rafmacalaba/datause-encoder-data huggingface dataset:
# **Baseline, Intervention, and Post-Intervention**

The baseline questionnaire was administered to all women in the sample to capture their prior knowledge, beliefs, and attitudes about tetanus and tetanus vaccination, as well as their own and their household’s baseline characteristics. Global Positioning System (GPS) coordinates of respondents’ dwellings were measured. At the end of the baseline survey, the heart rate was measured using a heart rate monitor to capture the baseline emotional state (or autonomic arousal) of each respondent.

Immediately after completing the baseline questionnaire, the flipchart intervention took place. After the session, respondents were provided a voucher that they could redeem at the assigned health clinic. The assignment of health clinic for each respondent was determined based on the village where she resided.<sup>36</sup> After the intervention, respondents were asked questions related to tetanus and tetanus vaccination identical to those asked in the baseline survey to see whether the flipchart intervention triggered any changes in knowledge and beliefs. Respondents were also asked if the intervention caused an emotional arousal. The heart rate was measured immediately after the intervention once again in the same way as at the end of the baseline survey to measure the emotional response to the flipcharts.

# **Health Clinic**

An interviewer (who was a different interviewer than the ones who had conducted the baseline surveys) administered a brief survey with each attendee in each clinic.<sup>37</sup> At the beginning of the interview, the attendee was given the vaccination upon her agreement by the health staff right then. Under Vaccine condition, no respondents refused the vaccination. Respondents under Clinic condition were also provided the vaccine if she would like to receive it when asked. Then, the interviewer recorded her vaccination decision in the survey form. Our measure of vaccine take-up is based on this clinic survey.<sup>38</sup> At the end of the interview, monetary compensation 12
Table A5: Robustness check using the subsample with tenure being 3 years and less— Entry salary

|||Log (entr|y salary)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.355<sup>_∗∗∗_</sup>|-0.103<sup>_∗∗∗_</sup>|-0.083<sup>_∗_</sup>|-0.079<sup>_∗∗_</sup>|
||(0.046)|(0.038)|(0.045)|(0.036)|
|Schooing in years||0.065<sup>_∗∗∗_</sup>|0.076<sup>_∗∗∗_</sup>|0.053<sup>_∗∗∗_</sup>|
|||(0.006)|(0.008)|(0.009)|
|Age at hire||0.029<sup>_∗∗∗_</sup>|0.025<sup>_∗∗∗_</sup>|0.015<sup>_∗∗∗_</sup>|
|||(0.004)|(0.004)|(0.004)|
|Father’s pri.school||0.034|0.022|0.027|
|||(0.042)|(0.039)|(0.036)|
|Mother’s pri.school||0.029|0.092<sup>_∗∗_</sup>|0.011|
|||(0.037)|(0.041)|(0.042)|
|Math z-score||0.068<sup>_∗_</sup>|0.071<sup>_∗∗_</sup>|0.049<sup>_∗_</sup>|
|||(0.035)|(0.036)|(0.028)|
|Language z-score||-0.105<sup>_∗∗_</sup>|-0.090<sup>_∗_</sup>|-0.036|
|||(0.042)|(0.046)|(0.042)|
|Big 5 index z-score||0.028|0.006|-0.020|
|||(0.040)|(0.039)|(0.036)|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.174|0.489|0.521|0.289|
|N|1745|1744|1744|1744|

_Note_ . The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

42
In order to correct for potential nonspherical errors, a Generalized Least Square (GLS) approach would be appropriate. However, the GLS estimation requires knowledge of the 2 unconditional variance matrix of υ _ijt_ , Ω , up to scale. Hence, we must be able to write Ω = σ _C_ , where _C_ is a known GxG positive definite matrix. But, in this case, as this matrix is not known, we will follow a Feasible GLS (FGLS) approach that replaces the unknown matrix Ω with a consistent estimator.

# **IV. Data**

This research required the construction of an unbalanced panel data set of key indicators for utilities in LAC in electricity distribution. For this, we used official data reported by firms to their investors and statistical reports of the regulator agencies of each country. Furthermore, information was requested from each firm as well as from each regulatory office. Additional sources used include OLADE (Latin American Organization of Energy) publications. A particular effort was made to corroborate the data provided by the firms with several public sources and with data on the firms provided by different governmental offices. In addition, careful attention was paid to ensure the consistency and comparability of the data over time and across countries.<sup>10</sup> The analysis focuses on several indicators of outcomes, employment, labor productivity, efficiency, quality, coverage and prices. Table 1 presents the definitions of the variables used in the present analysis and Table 2 presents the summary statistics of the variables used.

