rafmacalaba/datause-encoder-data huggingface dataset:
the proportion of women hired in the new jobs created to 50 percent by 2010; and increasing the proportion of trained workers in the total workforce to 40 percent by 2010. This will demand rapid expansion of the private sector, the most efficient and effective creator of jobs. If regional gaps are to be closed, it will be important that jobs be created in all parts of the country.

The importance of equitable growth - Meeting these targets will require economic growth that is both rapid and that distributes benefits more evenly than in the past. Different patterns of growth across Vietnam will have different effects on Vietnam’s poverty reduction efforts. Growth that is concentrated in urban areas will raise incomes in the cities but have less immediate impact on the incomes of the majority of the poor located in rural areas, except through rural urban migration which is likely to speed up in the future. Figure 5 illustrates the potential impact on poverty reduction of different levels and patterns of growth in Vietnam. Four different scenarios are shown:

1. A base case projection assuming annual GDP growth rate of 5 percent and no change in the pattern of growth across regions;

2. A higher growth scenario based upon the CPRGS target of doubling real GDP by 2010 (requiring a real GDP growth rate of 7.5 percent till 2005 and 7 per cent till 2010); 3. An even growth scenario where all regions and locations (rural and urban) grow at the same rate (the 5 percent assumed in the base case scenario); and 4. A more uneven regional growth rate (5 percent overall), where the relative growth rates between the regions are based upon each region’s share of new enterprise registrations in 2000.

The final scenario sees the South East and the Red River Delta regions (incorporating Ho Chi Minh City and Hanoi City) experience the fastest growth and could be interpreted as a ‘growth pole’ scenario, whereby the majority of future growth is concentrated in two or three key regions.

Figure 5. Poverty reduction projections under different growth scenarios <!-- Start of picture text --> Projected general poverty reduction<br>38 ————_—_——_—--A ---~—<AnrsyYv— oO<br>°<br>@ 30<br>pany2 os —SSS 8<br>3 is)<br>= 20<br>~£ ===—-Uneven regional growth Sel<br>3 1S Base case =m 14<br>oa = TM Higher growth<br>10 a OS a, a i es a a |<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text -->

Source: World Bank (2002a).

Not surprisingly, achieving a higher growth rate will lift more people out of poverty. But the importance of ensuring that the benefits of growth are distributed widely are also highlighted with the even growth scenario — 5 percent real GDP growth distributed evenly across the population can achieve greater reductions in poverty than a higher growth rate (7 per cent) distributed unevenly across the population 14
lower and in some instances may increase gender gaps. This finding complements earlier studies documenting an increase in wage inequality after trade liberalization in a number of developing countries, possibly reflecting skill complementarities.

We also found that labor market institutions have a major impact on occupational gender wage differentials, and may interfere with the impact of globalization on the gender gap. Unfortunately time-variant measures of labor market institutions are scarce, hampering our understanding of the mechanisms behind the changes in the occupational gender wage gap over time. Did labor market institutions change with changes in trade intensity, (partly) explaining the fall in the occupational gender wage gaps? With more information on the actual institutional functioning of the labor market, we may be better able to understand why trade may have lowered the occupational gender wage gap.

# **References**

Acemoglu, D., S. Johnson and J. Robinson (2001): “The Colonial Origins of Comparative Development: An Empirical Investigation”, _American Economic Review_ , 91(5), pp.1369-1401.

Anker, R. (1998): _Gender and jobs: sex segregation of occupations in the world_ . Geneva: ILO.

Arbache, J., A. Dickerson and F. Green, F (2003): “Trade Liberalisation and Wages in Developing Countries”, Working Paper.

Artecona, R. and W. Cunningham (2002): “Effects of Trade Liberalization on the Gender Wage Gap in Mexico”, The World Bank Becker, G. (1971): _The Economics of Discrimination_ . 2<sup>nd</sup> ed. Chicago: University of Chicago Press.

Beyer, H., P. Rojas and R. Vergara (1999): “Trade Liberalization and Wage Inequality”, _Journal of Development Economics_ , 59(1), pp. 103-23.

Berik, G., Y. Rodgers, and J. Zveglich Jr. (2004): “International Trade and Gender Wage Discrimination. Evidence from East Asia”, forthcoming in _Review of Development Economics_ .

Black, S. and E. Brainard (2002): “Importing Equality? The Impact of Globalization on Gender Discrimination”, NBER Working Paper 9110.

31
**Table 3. Difference (in percentage points) between statistics computed before and after the exclusion of outliers**

|**Country**|**Gini**|**MLD**|**Theil**|**Atkinson**|**H**|**PG**|**PG2**|
|---|---|---|---|---|---|---|---|
|Armenia|0.3|0.4|0.3|0.8|0.2|0.1|0.1|
|Bangladesh|1.2|1.5|2.9|1.6|0.1|0.1|0.1|
|Bolivia|3.7|8.5|12.5|10.1|0.3|0.5|0.6|
|Bulgaria|1.0|1.3|1.3|3.0|0.4|0.4|0.3|
|Burkina Faso|0.4|0.5|0.9|0.5|0.1|0.1|0.0|
|Cambodia|0.4|0.5|0.7|0.7|0.2|0.2|0.1|
|Cameroon|1.2|1.9|3.3|1.4|0.0|0.0|0.0|
|Côte d’Ivoire|2.2|3.2|5.5|2.9|0.1|0.1|0.1|
|Ecuador|0.9|1.3|2.1|1.4|0.1|0.1|0.1|
|Ethiopia|1.1|1.4|2.5|1.2|-0.1|0.0|0.0|
|Georgia|1.6|2.2|3.5|2.5|0.1|0.2|0.2|
|Ghana|1.2|1.8|3.2|1.6|0.0|0.1|0.1|
|Guatemala|1.0|1.4|2.3|1.6|0.1|0.1|0.1|
|India|1.8|2.6|5.1|2.3|0.1|0.1|0.1|
|Iraq|1.1|1.2|1.8|1.4|0.1|0.1|0.1|
|Kyrgyzstan|1.3|1.2|1.4|2.1|0.5|0.3|0.2|
|Malawi|5.0|7.6|28.2|5.7|0.1|0.1|0.1|
|Mali|4.2|7.0|15.5|4.7|0.0|0.1|0.1|
|Mexico|4.8|8.3|13.7|8.9|0.3|0.5|0.4|
|Mongolia|0.6|0.7|0.9|1.6|0.1|0.1|0.1|
|Mozambique|5.2|9.0|16.5|9.4|0.7|0.8|0.7|
|Nepal|1.2|1.6|2.7|1.6|0.1|0.1|0.1|
|Nicaragua|0.6|1.0|1.4|1.3|0.2|0.2|0.1|
|Niger|0.7|0.9|1.6|0.9|0.0|0.0|0.0|
|Nigeria|6.0|9.7|27.7|7.4|0.4|0.4|0.3|
|Pakistan|0.7|0.9|1.4|1.0|0.3|0.2|0.1|
|Peru|0.8|1.2|1.8|1.4|0.1|0.1|0.1|
|Rwanda|3.4|5.6|11.5|4.3|0.3|0.3|0.2|
|Senegal|1.3|2.4|3.0|4.0|0.5|0.5|0.5|
|Serbia|1.1|1.3|1.8|1.9|0.3|0.2|0.2|
|Sierra Leone|0.5|0.8|0.9|1.3|0.3|0.3|0.2|
|Tanzania|0.8|1.2|1.8|1.1|0.0|0.0|0.0|
|Uganda|2.4|4.1|10.1|3.0|0.2|0.2|0.2|
|Vietnam|2.8|3.8|7.0|3.6|0.2|0.2|0.2|
|_Average_|_1.8_|_2.9_|_5.8_|_2.9_|_0.2_|_0.2_|_0.2_|
|_SD_|_1.6_|_2.8_|_7.2_|_2.6_|_0.2_|_0.2_|_0.2_|
|_Min_|_0.3_|_0.4_|_0.3_|_0.5_|_-0.1_|_0.0_|_0.0_|
|_Max_|_6.0_|_9.7_|_28.2_|_10.1_|_0.7_|_0.8_|_0.7_|

Note: MLD is for Mean Logarithmic Deviation; ‘Atkinson’ is for the Atkinson index, where the inequality aversion parameter epsilon is set equal to 2; H is for the poverty headcount ratio; PG is for the poverty gap; PG2 is for the poverty gap squared. Poverty indices are computed using a relative poverty line, equal to 60% of median per capita consumption in the original distribution.

29
<!-- Start of picture text --> Firm Distribution<br>by Age<br>8<br>zyy<br>Se<br>Cy<br>2<br>© oe&gs ¥£ 4SsF weFS CSCEES# SSSsS £EE &SS ¥eg oesES 2 Ss<br>15<br>old 10+<br><!-- End of picture text --> <!-- Start of picture text --> Entry and Exit Rates<br>2&<br>&<br>Boce<br>&<br>°<br>© eFSFFES % ¢ FFS~FP KFSF SFf FTg- csoFES Seo ¢SFRy ra@ se&e2 & ss e<br>‘South Africa: sdministartive date, 2014<br>other countries from Bartelsman et. al (2009)<br><!-- End of picture text -->
|**Table 15:**<br>**Plant**<br>**Locatio**<br>**n**|**Summary**<br>**Gen.**<br>**Capacit**<br>**y**|**Damages to Solar Plants From 2017 Hurrica**<br>**Damages**|**nes, Puerto Rico**<br>**Design Specifications**|
|---|---|---|---|
|Humaca<br>o|44 MW|* The first phase of the solar farm, (finished<br>in 2016) fared relatively well (Limperis<br>2017)<br>* The solar modules of the northern<br>expansion still under construction,<br>consisting of 52 MW, were almost<br>completely ripped from their mountings<br>(Limperis 2017)<br>* Majority of newly added solar panels<br>ripped from foundation and completely<br>destroyed by winds (Smith 2017)|* System fitted with Fonroche<br>modules (Limperis 2017)|
|Guayam<br>a|24 MW|* Satellite imagery estimates a quarter of<br>modules were ripped from their mountings<br>by the storm (Limperis 2017)<br>* Site back to 74% generating capacity by<br>mid-October (Gallucci 2018)<br>* Curtailed because of transmission system<br>damage(Gallucci 2018)<sup>4</sup>||
|Loiza|27 MW|* Minimal damage (Limperis 2017)|* Panels and all components<br>(inverters, transformers) built<br>several (at least 2) meters off the<br>ground on mountings designed by<br>TSK to avoid floods (Limperis<br>2017)<br>* Reinforced to withstand<br>Category 5 winds (251 kph)<br>(Limperis 2017)<br>* Design withstood both the<br>hurricane and the torrential<br>rainfall well (Limperis 2017)|
|Isabela|58 MW|*Apart from occasionally torn away<br>modules, almost no damage was caused<br>(Limperis 2017)|<br>* Example of resilient design<br>(Limperis 2017)|

# **4.2 - Other Considerations**

The Puerto Rican government was facing challenges prior to the arrival of Hurricane Maria (Campbell, Clark, and Austin 2017). In the last decade, Puerto Rico’s economy has shrunk by over 15 percent, and this economic downtown has coincided with a sharp rise in the price of oil, which generates the majority of the island’s electricity (Central Intelligence Agency 2018). Prior to the 2017 hurricane season, the island’s electricity infrastructure was in poor condition, largely due to underinvestment and a lack of

> 4 Note: there are conflicting reports concerning the extent of damages at the Guayama wind farm. This highlights the need for more complete coverage of storm damages.

24
the least concentrated export structure by product, while Azerbaijan shows the most concentrated one (in fact, for the latter country, the share of exports in the top 5 products was above 95 percent in 2010/11). The evolution of the Herfindahl index is in line with the evolution of the previously discussed indicators (Figures 9a and 9b).

Figure 9a: Diversification Indicators for Resource Rich Economies.

