rafmacalaba/datause-encoder-data huggingface dataset:
26 Anex 3 (Cont'd) **Lisc** Regresdon Re1ut: **The Deteinauts** of Repetition in Guatemala (Dependent Variable: REPEAl)

|Vaiable|Logit<br>Coefficient|Variable<br>Mean|Margsia<br>Effect (%)|
|---|---|---|---|
|AGE|0.629<br>|11.660|14.31|
||(14.3)|||
|MALE|0.037<br>(0.4)|.538|0.84|
|INDIO|_0.MIS_|.29S|11.72|
||(3.9)|||
|RURAL|0.708<br>|.S77|16.11|
||(6.7)|||
|SM|-0.293|3.199|-6.67|
||(12.3)|||
|YFAM(xlOOO)|40.186|.401|-4.23|
||(1.8)|||
|MALEHHD|0.304|.863|6.92|
||(2.0)|||
|PRIVATE SECrOR|0.074<br>|.369|1.68|
||(0.4)|||
|PUBLIC SECTOR|-0.412|.105|-9.38|
||(2.0)|||
|SELFEMPLOYED|0.017|.414|0.39|
||(0.1)|||
|NSEBS|0.037|2.782|0.84|
||(7.1)|||
|NROOMS|-0.21S<br>_(5.2)_|2.912|-4.89|
|KITCHEN|0.0S2|.429|1.18|
||(0.S)|||
|RUNWATER|_-0.33S_|.S32|-7.62|
||(3.1)|||
|Consdant|-S.859|||
|Chi-squaw<br>N|971<br>2974|||

Note: Numbers in p_mumu _puut t-valuus.
population make it a key destination for these services. A number of Indian providers have opened up call center operations in the Philippines during the past two years, to diversify their operations base and further lower costs (Nair, 2010).

The offshore services industry in the Philippines began less than 10 years ago. During 2004 to 2007, the industry showed growth rates on the order of 50% (see Table 5). In 2008 and 2009 the IT-BPO services growth rate declined to 24% and 18%, respectively. However, comparing the same indicators with India (see Table 6), the Philippines presents higher growth rates in both industry revenues and employment. In 2009, the growth rate for offshore services employment in the Philippines was 19%, while in India it was only 11%.

**Table 5. Philippines IT – BPO Industry, 2004-2009**

||**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|
|---|---|---|---|---|---|---|
|**Annual GDP Growth(%)**|6.10%|5.10%|5.40%|7.10%|3.80%|0.90%|
|**Revenues in Offshore Services**<br>**Industry: Philippines**(US$ billions)|1.5|2.2|3.3|4.9|6.1|7.2|
|**Growth Rate Offshore Services**<br>**Industry**||47%|50%|48%|24%|18%|
|**Offshore Services Employment**|101,00|163,000|236,000|300,000|372,000|442,000|
|**Growth Rate Offshore Services**<br>**Employment**||61%|45%|27%|24%|19%|

Source: National Statistical Coordination Board; Business Processing Association of the Philippines The past two years have confirmed the consistent rise of the Philippines as a leading BPO destination. In the opinion of some market experts, the crisis has accelerated the takeover of India's back office supremacy by the Philippines (SiliconIndia, 2009). In 2007, India had over 300,000 call agents, while the Philippines had just half of that. Today, India and the Philippines have equal strength with 350,000 employees each in voice BPO (The Economic Times, 2010).

According to the result of a recent survey of BPO providers conducted by the Business Processing Association of the Philippines and Outsource2Philippines, 49% of executives say that in 2009-2010 they will expand their headcount by at least 11% and 33% of the respondents confirm that the crisis has accelerated their service expansion process. The survey also reveals that only 5% declared that the effect of the crisis was “very significant.” Finally, the poll shows that some innovative firms are identifying new opportunities as a result of the downturn (Philippine Daily Inquirer, 2009; TeamAsia, 2009). Overall, it is estimated that the BPO industry in the Philippines will create 90.000 new jobs during 2010 (ABS-CBN news.com, 2010).

Many of the leading companies in the BPO industry, such as Genpact, Wipro BPO, Intelenet, Aegis BPO and Firstsource, are scaling up their investments in the Philippines. Wipro BPO, one of the largest Indian service companies that is specialized in IT and BPO, set up a new BPO center in Cebu City, the Philippines that hired 1,000 workers (SiliconIndia, 2009). Similarly Convergys opened three new call centers employing 3,000 workers in April 2009 (TeamAsia, 2009).