Some of these variables have been used by other authors in other samples, such as Ros (1999), who used equivalent indicators for coverage, labor productivity, quality and prices but for the telecommunications sector. Ramamurti (1996) used analogous indicators in _output_ , _coverage_ and labor productivity for the four Latin American telecommunications firms of his study. Saal and Parker (2001) used similar indicators for output, employment, quality and prices _,_ but for water and sewerage companies of England and Wales.

The countries analyzed were Argentina, Bolivia, Brazil, Chile, Colombia, El Salvador, Guatemala, Nicaragua, Panama, and Peru. The sample consists of unbalanced panel data that includes 116 firms and 1,103 firm-year observations. Each of the firms included in the sample contains at least one year of pre privatization data. In fact, 98 of the 116 firms have information for at least the previous three years.

> 10 As quality indexes vary across countries, we collected the closest indexes in order to be able to compare their evolution across time, rather than the absolute level.

_10 of 28_
uncomfortable with the massive global transfer and re-nationalization of finance that has taken place in the form of accumulating wealth with sovereign wealth funds. And of course, the gravest and most existential threat of all, that to environmental security, confronts the world in the form of climate change.

Each of these issues has significant global or trans-border dimensions which requires a cooperative response by countries. In the rest of the paper, we will elaborate on these threats to security, highlighting the case for cooperation and assessing whether the WTO is the appropriate forum. Our claim is not that stresses on supply and other threats to security are durable. It is rather that current multilateral rules are less attuned to dealing with these threats. These rules therefore need to be relevant not just to the “good” states of the world where supply is plentiful and the traditional protectionist concerns paramount; but also to the “bad” states of scarcity where food and energy security are important.

# **_III.1  Agriculture and food security_**

The severity of the global food crisis is undeniable. As chart 5 shows, prices of major commodities have increased substantially over the last three years, and especially, in the last few months. According to the World Bank, about 100 million people might be thrown back into the ranks of the poor because of these price rises. There have been riots in a number of countries, and the Bank has identified 33 as especially vulnerable. The poor are especially vulnerable because they spend the largest portions of their income on food. For example, in Nigeria, about 70 percent of income is spent on food, 75 percent in Vietnam, and 50 percent in Indonesia compared with 12 percent in the United States (though that figure is also now on the rise).

Pressure on food supplies, and associated high food prices, could be a medium- to longterm reality because some of the driving factors— rising prosperity in the developing world which creates more demand, high fuel prices, stagnant agricultural productivity, and climate-change induced pressure on agricultural supplies —could also be of a durable nature.

<mark>These fundamentals are being exacerbated by two types of trade policy interventions: export restrictions on foodstuffs, and trade-related biofuels policies in the industrial countries. In the current crisis, 18 developing countries have imposed some form of export restrictions in the current crisis (see the World Bank’s study available at</mark> <u><mark>http://siteresources.worldbank.org/NEWS/Resources/risingfoodprices_chart_apr08.pdf</mark></u> <mark>). Each country is trying to keep domestic supplies high on the grounds of food security. But, as more countries implement export controls, global supply contracts, pushing prices up further, aggravating global food security. In ongoing research with Maros Ivanic and</mark> 7
All these trends have important implications for Vietnam. Although enrolment<sup>inbasic</sup> education (defined as primary and lower secondary education)<sup>has been improvedforpoor</sup> children, inequalities persist at primary level, and the gaps are widening<sup>athigherlevels.This</sup> suggests that poor children face disadvantaged conditions more often<sup>than dobetter-off children,</sup> such as poorer quality of education, lack of sufficient time spent<sup>studyingdue to houseworkor</sup> income earning activities, and poor health.

**Table 3: Age/grade matching, 1993 and 1998**

||A<br>|gegroup:<br>|percentage<br>|oftotal <br>|enrolledin<br>|level:<br>||
|---|---|---|---|---|---|---|---|
|Level|<6|6-10|11-14|15-17|18-24|>24|Total|
|_1993_||||||||
|Primary|4.8|72|22.1|1|0.1||100|
|Lowersecondary||1.0|71.3|25.9|1.8||100|
|Uppersecondary|||1.4|83.7|13.5|1.4|100|
|Postsecondary||||17.1|67.5|15.4|100|
|_1998_||||||||
|Primary|1.2|79.7|17.9|1|0.2||100|
|Lowersecondary||1.2|79.2|18.7|0.8||100|
|Uppersecondary|||0.7|79.1|19.9|0.3|100|
|Postsecondary|||0.2|4.2|79.7|16|100|

Source: Author's estimates based on 1992-93 and 1997-98 VLSS.

