.602|
|Adjusted R-squared|0.217|0.113|0.067|0.268|0.074|0.073|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports baseline correlations between having received transfers from employers and contract terms (wage per hour and volatility (range) of monthly wage earnings). ‘Received transfers from an employer’ is a dummy variable equal to 1 if respondent is working for an employer who lives in the same community and who is also reported as a source of transfers for the respondent’s household. Sample includes baseline observations from ultra-poor and non-ultra-poor households. Each observation is weighted using sampling weights calculated as the fraction of households surveyed from each wealth class (lower, middle and upper) relative to the number of households from the relevant wealth class in the community census. Columns 1 - 3 refer to terms of labor contracts of primary female in the household and columns 4 - 6 refer to terms of labor contracts of the male household head. All regressions control for the following characteristics of the relevant individual [primary female in columns 1 - 3 and male household head in columns 3 - 6] : whether the individual is literate, individual’s age (in years) and age squared, total household wealth (in BDT) and female respondent’s height (in centimeters) – male respondent’s height was not available, thus we control for female respondent’s height as a proxy. In columns 1 and 4, the dependent variable is wage per hour (in BDT), which is the sum of earnings from wage labor divided by hours spent in wage-labor during the year preceding the survey. In columns 2 and 5, the dependent variable is the range of monthly wage earnings, which is the difference between the maximum and minimum monthly earnings of the respondent from wage employment during the year preceding the survey. In columns 3 and 6, Ithe dependent variable is the range of monthly wage earnings divided by the wage per hour (in BDT). In 2007, 1USD=69BDT. *** (**) (*) indicates significance at the 1% (5%) (10%) level.

3
Table 3. Distance and income effect on technology hiring within the US

||Dep variable: <br>of tech-re|Normalized share<br>lated postings|
|---|---|---|
||(1)|(2)|
|Mean distance from pioneer, logs<br>Income per capita, logs|-0.004**<br>(0.000)|-0.004***<br>(0.000)<br>0.012***<br>(0.004)|
|Technology FE|Yes|Yes|
|Year FE|Yes|Yes|
|Adjusted R-squared|0.079|0.083|
|Observations|13,311|13,311|

Notes: This table displays regression results of the share of technology-related job posting on the (log) distance and (log) median household income in the US, for technology T in state s and year t, controlling for technology, year and state-technology fixed effects. The median household income for each state is measured in 2022 US dollars with data the Federal Reserve Bank of St. Louis. Standard errors are clustered by technology. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Table 4. Distance and income effect on technology hiring, across 107,981 global firms

||Dep variable:|Normalized s|hare of tech-r|elated postings|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Nearest distance from pioneer city, logs|-0.075***|-0.104***|-0.087***|-0.100***|
||(0.016)|(0.021)|(0.019)|(0.026)|
|Firm sales in USD, logs|||0.034**<br>(0.013)|0.030**<br>(0.013)|
|Technology FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Country FE||Yes||Yes|
|Adjusted R-squared|0.001|0.002|0.001|0.001|
|Observations|18,709,716|18,709,716|3,907,761|3,907,761|

Notes: Standard errors clustered at the tech level in parentheses. The normalized shared is calculated by Normalized share _i,τ,t_ =<sup>_share jobs exposedi,τ,t_</sup> . The normalized share is capped at the 99<sup>_th_</sup> percentile of _share jobs exposedτ,t_ non-zero observations. The specification excludes observations before the year of emergence of a technology.

