rafmacalaba/datause-encoder-data huggingface dataset:
underlying measure of ENSO cycles, which alter ocean temperature. Data on ONI are publicly available from the National Weather Service Climate Prediction Center.<sup>6</sup> These data are matched to every cooperative purchase in the logbook data. The second half of 2007 and first half of 2008 were marked by a “cold episode” (more negative values), while the second half of 2009 saw the onset of a “warm episode” (more positive values).

Warmer ocean temperatures have three potential effects on organisms. First, they negatively affect juvenile recruitment of some species and positively affect recruitment of others. These population growth rate effects ultimately impact catch with a lag that generally ranges from one to seven years. Second, warmer temperatures can either positively or negatively affect adult population abundance by causing individuals to migrate. Third, adult size may be affected through changes in individual growth. The model focuses on population growth rates and does not incorporate the other two effects for the sake of tractability. Therefore, the empirical tests also focus on the first effect.

Finally, we conducted a thorough review of the ecological literature to construct a detailed classification of species on two dimensions. First, we classify species as “large scale” (i.e., large scale of movement, or highly mobile) or “small-scale” (i.e., small scale of movement, or less mobile). This classification is based primarily on knowledge of the movement of adult organisms, rather than on knowledge of larval dispersal. Second, we create a variable equal to 1 if higher ONI has a positive effect on recruitment (and hence population growth rates) and equal to -1 if higher ONI has a negative effect on recruitment. If the effect on recruitment is unknown to us or if there is no effect on recruitment, we set the variable to 0.

Table 1 provides summary statistics on the final merged data set.<sup>7</sup> The first panel shows summary statistics for the three cooperatives together, the second panel shows statistics for Pichilingue and Sargento only, and the third panel shows statistics for Abreojos only.

While the logbook data is at the individual fishing transaction level, the data are aggregated in our tests of the model’s implications to the species-year-month-week level by averaging prices and totaling catch. This is because our own field visits suggested that cooperatives do not usually

> 6. Available at http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml. 7. Observations where the market price is less than the cooperative price are dropped due to concerns about measurement of market prices. This affects only 762 transactions out of approximately 42,000 individual fishing transactions for which we have cooperative price data.

1
Table 10. Impacts of EGPP Participation on Non-food Consumption

|Log (monthly<br>|To|tal|Per c|apita|Num of<br>|
|---|---|---|---|---|---|
|expenditure)|IV|Heckman|IV|Heckman|obs|
|Clothes and shoes|-0.069|-0.077*|-0.095**|-0.095**|6,718|
||(0.049)|(0.046)|(0.048)|(0.045)||
|Health care (men)|0.375***|0.372***|0.375***|0.372***|6,256|
||(0.129)|(0.121)|(0.129)|(0.121)||
|Health care (women)|0.344***|0.334***|0.344***|0.334***|6,603|
||(0.124)|(0.117)|(0.124)|(0.117)||
|Education (boys)|-0.055|-0.192|-0.055|-0.192|2,508|
||(0.188)|(0.175)|(0.188)|(0.175)||
|Education (girls)|-0.220|-0.241|-0.220|-0.241|2,618|
||(0.199)|(0.186)|(0.199)|(0.186)||
|Housing|0.020|-0.013|-0.006|-0.032|6,675|
||(0.081)|(0.077)|(0.081)|(0.077)||
|Others (leisure, festivals,|0.842***|0.799***|0.820***|0.784***|6,552|
|gifts, etc.)|(0.217)|(0.204)|(0.216)|(0.204)||
|Covariates,fixed effects|Y|es|Y|es||

Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The table presents estimated coefficients of EGPP participation on various outcomes using two different approaches with a full specification based on column (4) in Table 7.

Households’ disposable incomes are affected by labor market earnings, savings and loans decisions; thus, we also examined the impact of EGPP participation on these decisions. Table 11 reports that EGPP participation has not changed labor supply decisions of the households maintaining the same level of employment both in numbers and ratios. Also, the propensity to have a migrant worker in the household did not vary by EGPP participation. To contrast, EGPP households have significantly reduced their amount of outstanding loans. Recall that about 70 percent of the sample households have an outstanding loan, and the median [average] loan amount is about BDT 15,000 [30,000] — about 1.9 [3.8] times the monthly household expenditure. The EGPP participating households experience about a 40 percent reduction in the amount of outstanding loans, equivalent to BDT 6,750 at the median level, larger than any other reallocation of expenditure discussed previously.