# _Gender Differences_

Vietnam has almost achieved gender equality of enrolment in<sup>generaleducation</sup> (including primary and secondary) between boys and girls. Testing<sup>hypothesisthat girlsand boys</sup> had the same net enrolment rates showed that this equality is statistically<sup>significant.In1998,</sup> 91% of school-age girls were enrolled in primary school compared<sup>with92%of boys. Gender</sup> inequality is not found at lower secondary level (NERs in 1998 were<sup>33.5%for boys and 33.7%</sup> for girls), although a gap between girls' and boys' net enrolnent in<sup>uppersecondarylevelis</sup> larger and increased slightly from 2 percentage points in 1992-93 to 3<sup>percentagepoints in1997-</sup> 98 (Figure 3).

<!-- Start of picture text --> Figure 3: School NER by Gender and Education Level, 1993 and 1998<br>90 -------------------------------- -<br>80 -- - - - - - - - - - - - - - - - - - - - -- -- -- -- - -- G irls 1---------<br>760 . -<br>50 .-<br>40.--<br>30 - -l<br>20 --.I<br>10 - . -- DR ----- - -;<br>0<br>rirrary Low er Upper Pirrory Low er Upper<br>Secondary Secondary Secondary Secondary<br><!-- End of picture text --> _5_
# **3 Data and Context**

To carry out the analysis we make use of two main novel sources of data. The first is monthly data on the universe of Indonesian exporters and importers which was confidentially shared by the Indonesian Directorate General of Customs and Excise (DGCE, referred to as DG Customs in the paper) within the Ministry of Finance.<sup>8</sup> The second is a time-varying data of NTMs at a highly disaggregated level of sectoral classification (HS-10 digit) which was assembled and maintained for Indonesia by the World Bank. This data was compiled on the basis of extensive regulatory checks and varies at a monthly level at 3-digit MAST classification and 10 digit HS level. This section also provides information on other data used in the analysis and the choice to focus the analysis on Indonesian firms’ exports to Japan.

## **3.1 Indonesian customs data**

For the trade data, we use customs-level data covering the universe of Indonesian exporters from 2014 to 2018 in a monthly series. The data are collected by the Indonesian customs and record values exported and imported by each firm at the 10-digit level of the Harmonized System (HS). The data also has information on the country of destination, the value of exports, the quantities and status of the exporting firm (for example whether it is a producer and/or a general importing firm).

In Indonesia, an importer must have Business Registration Number (NIB). The NIB represents the Certificate of Company Registration (TDP), Customs Registration (NIK) and Importer Identification Number (API). When registering NIB, an importer must choose which API will be chosen between the two types of API that exist: API-U (General API), which is granted to importers that import goods for trading or transfer to other parties; and API-P (Producer API), which is for importers that import goods for their own use as raw materials, supporting materials and/or for supporting production process. Our data

> 8This was the result of an ongoing collaboration with the World Bank office in Jakarta.

9
Figure 4: Growth incidence among rural households in coffee regions, 1992-1995 <!-- Start of picture text --> Growth incidence curve Growth rate in mean<br> Mean of growth rates<br>20<br>15<br>10<br>5<br>0<br>-5<br>0 10 20 30 40 50 60 70 80 90 100<br>Percentiles<br><!-- End of picture text --> Source: Authors’ calculations.

Figure 5: Growth incidence among coffee farmers, 1995-1999 <!-- Start of picture text --> Growth incidence curve Growth rate in mean<br> Mean of growth rates<br>20<br>15<br>10<br>5<br>0<br>-5<br>0 10 20 30 40 50 60 70 80 90 100<br>Percentiles<br><!-- End of picture text --> Source: Authors’ calculations.

16
# **Literature**

Annabi, Nabil, John Cockburn, and Bernard Decaluwé (2006) _Functional Forms and Parametrization of CGE Models_ , MPIA Working Paper 2006-04, Poverty and Economic Policy (PEP) Network. Available at: http://www.pep-net.org/ Atamanov, Aziz, Matthias Luecke, Toman Omar Mahmoud, Roman Mogilevsky, Kseniya Tereshchenko, Natalia A. Tourdyeva, Ainura Uzagalieva, and Vitaliy Vavryshchuk (2009) _Income and Distribution Effects of Migration and Remittances: An Analysis Based on CGE Models for Selected CIS Countries_ , Center for Social and Economic Research, CASE Network Reports No. 86/2009.

Centre for Social and Economic Research, CASE (2008 _) The effects of migration and remittances in rural Moldova and a Poland’s case study of migration management_ , Polish Aid.

Expert Grup (2011 _) Real Economy, Monthly Economic and Policy Review_ , Issue No. 20, June.

Government of Moldova (2010) