rafmacalaba/datause-encoder-data huggingface dataset:
growth in general that reduces poverty in developing countries, but the direct and indirect effects of growth in agriculture. In their study of poverty in India over a 35-year period, Datt and Ravallion (1996, 1998) find that higher farm productivity reduces both absolute as well as relative poverty. This is partly due to a direct channel of higher household income operating in the short run and partly due to indirect channels, such as higher wages and lower food prices in the longer run. Other empirical studies also suggest that these are the main channels and not labor migration from agriculture into other sectors. This strengthens the argument for supporting agricultural growth.<sup>6</sup> Similarly, Loayza and Raddatz (2010) show for a cross-section of developing countries that growth in more labor-intensive sectors such as agriculture has a larger impact on poverty reduction than less labor-intensive activities. Christiansen and Demery (2007) estimate that 1 percent per capita agricultural growth reduces poverty 1.6 times more than the same growth in industry and three times more than growth in the service sector. Case studies confirm these cross-country findings. For example, Dercon and Christiaensen (2005) estimate that among 15 villages in Ethiopia consumption per adult equivalent is 8.5 percentage points higher if households use fertilizers, i.e., inputs to increase farm productivity. Another channel through which agricultural growth can reduce poverty is employment generation in the nonagricultural sector. Mellor (2001) finds that this effect is mostly driven by increased consumption demand and not so much by production linkages. Hence, it can be said that agricultural growth is a central instrument in helping the poor in developing countries.

However, although these empirical investigations establish correlation between agriculture and GDP growth, they do not imply causation in either direction. The correlation observed could be spurious if both sectors have been growing independently from each other or as a result of a common third factor. As a result, studies that have argued a causal effect of agricultural growth on economic growth have been criticized. To address this issue of endogeneity in empirical work, Tiffin and Irz (2006) use Granger causality tests to establish that agricultural value added per worker has a positive effect on GDP per capita in developing countries. Bravo-Ortega and Lederman (2005) also employ panel data tools such as GMM and Granger causality tests to re-estimate the effect of agricultural growth on the overall growth rate.

> 6  The 2008 World Development Report suggests that the recent reduction in rural areas in developing countries has been driven by improvements in conditions in rural areas rather than by migration to urban areas (World Bank 2007).

10
**Table 5. WTO Member Antidumping and Countervailing Measures Initiations, Impositions and DSU Challenges, by Targeted WTO Exporter for 1995-2008**

|**Targeted WTO member**|**New AD**<br>**Initiations**|**New AD**<br>**Measures**|**Exporter**<br>**uses DSU to**<br>**challenge**<br>**New AD**|**New CVM**<br>**Initiations**|**New CV**<br>**Measures**|**Exporter uses**<br>**DSU to**<br>**challenge**<br>**New CVM**|
|---|---|---|---|---|---|---|
|**Total developed economy exporters**|1175|722|72|72|39|15|
|EC|283|161|55|33|22|9|
|Japan|144|106|2|0|0|0|
|U.S.|189|115|5|7|1|0|
|Korea|252|150|3|16|9|3|
|Taiwan*|92|64|2|1|0|0|
|Other developed|215|126|5|15|7|3|
|**Total developing economy exporters**|1416|909|38|125|82|9|
|Argentina|30|15|3|6|4|0|
|Brazil|97|74|5|7|8|1|
|China*|410|295|5|23|14|5|
|Costa Rica|2|0|1|0|0|0|
|Guatemala|3|1|1|0|0|0|
|India|137|84|10|46|27|2|
|Indonesia|145|82|2|11|8|0|
|Malaysia|90|50|0|3|3|0|
|Mexico|40|27|5|0|0|0|
|Pakistan|10|6|0|1|1|0|
|Philippines|11|6|0|1|2|0|
|South Africa|58|38|0|6|4|0|
|Thailand|142|84|2|9|3|0|
|Turkey|44|25|2|2|1|0|
|<br>Other developing|197|122|2|10|7|1|
|**Total WTO member exporters**|2591|1631|110|197|121|24|

Source: Table 4-3 of Bown (forthcoming, b). *Since WTO accession in 2001.