<!-- Start of picture text --> 4000<br>Number of Products Exported<br>3500<br>2002-2003<br>3000<br>2006-2007<br>2500<br>2010-2011<br>2000<br>1500<br>1000<br>500<br>0<br>AZE KAZ RUS TJK TKM UZB<br><!-- End of picture text --> <!-- Start of picture text --> Share of Exports of Top 5 Products<br>1 2002-2003<br>0.9 2006-2007<br>2010-2011<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>AZE KAZ RUS TJK TKM UZB<br>0.9<br>Herfindahl Index<br>0.8<br>0.7 2002-2003<br>0.6 2006-2007<br>2010-2011<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>AZE KAZ RUS TJK TKM UZB<br><!-- End of picture text --> Source: Authors’ calculations based on Comtrade data from WITS.

Figure 9b: Diversification Indicators for Resource Poor Economies.

<!-- Start of picture text --> 4000<br>Number of Products Exported<br>3500<br>2002-2003<br>3000<br>2006-2007<br>2500 2010-2011<br>2000<br>1500<br>1000<br>500<br>0<br>ARM BLR GEO KGZ MDA UKR<br>Share of Exports of Top 5 Products<br>1<br>0.9<br>2002-2003<br>0.8<br>2006-2007<br>0.7 2010-2011<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>ARM BLR GEO KGZ MDA UKR<br>0.9<br>Herfindahl Index<br>0.8<br>0.7 2002-2003<br>0.6 2006-2007<br>0.5 2010-2011<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>ARM BLR GEO KGZ MDA UKR<br><!-- End of picture text --> Source: Authors’ calculations based on Comtrade data from WITS.

9
Table 7: Incremental Specification

||EQ1|EQ2|EQ3|EQ4|EQ5|EQ6|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ACD Confict|0.294<sup>_∗∗∗_</sup><br>(0.056)||0.278<sup>_∗∗∗_</sup><br>(0.053)|0.243<sup>_∗∗∗_</sup><br>(0.049)|0.288<sup>_∗∗∗_</sup><br>(0.055)|0.248<sup>_∗∗∗_</sup><br>(0.051)|
|TI Corruption||0.072<sup>_∗∗∗_</sup><br>(0.025)|0.06<sup>_∗∗∗_</sup><br>(0.021)|0.053<sup>_∗∗∗_</sup><br>(0.02)|0.066<sup>_∗∗∗_</sup><br>(0.022)|0.058<sup>_∗∗∗_</sup><br>(0.021)|
|Log of DB Start Business||||0.048<br>(0.05)||0.058<br>(0.045)|
|Log of DB Construction Permit||||0.013||-.010|
|||||(0.063)||(0.065)|
|Log of DB Import+Export||||-.043<br>(0.083)||-.034<br>(0.08)|
|Log of DB Register Property||||0.032<br>(0.029)||0.025<br>(0.031)|
|Log of DB Enforce Contract||||0.29<sup>_∗∗∗_</sup><br>(0.08)||0.319<sup>_∗∗∗_</sup><br>(0.087)|
|PIMI|||||0.012|-.002|
||||||(0.058)|(0.066)|
|Foreign frm or JV|0.247<sup>_∗∗_</sup>|0.247<sup>_∗_</sup>|0.216<sup>_∗_</sup>|0.182|0.232<sup>_∗_</sup>|0.201<sup>_∗_</sup>|
||(0.12)|(0.135)|(0.131)|(0.12)|(0.123)|(0.116)|
|World Bank|0.007|-.005|-.005|-.017|-.009|-.028|
||(0.096)|(0.09)|(0.096)|(0.094)|(0.094)|(0.092)|
|Bilateral Donor|0.187|0.164|0.175|0.149|0.169|0.134|
||(0.137)|(0.137)|(0.137)|(0.132)|(0.137)|(0.131)|
|Obs.|3322|3322|3322|3322|3322|3322|
|_R_<sup>2</sup>|0.899|0.898|0.9|0.901|0.9|0.901|

_Notes_ : Regression includes all controls (not shown) from the base model in table 4; dependent variable is the log of cost per km; all models control for work activity fixed effects, year fixed effects, an interaction between work type and five-year period fixed effects; base categories are actual costs; robust standard errors in parentheses, clustered at the country;<sup>_∗_</sup> ,<sup>_∗∗_</sup> ,<sup>_∗∗∗_</sup> denote significance at 10%, 5% and 1% levels. _Source_ : Authors’ analysis based on data described in the text.