Table 11. Impacts of EGPP Participation on Household Decisions

|Outcome|T<br>IV|otal<br>Heckman|Num of obs|
|---|---|---|---|
|Number of employed|0.038|0.083|6,812|
||(0.071)|(0.067)||

28
# **3. Data**

This paper gathered data from 67 ports with container terminals in 27 countries in the region, covering 18 ports in Central America and Mexico, 14 ports in the Caribbean and 35 ports in South America (Table 2). All are gateways for imports/exports traded in containers for each country, representing around 90% of the container cargo handled by the region.

_Table 2: Summary of the Ports in the Sample_

|**Region **|**Country**|**Container Ports**|
|---|---|---|
||**Costa Rica**|Puerto Caldera,Puerto Limón|
||**ElSalvador **|Acajutla|
|**Central**|**Guatemala**|Puerto Barrios,PuertoQuetzal,Santo Tomás de Castilla|
|**America and**|**Honduras**|Puerto Castilla,Puerto Cortés|
|**Mexico**|**Mexico**|Altamira,Ensenada,Lázaro Cárdenas,Manzanillo-MEX,Progreso,Veracruz|
||**Nicaragua**|Corinto|
||**Panama**|Balboa,Colón CT,Puerto Manzanillo-PAN|
||**Aruba**|Oranjestad|
||**Bahamas**|Freeport|
||**Barbados**|Bridgetown|
||**Cuba**|Havana|
||**Dominican Republic**|Caucedo,Rio Haina|
||**Martinique**|Fort-De-France|
|**Caribbean**|**Guadeloupe**|Pointe-A-Pitre|
||**Jamaica**|Kingston|
||**Netherlands Antilles**|Willemstad|
||**Puerto Rico**|San Juan|
||**Saint Lucia**|Vieux Fort|
||**Trinidad and**<br>**Tobago**|Point Lisas, Port of Spain|
||**Argentina**|Buenos Aires(excl. Exolgan),Exolgan,Rosario,Zarate|
||**Brazil**|Belém, Fortaleza, Iitajaí, Manaus, Paranaguá, Pecém, Rio De Janeiro, Vitória,<br>Rio Grande,Salvador,Santos,São Francisco do Sul,Sepetiba,Suape|
||**Chile**|Antofagasta,Arica,Iquique,Lirquén,San Antonio,San Vicente,Valparaíso|
|**South**<br>**Ai**|**Colombia**|Barranquilla,Buenaventura,Cartagena,Santa Marta|
|**merca**|**Ecuador **|Guayaquil|
||**Peru**|Callao,Paita|
||**Uruguay**|Montevideo|
||**Venezuela**|La Guaira,Puerto Cabello|

The database was primarily populated from information published in Containerization International Yearbook from various years. It spans 10 years (1999–2009) and contains key port infrastructure indicators such as berth length, port area, number of mobile and quay cranes<sup>3</sup> , and number of ship-to-shore (STS) gantry cranes. It also includes annual container throughput in TEUs. Since the focus of this paper is on container terminals, the database is limited to output measures related to the volume of containerized cargo. This is the same approach as in Coto-Millan et al. (2000), supported by the fact that a large portion of the cargo in Latin America is dispatched in containers and this proportion is rapidly increasing, as discussed in Section 1. The original data are available at the terminal level; however, figures were aggregated at the port level when needed for comparative purposes.

The database includes data for ports with a wide range of sizes and infrastructure endowments (Table 3). On average, Caribbean ports in the sample move annually about 455,000

> 3 Only cranes with over 14 tons capacity were considered, a capacity required to handle a 20-foot container.

9
The trade data are reported in current US dollars, and are deflated by the US CPI. We recognize that ideally we would use bilateral price indices to deflate trade between different country pairs but such bilateral deflators are not available. However, the presence of the very general fixed effects has the consequence also of implicitly deflating the trade data. The data are implicitly deflated by prices that vary by importer, product and time; by importer, product and exporter; and by exporter, product and time. They are, however, not deflated by prices that vary along all four dimensions (importer, exporter, product, and time).