19
different and, in many ways, more challenging environment in which to test satellite‐based crop yield measurement approaches.

Second, whereas BL17 relied on farmer self‐reported data on maize production, this paper uses objective measures based on survey field team harvests of maize grain for 64 m<sup>2</sup> subplots within each plot (“crop cuts”), as well as whole plot harvests for approximately 1 random half of our sample (“full plot harvests”). Thus, we are able to compare different ground‐based measures with each other, and with the satellite data.

Third, the study uses data from the Copernicus program’s Sentinel‐2A satellite, which has coarser spatial resolution but more spectral bands than the Skysat sensor used in BL17. Furthermore, whereas Skysat data are currently only available for selected locations, Sentinel‐2A and its recently launched sister satellite Sentinel‐2B each capture imagery every 10 days for the entire land surface of the Earth, with an effective 5‐day repeat for the Sentinel‐2 duo since June 2017. These data are quickly made available to the public at no cost. For these reasons, Sentinel‐2 represents an attractive option for estimating yields over large regions.<sup>2</sup> All plot‐level measures of maize yield, including farmer‐reported self‐reported production per hectare (SR), sub‐plot crop cut production per hectare (CC), full plot crop production per hectare (FP), and variants of remotely sensed production per hectare (RS), rely on GPS‐based plot areas; are compared to each other using standard statistical approaches; and are used to study the sensitivity of the associations between maize yield and various production factors measured through a combination of a household survey and extensive soil sampling.

The paper is organized as follows. Section 2 describes the data. Section 3 presents the comparisons among ground‐based yield measures; between ground‐ and satellite‐based yield measures; and the results from the estimations of maize yield regressions for each yield variant of interest. Section 4 concludes.

# **2. Data**

MAPS: Methodological Experiment on Measuring Maize Productivity, Soil Fertility and Variety is a two‐round household panel survey that was conducted in Eastern Uganda to test the relative accuracy of subjective approaches to data collection vis‐à‐vis objective survey methods for maize yield measurement, soil fertility assessment, and maize variety identification. Both survey rounds were implemented by the Uganda Bureau of Statistics, with technical and financial assistance

> 2 BL17 focused on field campaigns in 2014 and 2015, before Sentinel‐2 was operational.

4
grew twice as fast as credit to corporations<sup>75</sup> and Greece was the country with the fastest increase in construction. In 2007, Greece and Portugal had around 45% of the stock of total credit with households, with Ireland and Spain close behind.

**Lesson 10a: Structural reforms before and after crises have different effects.**

**Lesson 10b: Wage cuts in a downturn and during fiscal consolidation may not promote growth recovery (particularly when employment also falls).**

_<u>While the short run effects of negative shocks may be equally strong in countries with more and less flexible economic systems and strong institutions (see Section 1), the recovery is faster in the former. This is because resources reallocate faster and creditor perceptions are more favorable, and it often takes time for reforms to have a net positive impact on output. Structural reforms to make markets more flexible during or in the immediate aftermath of a downturn can have unexpected (even negative) effects.</u>_ _<u>Falling real wages during fiscal consolidation and household deleveraging, aggravate declines in domestic demand and worsen the downturn.</u>_

**Figure 14.a: Global competitiveness ranking**

**Figure 14.b: Resolving insolvency ranking**

<!-- Start of picture text --> 0<br>20<br>40<br>60<br>80<br>100<br>Greece Ireland Portugal Spain<br>Source: World Economic Forum,<br>Global Competitiveness Reports.<br>100<br>95<br>90<br>85<br>80<br>75<br>70<br>65<br>60<br>55<br>50<br>Greece Ireland Portugal Spain<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>2000 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>10<br>20<br>30<br>40<br>50<br>60<br>70<br>80<br>Greece Ireland Portugal Spain<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Higher rank means less cumbersome regulation Source: The World Bank, Ease of Doing Business Database.

**Figure 14.c: Control of corruption score               Figure 14d: Product market regulation**

<!-- Start of picture text --> 1998 2003 2008 2013<br>0<br>5<br>10<br>15<br>20<br>25<br>30<br>35<br>Greece Ireland Portugal Spain<br><!-- End of picture text --> Percentile rankings (0 lowest rank, 100 best performance) Source: The World Bank, Worldwide Governance Indicators.

<!-- Start of picture text --> Among 34 OECD members, higher rank (lower number)<br>means less regulation.<br>Source: OECD<br><!-- End of picture text --> _<u>Business climate and governance: when policy, institutions and perceptions follow each other.</u>_ In comparative terms, Ireland, by far the richest country in the group, has always had the best business

> 75 In Portugal the number was 16 pp higher and in Spain, 10 pp higher.