The third and fourth reasons to prefer SG are more subtle but nevertheless still important. Because global safeguards are “fair trade” provisions, they are less adversarial to foreigners. Unlike AD or CVD, using SG does not require an allegation of foreign wrongdoing (dumping or illegal subsidies). Instead, SG entails greater recognition and acceptance that the act of protectionism is a response to a crisis. Using SG over AD/CVD may thus decrease the likelihood of foreign retaliation and the ramping-up of protectionist sentiment in trading partners. As a final technical matter, SG is simply less costly for a bureaucracy to administer than policies such as AD and CVD, which are much more data intensive. Taking as given that the end of an investigation is simply going to result in 22
<!-- Start of picture text --> Flour Bread<br>Morocco Iran, Islamic Rep. Libya Djibouti Libya Iran, Islamic Rep.<br>° 0<br>me<br>75<br>=ian B -40<br>=. = 60<br>Es+aE§a -t0<br>-10 -100<br><!-- End of picture text --> <!-- Start of picture text --> =<br>ON a" ge°*"— ggee* ge" 0.00% —<br>t 70.20% &<br>& t 5.00%<br>E .0.40% =g -10.00%<br>mQuintile1 sQuintile2 sQuintile3 mQuintile4 mQuintileS<br>=Quintile1 Quintile2 Quintile3 SQuintile4 m= Quintiles<br>Sugar Oil<br>Morocco Libya Djibouti Libya Djibouti<br>& 46 "S58 & ° —— — =<br>><br><!-- End of picture text --> <!-- Start of picture text --> b=]ia 4<br>£3 3iS<br>gR- §<br>Esoa —oa 8<br>-6 -10<br>£ =<br>ty 70.20% 5 70.50%<br>E @<br> 0.40% E -1.00%<br>eQuintie1 mQuintile2 me Quintile3 me Quintile4 m Quintiles eqQuintile1 sQuintile2 sQuintie3 eQuintile4 wm QuintileS<br><!-- End of picture text -->
**Table 3.5. Sectoral GDP shares in 2012 and for base simulation by period (Percent of nominal GDP at factor cost)**

||**2012**|**2017**|**y p**<br>**2030**|**(**<br>**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|---|---|
|**Mining**|3.6|17.3|12.9|13.2|14.7|14.3|
|**Non-mining**|96.4|82.7|87.1|86.8|85.3|85.7|
|**Agriculture**|40.6|34.1|34.5|36.1|33.9|34.5|
|**Non-mining industry**|7.5|4.8|3.6|6.1|3.9|4.5|
|**Private services**|35.6|29.5|34.4|31.1|32.3|32.0|
|**Government services**|12.6|14.3|14.6|13.6|15.1|14.7|
|**Human development services**|6.2|6.4|6.6|6.3|6.6|6.5|
|**Infrastructure services**|1.8|1.9|1.7|1.8|2.1|2.0|
|**Othergovernment services**|4.7|5.9|6.3|5.4|6.4|6.1|
|**Total**|100.0|100.0|100.0|100.0|100.0|100.0|

**Table 3.6. Real GDP at factor cost by sector: level in 2012 (constant US$mn) and base growth by period (Percent per year)**

||**2012**|**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|
|**Mining**|59.8|48.4|0.5|12.0|
|**Non-mining**|1,578.9|5.6|5.9|5.8|
|**Agriculture**|665.2|4.3|5.2|5.0|
|**Non-mining industry**|122.8|1.3|5.1|4.0|
|**Private services**|583.8|5.7|7.1|6.7|
|**Government services**|207.1|11.1|4.6|6.4|
|**Human development services**|101.3|10.3|5.3|6.7|
|**Infrastructure services**|28.8|12.1|3.4|5.8|
|**Othergovernment services**|77.1|11.9|4.0|6.1|
|**Total**|1,638.7|8.8|5.2|6.2|

**Note: Constant US$ are at 2009 prices.**

22
indexes and rankings when the next round of the Paris Monitoring data becomes available, over the longer term we recognize the value of having ranking exercises carried out by independent think tanks and researchers rather than by research staff at a donor agency. We hope that with this paper, we have added some data sources, methodological considerations, and cautions about interpretation that will influence other ranking exercises and—perhaps most importantly—will also inform consumers of the rankings.

as a major source of data. Two differences are that unlike ours, their approach does not appear to make adjustments for factors outside the donor agency’s control, nor does it attempt to combine the scores along different dimensions into an overall score and ranking.

21
do not account for this biological externality because they are paid according to the tons they catch and the international price of fishmeal, not the composition of juveniles and adults they catch (Fr ́eon et al., 2014; Sindicato Univo de Pescadores de Nuevas Embarcaciones del Peru (SUPNEP), 2017; Hansman et al., 2020). A tax could achieve the optimal level of juvenile catch, but the fishing industry opposes such a policy (Grainger and Parker, 2013; Instituto Humboldt de Investigaci ́on Marina y Acu ́ıcola (Instituto Humboldt), Sociedad Nacional de Pesquer ́ıa (SNP), Centro Desarrollo y Pesca Sustentable (CeDePesca), 2018). Instead, the regulator attempts to reduce juvenile catch by implementing temporary spatial closures in areas where it believes juveniles are abundant.