Exchange rate data are from the IMF’s International Financial Statistics (IFS) database. In the theoretical framework, the key price that determines the transmission mechanism of exchange rate changes is the price in the importing market charged by Chinese exporters. Equation (6) suggests that this price is determined by the domestic price of the good in China, the Chinaimporter bilateral exchange rate and the passthrough. Since we parameterize the passthrough, the relevant changes to focus on are those stemming from changes in domestic prices in China and the exchange rate. Hence, our bilateral exchange rate is deflated by China’s CPI.

Before we present the econometric results, it is worth looking at some basic data. Figure 2 plots China’s average index of competition (where the average is over all exporters and products). The index is measured in two ways consistent with the discussion in the analytical section. Both the VBI and the CBI rise over time, consistent with China becoming a bigger and more diverse exporter. The CBI shows in particular that by 2008, on average, China occupies nearly all the product space of other developing country exporters. Figures 3a and 3b plot the same indices but disaggregated by region. These charts show that China’s overlap with all regions has risen steadily over time, with the level of the overlap greatest with other exporters in Asia (over 95 percent in 2008 for the CBI) and least with Europe and Central Asia.

14
# **Prospects of Estimating Poverty with Phone Surveys: Experimental Results from Serbia**

Vladan Boznic<sup>#</sup> Roy Katayama* Rodrigo Munoz<sup>†</sup> Shinya Takamatsu* Nobuo Yoshida*

**Keywords:** poverty data collection; telephone interview; randomized experiment, survey to survey imputation; Real-time Poverty Data

**JEL:** C81, C83, I32

# Statistical Office of the Republic of Serbia, Belgrade, Republic of Serbia

† Sistemas Integrales, Santiago, Chile

* World Bank Group, Washington DC., USA

We are grateful to the staff at the Statistical Office of the Republic of Serbia (SORS), in particular Maja Radenkovic, for their careful attention and speedy implementation of the survey. We are also grateful to Caterina Ruggeri Laderchi for creating this research opportunity and facilitating the partnership with SORS, to Amparo Ballivian and Joao Pedro Wagner de Azevedo for sharing their experience from the Listening to LAC project and providing advice for this study, to Ghazala Mansuri and Gabriela Inchauste for their helpful suggestions on sampling and experimental design, to Talip Kilic for his collaboration on previous survey to survey imputation analysis in Serbia and suggestions on the design of experiment, to Diane Steele and Amparo Ballivian for comments on modeling and the data collection process as peer reviewers of this report, and to Gero Carletto for advice on how to place this approach in the framework of household survey data collection. Lastly but not least, we would like to thank Antonius Verheijen, Lazar Šestović, Cesar A. Cancho, and Pierella Paci for helpful comments and kind assistance.
103

|% French|0.284<br>(0.083)|0.117<br>(0.116)|
|---|---|---|
|% Syro.Lebanese|.0.084<br>(0.090)|**.0293'**<br>(0.1M|
|% Other Foreign|(0306)<br>(0.118)|0.748<br>(0.168)|
|Effective Rate of<br>Protecion|0.020<br>(0.012)|Q005<br>(0.017)|
|% Exports|0.217*<br>(0.083)|0.137<br>(0.118)|
|Not Revenue<br>Per Worker|.0.30E-4<br>(0.23E-4)|.0.29E.4<br>(0-33E-4)|
|Sales Growth<br>Over_5_Years|0.820<br>(0323)|1.018'<br>(0.456)|
|RZ|0.45|0.49|
|_N_|_S13_|513|

* , dsnificant at 5 % stistical test leveL

+ - signifcant at 10 % staistical test leveL Source: Worker data from 17 firms ,he results in Table 7.1 suggest that union membership does not significantly improve most workers' base wage or total earnings. Once the effects of firm ownership and the workers nationality, sex, education, and experience are taken into account, trade union membership does not appear to add to one's earnings. This finding could be exFlained by the fact that our sample contains very few nonunionized workers and that the government extends the terms of the collective agreement to nonunionized workers as well. However, these findings are also consistent with those from our interviews, where it became clear that unions are seen primarily as protectors of the workers' job rights and enforcers of the Labor Code. For example, they protect the day laborer's right to obtain a permanent job after having worked for a specified length of time.

Each additional year of education raises the base salary by 7.4 percent and the total compensation by 11.1 percent. These
The Behaviour of Russia, Firms in 1992 Evidence from a Swvey Simon Commander, Leonid Libxmnan, Cecda Ugz and Ruslan Yemtsov<sup>1</sup> The World Bank, Institute for Economic Forecastng, Russian Academy of Sciences and Moscow State University The authors thunk Timothy King and Jeni Klugman for supporting the work. The paper is closely keyed to - - a companion text *Wage and Employment Decisions in the Russian Economy by the same authors which is available on request.
Table 5: Frequency of Female Speech

||||_D_|_ependent var_|_iable:_||
|---|---|---|---|---|---|---|
||Citizens|Citizens|Admin.|Female Spee<br>Admin.|ch<br>Politicians|Politicians|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Female President|0.10|0.11|0.21<sup>_∗∗∗_</sup>|0.19<sup>_∗∗∗_</sup>|0.90<sup>_∗∗∗_</sup>|0.88<sup>_∗∗∗_</sup>|
||(0.08)|(0.09)|(0.09)|(0.08)|(0.07)|(0.08)|
|Female Atendance|0.001<sup>_∗∗_</sup>|0.001<sup>_∗∗_</sup>|0.0003|0.0000|_−_0.0002|_−_0.0002|
||(0.0005)|(0.001)|(0.001)|(0.001)|(0.0004)|(0.0004)|
|Female Literacy|0.34|0.42|0.06|_−_0.14|_−_0.11|_−_0.34|
||(0.40)|(0.41)|(0.66)|(0.81)|(0.43)|(0.46)|
|District FE|✓|✓|✓|✓|✓|✓|
|Backwardness Score Control||✓||✓||✓|
|Observations|913|913|473|473|322|322|

_Note:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. Robust Standard Errors, clustered at the district, in parenthesis. Te Backwarndess Score is an measure of village level development, calculating using demographic and infrastructal variables, including the share of population belonging to the Scheduled Castes and Tribes, as well as indicators for the presence of a primary or secondary school, hospital or medical clinic, and bank.

we compare average male to female floor time (measured by total number of words spoken by men versus women within a village), we see that men almost always occupy significantly more floor time than women (Table 7). Te one exception is female politicians, who still speak less on average than male politicians, though the differences are not statistically significant. Tat female _politicians_ are able to approach parity with men in terms of floor time suggests that providing women with a formal role or position may be important to closing the gender gap in deliberation.

Table 6: Length of Speeches, by Gender

||Mean,Male Speeches|Mean,Female Speeches|t-statistic|pvalue|
|---|---|---|---|---|
|All Speakers|77.3307|55.0601|2.7035|0.0069|
|Citizens Only|34.1925|32.0133|0.6526|0.5142|
|Administrators Only|152.8220|184.6585|-0.9009|0.3690|
|Politicians Only|70.1786|41.0845|2.2511|0.0251|

Table 7: Assembly Floortime, by Gender

||Mean,Male Floortime|Mean,Female Floortime|t-statistic|pvalue|
|---|---|---|---|---|
|All Speakers|1758.5000|659.6200|6.1285|0.0000|
|Citizens Only|399.4600|233.0698|3.1440|0.0023|
|Administrators Only|1204.3778|590.5172|3.4689|0.0009|
|Politicians Only|529.0385|343.1765|1.1740|0.2478|

27
# **1. Introduction**

The traditional informal system of safety nets and support based on family aid and communal living has eroded in many Sub-Saharan African countries where poverty and AIDS have destabilized households, orphaned children and changed demographic patterns (UNICEF, 2006). As a consequence, families living amid widespread poverty in AIDS-affected communities face a social protection vacuum when both informal arrangements and formal interventions fail to provide the safety net that families need to survive (Miller, 2007). Over the last decade, social protection policy frameworks have gained increasing interest and support among governments and donors in Sub-Saharan Africa. In particular, social protection in the form of cash transfers is considered to be a critical component in fighting poverty and responding to families that have been overwhelmed by disease, illnesses, and other shocks (Barrientos and DeJong, 2004).

Impact evaluations of social protection programs represent a fundamental tool for policy formulation at the national level as well as for funding decisions from bilateral and multilateral donors. However, such evaluations require a careful analysis of the interaction of a formal safety net with the informal insurance arrangements pre-existing in a particular context: in fact, in addition to direct effects, government interventions can also present indirect effects which might change the incentives to participate into private arrangements. Such indirect effects can be quite important, inducing crowding out of private insurance schemes, thus breaking down the fragile social fabric that maintains some form of social insurance among related individuals<sup>2</sup> (Albarran and Attanasio, 2003).

In this paper we focus on the specific context of Malawi, a country where the structure of the household and consequently the informal relationships are deeply affected by the HIV/AIDS pandemic. Using a unique and original dataset based on a randomized design, we evaluate the effects of a public unconditional cash transfers program on consumption expenditures and whether this intervention actually weakens or reinforces an already fragile social fabric. While there have been some studies of crowding out effects of social cash transfers programs in Latin America, very few studies have been done in Sub-Saharan Africa and even fewer are based on a randomized experiment. Our results indicate that the impact on consumption expenditures for those households

> 2 However, the side effects of a public intervention program do not need to be negative: while it is possible that some activities are crowded out, others could grow as a consequence of it (Albarran and Attanasio, 2003).

2
CAMS-3 maximum to 306. This may reflect random technical variation, measurement error, or spikes in nearby emissions that our methodology cannot capture.

# **Table 7:  CAMS-3 PM2.5 vs predictions from MODIS and Google Traffic**

Units: ug/m<sup>3</sup> Collection period (Dhaka time): 9 AM - 2 PM

||MO|DIS|Google|Traffic|
|---|---|---|---|---|
||CAMS-3|Prediction|CAMS-<br>3|Prediction|
|N|402|90|50|107|
|min|11|22|13|20|
|p10|24|46|21|25|
|p25|35|64|31|29|
|p50|67|99|59|39|
|p75|121|136|176|136|
|p90|187|166|232|180|
|p95|217|192|289|200|
|p99|293|244|309|219|
|max|343|244|309|237|

Note: The CAMS-3 columns in Table 7 differ from those in Table 6 because the comparison data samples differ.

**Table 8:  CAMS-3 NO2 vs predictions from Sentinel-5P and Google Traffic**

Units: ppb Collection period (Dhaka time): 1 PM - 2 PM

||Senti|nel-5P|Googl|eTraffic|
|---|---|---|---|---|
||CAMS-3|Prediction|CAMS-3|Prediction|
|N|379|406|65|93|
|min|0.01|2|5|6|
|p10|5|5|7|7|
|p25|6|6|9|8|
|p50|9|7|11|11|
|p75|12|9|17|22|
|p90|19|12|26|27|
|p95|25|13|32|29|
|p99|40|17|48|43|
|max|48|19|48|43|

In summary, our results show that all three global technologies (Sentinel-5P, MODIS, Google Traffic), when combined with commonly-available weather data, provide statistically-robust and conservative PM2.5 predictions that identify hazardous conditions with high likelihood. At the same time, some global technologies perform better than others in our analysis. In the higher 15
**Figure 7. Average TWP Processing Times (in calendar weeks): Top 10 fastest and slowest economies**

<!-- Start of picture text --> Singapore<br>Mozambique<br>Mauritius<br>Côte d'Ivoire<br>Dominican Republic<br>Montenegro<br>Thailand<br>Taiwan, China<br>Albania<br>Global average<br>Austria<br>Slovak Republic<br>Algeria<br>South Africa<br>United Kingdom<br>Costa Rica<br>Morocco<br>Honduras<br>Greece<br>Saudi Arabia<br>0 5 10 15 20 25 30<br><!-- End of picture text --> Source: FDI Regulation Database

# **4.2.2. Good practices with regard to processing Temporary Work Permits**

# • **Online TWP processing**

A good practice with regard to the TWP application process is ensuring the process can be completed online. Completion of the TWP online means that (1) the application form(s) can be downloaded online, (2) documents can be submitted online, (3) confirmation documentation is received online and (4) the progress of the application can be monitored online. In addition, when the work permit becomes available, (5) notification is received online. Online completion entails that no paperwork<sup>8</sup> needs to be submitted to the relevant authority.

> 8 In certain economies documents can be submitted online but those same documents are also required to be submitted separately in person. For example, in Turkey even though the documents can be submitted online, hard copies signed by the applicant must also be submitted to the Labor Ministry. Some economies are taking active steps to upgrade to a fully online application system. For example, even though in the Netherlands currently requires sending in a hard copy of the application documents (which can be downloaded) to the work placement branch of the Employee Insurance Agency (UWV WERKbedrijf) by ordinary mail. The Dutch Modern Migration Policy and the National Visa Act both entered into force on June, 1, 2013, and it is expected that once the new information system (INDIGO) is fully operational applications will be able to be to be completed online (no hard copies are required).

13