36
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||||**RIF estimat**|**Table 9**<br>**es, North(exc**|**luding the we**|**st)**|||||
|---|---|---|---|---|---|---|---|---|---|---|
||_mean_|_10th_<br>_Percentile_|_20th_<br>_Percentile_|_30th_<br>_Percentile_|_40th_<br>_Percentile_|_50th_<br>_Percentile_|_60th_<br>_Percentile_|_70th_<br>_Percentile_|_80th_<br>_Percentile_|_90th_<br>_Percentile_|
|_A. Gender Differential_|||||||||||
|Male Managedplot|11.38***|9.619***|10.21***|10.64***|11.04***|11.33***|11.67***|12.05***|12.49***|13.28***|
||(0.0351)|(0.0573)|(0.0491)|(0.0411)|(0.0392)|(0.0301)|(0.0403)|(0.0369)|(0.0492)|(0.0547)|
|Female managedplot|11.34***|9.694***|10.33***|10.66***|10.97***|11.26***|11.67***|12.06***|12.61***|13.31***|
||(0.116)|(0.150)|(0.0833)|(0.120)|(0.0815)|(0.123)|(0.115)|(0.193)|(0.148)|(0.140)|
|Gender Differential|0.0389|-0.0754|-0.117|-0.0128|0.0761|0.0695|-0.00590|-0.0101|-0.116|-0.0308|
||(0.121)|(0.161)|(0.0967)|(0.127)|(0.0904)|(0.126)|(0.122)|(0.197)|(0.156)|(0.150)|
|_B. Aggregate Decomposition_|||||||||||
|Endowment Effect|-0.235***|-0.0248|-0.0694|-0.183**|-0.263***|-0.224***|-0.216**|-0.237**|-0.302**|-0.415***|
|Share of the|(0.0875)|(0.109)|(0.0924)|(0.0841)|(0.0791)|(0.0691)|(0.0885)|(0.0970)|(0.118)|(0.130)|
|Gender Differential|-604%|33%|59%|1430%|-346%|-322%|3661%|2347%|260%|1347%|
|Male Structural Advantage|-0|0|0|-0|-0|0|-0|-0|-0|-0|
|Share of the|(0.00175)|(0.00296)|(0.00144)|(0.00142)|(0.00122)|(0.00193)|(0.00159)|(0.00426)|(0.00369)|(0.00277)|
|Gender Differential|0%|0%|0%|0%|0%|0%|0%|0%|0%|0%|
|Female Structural Advantage|0.274***|-0.0506|-0.0477|0.170|0.339***|0.293**|0.210*|0.227|0.186|0.384**|
|Share of the|(0.102)|(0.166)|(0.110)|(0.127)|(0.0937)|(0.116)|(0.119)|(0.176)|(0.146)|(0.157)|
|Gender Differential|704%|67%|41%|1328%|446%|422%|-3559%|-2248%|160%|-1247%|
|**Observations**<br>Robust standard errors inparentheses.|***p<0.01,|**p<0.05,*p|<0.1. State fix|ed effects not d|isplayed.†in|**2050**<br>dicates dumm|yvariable||||

40
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14
municipalities (out of 2,457 municipalities) and 6,849 AGEBs.<sup>11</sup> Table A1 presents additional details on the geographic structure of Mexico.

Importantly, the publicly available data contains an AGEB-level identifier, which can be linked with the official AGEB shapefile. For rural areas, the data and associated shapefile identify localities, which are points rather than polygons. These were grouped together using the same AGEB shapefile into rural AGEBs. The survey collects income per capita, which is the welfare metric used to calculate the official poverty rate. With the AGEB-level identifiers and merged shapefile, we can link the survey data with predicted poverty rates and land classifications derived from satellite imagery.

To predict poverty for the entire country, we need data on the location of all households. For this, we create a “synthetic census” based on the 2010 census. For each AGEB, we estimate the number of households by dividing the total AGEB population in the 2010 census by the average household size, at the state-urban/rural level, in the 2014 household survey. We round to the nearest integer and use this to construct a synthetic census that links the “households” in an AGEB with the AGEB-level satellite indicators, which serve as the auxiliary data for the household model. This resulting synthetic census consists of just under 28 million records. In addition to the satellite imagery – which serves as the auxiliary data for the prediction exercises – we also include average household size at the state-urban/rural level, estimated with the 2014 MCS-ENIGH survey, which we use as population weights when aggregating across households to generate municipal poverty estimates.

# 3.2. Benchmark CONEVAL estimates

We compare the estimated poverty rates with a benchmark to evaluate the ability of the satellite imagery to improve poverty predictions. The benchmark used for evaluation is the municipal income poverty estimates, generated by CONEVAL using a combination of the 2014 MCSENIGH household survey and the 2015 Intercensus survey. The Intercensal survey is conducted every 5 years between two censuses, to update socio-demographic information at the national and

> 11 AGEBs (Áreas Geoestadísticas Básicas) are equivalent to sub areas in this context, while municipalities are target areas, or the areas at which we are interested in predicting poverty. Each municipality is made up of AGEBs, with larger municipalities generally having more AGEBs.

12
|Smallholder Cattle Development Project(02)||
|---|---|
|Smallholder Cattle Development Project(02)||
|Smallholder Cattle Development Project(02)||
|Smallholder Cattle Development Project(02)||
|Smallholder Cattle Development Project(02)||
|Smallholder Cattle Development Project(02)||
|Industrial EnergyConservation Project||
|Industrial EnergyConservation Project||
|Industrial EnergyConservation Project||
|Industrial EnergyConservation Project||
|Industrial EnergyConservation Project||
|Industrial EnergyConservation Project||
|ForestryInstitutions and Conservation Project|James J. Douglas|
|ForestryInstitutions and Conservation Project|James J. Douglas|
|ForestryInstitutions and Conservation Project|James J. Douglas|
|ForestryInstitutions and Conservation Project|James J. Douglas|
|ForestryInstitutions and Conservation Project|James J. Douglas|
|ForestryInstitutions and Conservation Project|James J. Douglas|
|ForestryInstitutions and Conservation Project|James J. Douglas|
|ForestryInstitutions and Conservation Project|James J. Douglas|
|ForestryInstitutions and Conservation Project|James J. Douglas|
|RailwayTechnical Assistance Project|Adhemar Byl|
|RailwayTechnical Assistance Project|Adhemar Byl|
|RailwayTechnical Assistance Project|Adhemar Byl|
|RailwayTechnical Assistance Project|Adhemar Byl|
|RailwayTechnical Assistance Project|Adhemar Byl|
|RailwayTechnical Assistance Project|Adhemar Byl|
|Tree Crops ProcessingProject|Muhammad O. Farruk|
|Tree Crops ProcessingProject|Muhammad O. Farruk|
|Tree Crops ProcessingProject|Muhammad O. Farruk|
|Tree Crops ProcessingProject|Muhammad O. Farruk|
|Tree Crops ProcessingProject|Muhammad O. Farruk|
|Tree Crops ProcessingProject|Muhammad O. Farruk|
|Tree Crops ProcessingProject|Muhammad O. Farruk|
|Tree Crops ProcessingProject|Muhammad O. Farruk|
|Tree Crops ProcessingProject|Muhammad O. Farruk|
|Sumatera and Kalimantan Power Project|Kurt F. Schenk|
|Sumatera and Kalimantan Power Project|Kurt F. Schenk|
|Sumatera and Kalimantan Power Project|Kurt F. Schenk|
|Sumatera and Kalimantan Power Project|Kurt F. Schenk|
|Sumatera and Kalimantan Power Project|Kurt F. Schenk|
|Sumatera and Kalimantan Power Project|Kurt F. Schenk|
|Sumatera and Kalimantan Power Project|Kurt F. Schenk|
|Sumatera and Kalimantan Power Project|Kurt F. Schenk|
|Yogyakarta Upland Area Development Project|Wiranto Soehendro|
|Yogyakarta Upland Area Development Project|Wiranto Soehendro|
|Yogyakarta Upland Area Development Project|Wiranto Soehendro|