34
wealth. For these reasons the most comprehensive population-scale study<sup>17</sup> outside the U.S. and Europe, a population-scale study in China, finds a strong _positive_ relationship between pollution and income. More broadly, for countries where anthropogenic sources of pollution dominate, there is a long recognized potential trade-off between urban-based development and environmental quality.

A primary reason that the few existing studies in LMICs have been mostly limited to urban areas is that data on air pollution and income or wealth are disproportionately available for cities. To overcome this challenge across a broad geographic scale we leverage recently released spatially resolved estimates of wealth for 103 LMICs.<sup>25</sup> These relative wealth indices (RWI) represent the most comprehensive micro-level wealth estimates to-date. They were derived from machine learning models trained on survey based measures of wealth. We combine these relative wealth estimates with global monthly spatially resolved estimates of ambient PM2 _._ 5 for 2015-2020 that are derived from a combination of satellite observations and modeled output.<sup>29</sup> Together, the data allow us to estimate the correlation between relative wealth and average ambient PM2 _._ 5 levels across grid cells in more than 100 LMICs at increasingly fine scales: across the full sample, separately within each country, and within cities.

For the pooled sample we regress average PM2 _._ 5 on relative wealth at the grid-cell-level and include separate intercepts for each country (Equation 1). The intercepts (fixed effects) account for baseline differences in wealth and pollution across countries and for the fact that wealth measures are normalized at the country level. The estimated coefficient on relative wealth in this specification provides a measure of average within-country correlation between wealth and pollution. Next, we estimate separate regressions for each country again regressing cell-level average PM2 _._ 5 on cell-level relative wealth (Methods). This exercise provides us with countrylevel estimates for the within-country correlation between wealth and pollution and allows us to assess which factors contribute to pollution being progressive or regressive in different settings. We next restrict our sample to large urban areas and estimate city-specific regressions in order to assess how pollution varies with wealth across neighborhoods within cities. Finally, we explore potential drivers of heterogeneity in both country- and city- level relationships.

# **2 Results**

While wealthier LMICs tend to have lower pollution on average (Section SI-1.1), we find that the average within-country correlation between wealth and ambient pollution is positive. This relationship is seen both in the raw data and in our regression results. Areas with the lowest average PM2 _._ 5 concentrations have the lowest average RWI levels while areas with the highest PM2 _._ 5 concentrations have the highest average RWI (Figure 1a). Results from our preferred 3
are interested in working and have looked for a job. Overall, the findings of this study suggest that social norms “campaigns” require detailed contextual knowledge and diagnostic surveys to help tailor messages to their appropriate targets.

In Jordan, it would be useful to update social norms on general views towards FLFP. Although 96% of people believe that it is okay for women to work, they expect that only 63% of people will approve. Any campaign to promote or encourage FLFP could use this as a starting point. The views of husbands, and wives’ perceptions of those views, are critical. Given the critical role men play in women’s labor force participation decisions, interventions could usefully focus on and target men as well. Campaigns might be developed to engage men as active partners in women’s empowerment. Finally, aspects of job design such as working hours and childcare, remain important barriers. Promoting flexible work hours or remote-work options might enable more women to work.

24
# Figure 2: _GIC_ Brazil 1995–2007

<!-- Start of picture text --> ooooooo oo o oo GICCounterfactual GICAverageCounterfactual Average<br>o oo oo o o o o o o o<br>oo o oooooo o oooooooooooooooooo oo o ooooooooooooooooo o ooooooooooooooo o oooo o ooooooooo o ooo<br>ooooooo o ooooo o ooooo oo ooooooooooooooo o ooo oooo ooo oo ooooooo o ooooooooo oo oooo o oo ooo oooooooooooooooo o oo o oooo oo oooooooooooo o ooooooooooooooooo oo ooo<br>0.0 0.2 0.4 0.6 0.8 1.0<br>quantiles<br>0.4<br>0.2<br>GIC<br>0.0<br>-0.2<br><!-- End of picture text --> and the mean log deviation, which is more sensitive to income gaps at the bottom of the distribution, lost almost 20% of its initial value.