I analyze the regulator’s temporary spatial closures policy in this paper, accounting for other regulations that also affect fishing. I use administrative microdata and data from fishing firms, which together contain the location, time, and number of juvenile anchoveta each vessel catches each time it sets its net in the water. These data comprise hundreds of thousands of vessel-level fishing operations. Vessels report the percentage juvenile they catch to the regulator in real-time.<sup>1</sup> When percentage juvenile values in an area are high, the regulator temporarily bans fishing in that area for three to five days. Vessels are not allowed to fish inside actively closed areas. But they are allowed to fish inside closed areas between the announcement and the beginning of closure periods, just outside closed areas during closure periods, and inside closed areas after the end of closure periods. Between 2017 and 2019, the regulator implemented 410 temporary spatial closures, each covering a different area of ocean and time period.

Estimating the causal effect of the temporary spatial closures policy requires counterfactual areas and times that could have been closed and are comparable to closures declared by the regulator. To address this challenge, I generate “potential closures” by creating an algorithm that mimics the regulator’s closure rule and takes as its input the same data the

> 1In my regressions, I correct for misreporting to the regulator by matching vessel-reported data to percentage juvenile measured by third-party inspectors. Third-party inspector data is not used by the regulator to determine closures.

3
do not encourage growth, and impose taxes and regulations that distort private decisions. Aggregate measures of government size will not capture the potentially important implications of how total government expenditures are allocated. Furthermore, even if government funds are always spent on growthpromoting goods, there may be complex, non-linear tradeoffs between the beneficial effects of government services and the deleterious implications of distortionary taxes (see: Barro (1990) and Easterly (1990)}.+2 Linear, crosscountry regressions will not appropriately capture these relationships. In addition, disaggregated measures of government expenditures and tax sources are only available for a limited number of countries since the 1970s and are particularly prone to measurement problems. Moreover, since government resources may be spent effectively or ineffectively, using simple expenditure data without accounting for government efficiency may yield inaccurate measures of the actual delivery of public services. While recognizing these problems, we focus on examining the robustness of past findings.

A common measure of the role of government in economic activity is the ratio of government consumption expenditures to GDP [e.g., Easterly (1990), Romer (1989), and Landau (1983)]. Table 5-A reports extreme bounds tests of this variable for the period 1960-1989. The coefficient is not robust although the estimated coefficient is always negative.?> 12 see Levine (1991) for an analysis of the effects of different types of taxes on long-run growth.

13 similar results were obtained with the Summer-Heston data set, but if the extreme bounds procedure is prohibited from choosing the ratio of government investment expenditures to GDP as a Z~-variable, the relationship is robustly negative (with the Summers-Heston data set).

15
— in a small number of cases — firsthand accounts from native speakers and translators; using these data sources, we generate a measure of the grammatical gender structure of each of the languages in our data set. Taken together, these languages account for 6.44 billion people, or over 99 percent of the world population.<sup>1</sup> We use these data in two ways. First, we calculate — for every country in the world — an estimate of the proportion of the population whose native language is a gender language. We are able to account for more than 90 percent of the estimated population in all but three countries. In our first piece of analysis, we explore the cross-country relationship between grammatical gender and women’s labor force participation, women’s educational attainment, and gender attitudes among both men and women. We then complement our cross-country analysis by estimating the individual-level association between grammatical gender and women’s participation in economic life using Afrobarometer data from African countries where both gender and non-gender languages are indigenous and widely spoken.

Our cross-country analysis suggests a robust negative relationship between grammatical gender and female labor force participation. Our preferred specification suggests that grammatical gender is associated with a 12 percentage point reduction in women’s labor force participation and an almost 15 percentage point increase in the gender gap in labor force participation. These associations are robust to the inclusion of a wide range of geographic controls (including suitability for the plough) that could not plausibly have been impacted by language. Taken at face value, our coefficient estimates suggest that gender languages keep approximately 125 million women around the world out of the labor force. Following the approach suggested by Altonji, Elder, and Taber (2005) and Oster (2017), we estimate that unobservable country-level characteristics would need to be 1.44 times more correlated with treatment than observed covariates to fully explain the apparent impact of grammatical gender on the level of female labor force participation; unobserved factors would need to be 3.23 times more closely linked to treatment to explain the impact of grammatical gender on the gender gap in labor force participation.

> 1This calculation is based on _Ethnologue_ estimates of the total number of native speakers in the world.