# **4 Diffusion through multinational supply chains**

The results reported in the previous section showed that emerging technology jobs are disproportionately concentrated in Fortune 500 firms and their supply chains. In this section, we examine empirically the role of various types of firm-to-firm linkages in the spread of new technologies. We find that firms with pre-existing supply chain linkages to pioneer firms before technologies emerged see faster technology diffusion, with external buyers 20
|‘<br>ICT<br>—|South<br>;<br>Africa|Upper<br>pper<br>Middle<br>Income|‘ct<br>SSA|Middle<br>East<br>as<br>and<br>Noth<br>.<br>Africa|South<br>,<br>Asia|East<br>as<br>Asia<br>.<br>Pacific|Latin<br>“<br>America<br>&<br>_<br>Caribbean|Europe<br>&<br>;<br>Central<br>.<br>Asia|Low<br>Income|Middle<br> Income|Hi<br>igh<br>Income<br>\<br>OECD|.<br>World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Teledensity<br>(total telephone subscribers/1000 people)|408|63<br>.|509<br>“|92<br>~~|92<br>7|TD<br>7|433<br>~|547<br>a|4.|468|393<br>a|501<br>.|
|Mainlines Teledensity<br>.<br>(mainlines subscribers/1000 people)|7<br>ao|asl|3|29<br>E|35|-<br>7<br>o|92<br>c|22<br>a|29|93,<br>c|572|213<br>E|
|Cellular Teledensity<br>.<br>(cellularsubscribers/1000people)|-<br>3<br>o4|381|73|-<br>7<br>16|37|-<br>9<br>6|2**4**9|325|37|2<br>80|27<br>8|-<br>29<br>6|
|onseholdswithOwnTelephone<br>(% ofhouseholds)|38|LB|4|a4|4|9|33|43|6|22|.|Lb|
|Cost ofLocalPhone Call<br>(US cents/3 minutes)|5<br>E|B|ao|i<br>o|be|5|7|7|S|7|5<br>E|9|
|Cas<br>Ca<br>8<br>“ost ofPhone<br>CallfotheUS<br>(US cents/3 minutes)|58|305|«497|«(128k<br>6|0 336|.450|328|326|«504|309|s«d3|38|
|Cost ofCellular Local Call<br>(US cents/3 offpeak minutes)|25|5<br>ts|2<br>be|52|7<br>Z|2<br>be|57|bi|bi|9<br>be|57|9<br>be|
|eemmecialPerceptionofTelephone/Fax Infrastructure<br>quality<br>(l=worst 7=best)|51|56|43|58|43|52|46|53|3.4|53|6.6|55|
|~ommercialPerception of Availability ofMobile or<br>Cellular<br>Telephone<br>(l=worst 7=best)|63|6.0|57|50|50|57|55|6.0|50|50|6.7|61|
|&ommercialPerception ofInternetAccess inSchools<br>(l=worst 7=best)|36|38|18|35|a7|a4|18|37|11|a4|54|40|
|~emmercialPerception ofPostal Efficiency<br>(l=worst 7=best)|33|44.0|037|2«-«49|2¢«&B|aD|3.1|43|31|39|62|46|
|Phone Faults<br>(reportedfaults/100 mainlines)|bb|te|57|23|97|39|2<br>a|3<br>be|i<br>oe|25|a|37|
|UnmetDemand<br>(%ofmaintelephonelinesinoperation)|I|4|5<br>0|26<br>6|2<br>4|3<br>I|7<br>I|8|7<br>4|2<br>I|0|2<br>I|

Note: Data used isfron: the latest observation available between 1997 and 2063.

Source: Estache, Anionio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office of the Vice President, June 2005.
# **Figure 5 Adjusted score gap in mathematics**

<!-- Start of picture text --> Grade 4<br>UP<br>0.15<br>** * MP<br>** *<br>0.10 ** KA<br> **<br>0.05 * *<br>0.00<br>High Male Mother Father Wealth<br>-0.05 caste literate literate<br>** p value below 1 percent, * p value below 5 percent<br>gap<br>Percent point score<br><!-- End of picture text --> In MP, students’ gender, caste and mother’s education are significant variables in the test score regressions (Table 8 and figures 5-6). An increase of one year in age decreases language score by 2 percentage points but not so in math where the age variable is insignificant. This may reflect the fact that a number of students in the sample districts of MP migrate temporarily every year with their parents for employment. Boys do better, scoring 5 percentage points higher than girls. Students from high caste (i.e. who are neither OBC nor SC/ST) have 4 percentage points higher score in math and ST students have a 7 percentage point lower score in language. Wealth variable is insignificant and so is father’s literacy. Those with a literate mother have 9-12 percent point higher score.

**Figure 6 Adjusted score gap in language**

<!-- Start of picture text --> Grade 4<br>UP<br>0.15 ** MP<br>** KA<br>0.10 ** **<br>**<br>0.05 * *<br>0.00<br>High Male Mother Father Wealth<br>-0.05 caste literate literate<br>** p value below 1 percent, * p value below 5 percent<br>Percent point score gap<br><!-- End of picture text --> In contrast, in Karnataka none of the observed student characteristics are significantly correlated with test scores. This may suggest that when schools are good, learning in school makes up for some of the household differences in learning and when students are not learning in school, family factors continue to matter more for learning outcomes. Results are generally similar for grades 3 and 2.

11