This pro-poor pattern is also evident in the fact that the average growth rate across quantiles was 13 _._ 8% - higher than in the US - despite a near stagnant average wage. Unsurprisingly, then, both the constant distribution and the distribution-neutral growth hypotheses are resoundingly rejected at 1% level of significance for Brazil as well, in both the Kolmogorov-Smirnov and Cram ́er-von Mises tests. This can be seen in Table 5, which collects the results for the KS and CVM tests ( _V_ 1 _n_ and _V_ 2 _n_ , respectively).

As in the US case, the counterfactual growth incidence curve lies everywhere below the _GIC_ , and has a very similar shape. This parallelism suggests that the main drivers of distributional heterogeneity - which in this case were highly equalizing - belong to the realm of changes in economic structure, affecting remuneration patterns and unobserved worker characteristics. One plausible such candidate driver was the sustained rise in Brazil’s minimum wage over this period, which is both consistent with the shape of the _GIC_ , and with earlier findings in the literature (e.g. Ferreira, Firpo, and Messina (2016)). Changes in the joint distribution of observed attributes - gender, age and education - on the other hand, had roughly equi-proportional effects across the distribution. These effects were generally positive - i.e. wage-increasing - as one would expect from rising experience and educational levels.

30
|_xual be-_<br>_or sexu-_<br>_e)_<br>(10)|-0.311|(0.314)<br>[0.536]<br>-1.089<br>(1.735)<br>[0.624]<br>0.211<br>(0.926)<br>0.299***<br>(0.101)<br>-0.106<br>(0.119)<br>[0.624]|580<br>0.185<br>Yes<br>-0.213|percent level,|equal to 1 if<br>rackets, based<br>ld size, wealth<br>nguage.|
|---|---|---|---|---|---|
|_Risky se_<br>_havior (f_<br>_ally activ_<br>(9)|-0.337|(0.314)<br>[0.455]<br>0.300***<br>(0.100)<br>-0.107<br>(0.118)<br>[0.789]|580<br>0.184<br>Yes<br>-0.213|e 1, 5 and 10|” is a dummy<br>es in square b<br>mily, househo<br> as a native la|
|_towards_<br>_ual behav-_<br>(8)|-0.085|(0.218)<br>[0.704]<br>-1.383<br>(1.057)<br>[0.455]<br>0.562<br>(0.735)<br>0.271***<br>(0.061)<br>0.034<br>(0.073)<br>[0.646]|924<br>0.140<br>Yes<br>-0.107|fcance at th|Opposite sex<br>usted p-valu<br>e with the fa<br>eaks Yoruba|
|_Attitudes_<br>_risky sex_<br>_ior_<br>(7)|-0.138|(0.207)<br>[0.625]<br>0.273***<br>(0.060)<br>0.032<br>(0.072)<br>[0.789]|924<br>0.139<br>Yes<br>-0.107|* denote signi|of Shuga. ”<br>1 . FWER adj<br>e, does not liv<br>n, muslim, sp|
|_esting_<br>(6)|-0.545*|(0.289)<br>[0.178]<br>0.840<br>(1.143)<br>[0.519]<br>-0.456<br>(1.011)<br>0.300***<br>(0.072)<br>0.012<br>(0.083)<br>[0.881]|924<br>0.222<br>Yes<br>-0.164|***, ** and|the messages<br>missing Y _t−_<br>spoken, singl<br>ary educatio|
|_HIV t_<br>(5)|-0.512*|(0.274)<br>[0.106]<br>0.301***<br>(0.071)<br>0.013<br>(0.082)<br>[0.876]|924<br>0.222<br>Yes<br>-0.164|(80 clusters).|sistent with <br>hat have non-<br>ation, english <br>er than second|
|_titudes_<br>(4)|-0.124|(0.228)<br>[0.587]<br>1.317<br>(0.961)<br>[0.386]<br>-0.072<br>(0.592)<br>0.397***<br>(0.061)<br>-0.148*<br>(0.075)<br>[0.156]|924<br>0.179<br>Yes<br>-0.316|centre level|es more con<br>ork friends t<br>ears of educ<br>tained high|
|_HIV at_<br>(3)|-0.082|(0.232)<br>[0.812]<br>0.395***<br>(0.060)<br>-0.148*<br>(0.075)<br>[0.154]|924<br>0.177<br>Yes<br>-0.316|the screening|ond to outcom<br> includes netw<br>nding school, y<br>ion, mother ob|
|_owledge_<br>(2)|0.540|(0.328)<br>[0.199]<br>2.493**<br>(1.234)<br>[0.228]<br>-1.540*<br>(0.786)<br>0.438***<br>(0.060)<br>-0.113<br>(0.072)<br>[0.238]|924<br>0.250<br>Yes<br>-0.185|ustering at|able corresp<br>nd. Sample <br>rrently atte<br>dary educat|
|_HIV kn_<br>(1)|0.640*|(0.332)<br>[0.106]<br>0.434***<br>(0.060)<br>-0.110<br>(0.072)<br>[0.236]|924<br>0.249<br>Yes<br>-0.185|adjusted for cl|ependent vari<br>er treated frie<br>emale, age, cu<br>er than secon|
|_Dep. Var. (Yt):_|Friend of Treated|Friend of Treated*Opposite Sex<br>Opposite Sex<br>Y_t−_1<br>Friend of Treated * Y_t−_1|Observations<br>R-squared<br>Controls (_a_)<br>Mean Dep. Var. (Control)|Notes. Standard errors in parentheses|respectively. Increasing values of the d<br>respondent has the opposite sex as his/h<br>on 10,000 replications.<br>(a) Controls in each regression include: f<br>index, home owner, father obtained high|