4
WPS8499 Policy Research Working Paper 8499 Improved Biomass Cook Stoves for Climate Change Mitigation?

Evidence of Preferences, Willingness to Pay, and Carbon Savings _Sahan T. M. Dissanayake Abebe Damte Beyene Randall Bluffstone Zenebe Gebreegziabher Gilbert Kiggundu Shannon H. Kooser Peter Martinsson Alemu Mekonnen Michael Toman_ Development Economics Development Research Group June 2018
|P086661|2004|2009|12/28/2008|9 Closed|Bangladesh|
|---|---|---|---|---|---|
|P086661|2004|2009|5/26/2009|10 Closed|Bangladesh|
|P086661|2004|2009|6/25/2009|11 Closed|Bangladesh|
|P086661|2004|2010|11/29/2009|12 Closed|Bangladesh|
|P086661|2004|2010|1/29/2010|13 Closed|Bangladesh|
|P086661|2004|2010|5/24/2010|14 Closed|Bangladesh|
|P086661|2004|2011|12/11/2010|15 Closed|Bangladesh|
|P086664|2004|2004||0 Closed|St. Vincent and the Gr|
|P086664|2004|2005|4/24/2005|2 Closed|St. Vincent and the Gr|
|P086664|2004|2006|12/30/2005|3 Closed|St. Vincent and the Gr|
|P086664|2004|2006|6/23/2006|4 Closed|St. Vincent and the Gr|
|P086664|2004|2007|12/28/2006|5 Closed|St. Vincent and the Gr|
|P086664|2004|2007|12/29/2006|6 Closed|St. Vincent and the Gr|
|P086664|2004|2007|5/18/2007|7 Closed|St. Vincent and the Gr|
|P086664|2004|2008|12/14/2007|8 Closed|St. Vincent and the Gr|
|P086664|2004|2008|5/16/2008|9 Closed|St. Vincent and the Gr|
|P086664|2004|2009|11/25/2008|10 Closed|St. Vincent and the Gr|
|P086664|2004|2009|5/31/2009|11 Closed|St. Vincent and the Gr|
|P086664|2004|2010|11/20/2009|12 Closed|St. Vincent and the Gr|
|P086664|2004|2010|3/13/2010|13 Closed|St. Vincent and the Gr|
|P086664|2004|2010|6/18/2010|14 Closed|St. Vincent and the Gr|
|P086664|2004|2011|2/12/2011|15 Closed|St. Vincent and the Gr|
|P086664|2004|2012|8/6/2011|16 Closed|St. Vincent and the Gr|
|P086664|2004|2012|12/28/2011|17 Closed|St. Vincent and the Gr|
|P086669|2009|2009|6/25/2009|1 Closed|Croatia|
|P086669|2009|2010|12/28/2009|2 Closed|Croatia|
|P086669|2009|2010|6/28/2010|3 Closed|Croatia|
|P086669|2009|2011|5/25/2011|4 Closed|Croatia|
|P086669|2009|2012|10/30/2011|5 Closed|Croatia|
|P086669|2009|2012|4/3/2012|6 Closed|Croatia|
|P086669|2009|2012|6/2/2012|7 Closed|Croatia|
|P086669|2009|2013|10/15/2012|8 Closed|Croatia|
|P086669|2009|2013|12/26/2012|9 Closed|Croatia|
|P086669|2009|2013|3/31/2013|10 Closed|Croatia|
|P086669|2009|2014|10/12/2013|11 Closed|Croatia|
|P086669|2009|2014|12/21/2013|12 Closed|Croatia|
|P086670|2004|2004|6/29/2004|1 Closed|North Macedonia|
|P086670|2004|2005|5/22/2005|3 Closed|North Macedonia|
|P086670|2004|2006|1/3/2006|4 Closed|North Macedonia|
|P086670|2004|2007|12/27/2006|5 Closed|North Macedonia|
|P086670|2004|2007|3/1/2007|6 Closed|North Macedonia|
|P086670|2004|2007|6/22/2007|7 Closed|North Macedonia|
|P086670|2004|2008|2/28/2008|8 Closed|North Macedonia|
|P086670|2004|2009|3/9/2009|9 Closed|North Macedonia|
|P086670|2004|2010|12/17/2009|10 Closed|North Macedonia|
|P086670|2004|2010|2/17/2010|11 Closed|North Macedonia|
|P086670|2004|2011|9/8/2010|12 Closed|North Macedonia|
asking about HIV status and whether the woman is taking any drugs. For the indicators representing non‐clinical quality of care, we do not find any correlation with wealth.

# 5. Price

A wealth‐quality gradient naturally gives rise to the question of a price‐quality gradient, and to the question of whether quality gaps mostly exist because wealthier women can pay or are more willing to pay for higher quality of care (and additional services). This section analyzes the exit interview data to explore the correlations between the price women reported paying and the quality of their consultation and between the price and wealth of users.

Table 6, columns 1 to 3, presents the results of regressions of the total price paid for the consultation on the quality index created from the exit interview data. Overall, there is a strong relationship between quality and price: each percent of content of care is associated with a price increase of CDF 28. Alternatively, each additional item performed is associated with an average price increase of CDF 219 (15 percent of the median price of CDF 1,500). The coefficient is statistically significant at the 99 percent level. The relationship remains statistically significant at least at the 95 percent level even after accounting for health zone and facility fixed effects. The size of the effect reduces to CDF 23 when health zone dummies are included and to CDF 18 when facility dummies are included.

Consistent with the positive correlations of quality with wealth and price, there is a positive correlation between wealth and the price paid for a first consultation. The regression estimates show that a one standard deviation increase in wealth is associated with an increase of CDF 325 (about 22 percent of the median price) in the price paid for the consultation (table 6, column 4). This effect decreases to CDF 207 (about 14 percent of the median price) when health zone dummies are included in the regression, but it remains statistically significant at the 99 percent level (column 5).

Sixty‐one percent of the health centers reported having a policy of exempting at least some fees for poor users. Health centers can also provide fee exemptions or reductions on a personal basis. We therefore proceed to test the within‐facility wealth‐price relationship. When facility dummies are included in the regression (table 6, column 6), a one standard deviation increase in wealth is associated with a price increase of CDF 122 (8 percent of the median price). The relationship between price and wealth remains similar in magnitude and statistically significant when controlling for the quality of the consultation. As presented in table 6, column 9, for a consultation of given quality, a one standard 14
<!-- Start of picture text --> Annual convergence rate<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>-0.20.40 —————-—<br>0.6<br>-0.8<br>-1.0<br>g2£<br>2 2 383882 2 2 2 8 8 8&<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>3.0<br>2.0<br>1.510<br>0.5<br>0.0<br>-0.51.0<br>1980s 1990s 2000s 2010s|1980s 1990s 2000s 2010s<br>Advanced economies EMDEs<br><!-- End of picture text -->

<!-- Start of picture text --> Log change @ Advanced economies @EMDEs<br>4<br>3 e<br>2<br>1 ototys8e.e ae ve?e*... 3op".@# ©Sto 8<br>oF oo BK eee, O%<br>0 e ert<br>; eerie e<br>1 e<br>"5 7 Initial log 9productivity 11 13<br>Percent<br>,<br>1.5<br>1.0<br>0.5<br>0.0<br>1980s 1990s 2000s 2010s<br><!-- End of picture text -->
13 considerable wage variation within each group, and the two group distributions do overlap. The second reason is that Gonzaga et al. (2006) consider only manufacturing workers, which accounted for 16% (13%) of total employment in 1988 (1995). Changes in their relative position vis-à-vis workers in agriculture (which were also affected by changes in tariffs) and in services (which were indirectly affected by changes in tariffs, and also by changes in the exchange rate) may have led to overall changes in wage inequality which are quite different from those mandated by the Stolper-Samuelson effects within manufacturing.

In the remainder of this paper we examine two basic questions. First, we seek to place the changes in wage inequality which can be attributed to trade policy changes in the context of other changes that were concurrently affecting the wage distribution. Second, and more specifically, we also seek to quantify the contribution of the trade-mandated employment reallocation effects to changes in the wage distribution. A third, albeit more tentative, contribution is to investigate the implications of these trade-driven changes in the wage distribution for poverty and inequality in the distribution of household incomes per capita.

# **3. Data and Methodology.**

# _3.1 The Data Sets._

The data used in this study come from two different sources. The first of these is the household survey data, which are used to obtain information on wages, hours of work, occupations, education levels, age, gender, race and location of workers. We use 11 waves of the _Pesquisa Nacional por Amostra de Domicílios_ (PNAD), fielded by the _Instituto Brasileiro de Geografia e Estatística_ (IBGE), annually between 1987 and 1999.<sup>15</sup> The PNAD is a nationally representative household survey, with a stratified and clustered sampling design which ensures coverage of rural and urban areas in every state of the federation, except for the rural areas of Acre, Amapá, Amazonas, Pará, Rondônia 15 There are eleven waves because the survey was not carried out in 1991 or 1994.