44
4 that some of the poorest African countries have been hit hardest by adversA terms of trade changes over the past 30 years.

chese examples show that the fears raised by elasticity pessimists are very much alive in Lie context of African commodity exports. The problem is viewed as being especially serious for countries whose exports are concentrated in commodities that are said to exhibit low import demand elasticities (e.g., Cote d'Ivoire which exports coffee and cocoa). Rhetoric has been strong on the part of proponents as well as opponents of pessimism. But evidence provided to date on either side is sketchy. We believe that therc is a real need to study the issue in depth and understand whether the export pessimists are justified and if so what can be done to maximize these countries' gains from exports. The present paper is a first effort in this direction.

Given the low price elasticity of world import demand, an increase in exports of coffee, cocoa and tea will lead to lower overall export revenues. However, some countries may gain. The distribution of gains and losses are examined in detail in the paper.

A central premise behind the paper is that domestic-policy instruments should be employed to promote efficiency at home while trade policy instruments should be used to deal with problems related to foreign demand.<sup>1</sup> Thus, the policy measures designed to correct domestic price distortions and real wage and exchange rate misalignment are well advised. The issue which deserves closer scrutiny, however, is whether 1 This view derives from the theory of the second best which emphasizes that distortions should be attacked at the source. See Bhagwati (1971) for further details.
16 on the streets of Bangkok; it smothers pedestrians and motorists alike and occasionally creates a safety hazard in traffic, literally blinding motorists in a black fog.

# Indonesia

The combination of densely congested traffic, poor vehicle maintenance, and large numbers of diesel vehicles and two-stroke engined (smoky) motorcycles has contributed to elevated air pollution levels in Jakarta and other large urban centers. While air quality data are not well documented, the carbon monoxide and ambient lead levels exceed internationally accepted standards.

# **Philippines**

The air pollution problem in Manila is quite serious. A large proportion of the vehicles is diesel-fueled and most of these, especially the "Jeepneys", emit excessive smoke. Owing to a lack of instrumentation, actual air quality data do not exist at present. However, it appears, that particulate, lead, carbon monoxide and possibly ozone levels exceed intemationally accepted standards. Based on an analysis of fuel consumption, motor vehicles are estimated to account for approximately 50% of particulates, 99% of carbon monoxide, 90% of hydrocarbons, and 5 _%_ of sulfur dioxide. Fuel quality is very poor as reflected by the sulfur content of as much as 1.0% by weight (compared to 0.3% in the US) and a lead content of up to 1.16 grams per liter (compared to 0.15 in the European Community).

# **Singapore**

The motor vehicle related air pollution in Singapore is primarily related to diesel particulates, though the problem is clearly not as severe as in many other Asian cities. There is also concern with carbon monoxide and ambient lead levels, which exceed internationally accepted standards.

The overall vehicle population continues to increase with rising incomes (about 25 % more vehicles were on the road in 1989 compared to 1980). The major problems are related to substandard maintenance of vehicles and the relatively high density of vehicles throughout the day in the city center. Authorities are concerned because surveys conducted by the Pollution Control Department in 1989 showed that the percentage of smoky vehicles on the roads had increased from 7% to 10% over a six-month period. Of the smoky vehicles stopped by enforcement officers on the roads, more than 87% were found to emit excessive black smoke as compared to 70% the previous year.

# **Mexico**

The combination of high altitude, special meteorological conditions, and a rapid rate of motorization have transformed Greater Mexico City, the world's largest metropolis (with a population of 19 million) into a virtual 'gas chamber'. The number of automobiles in Mexico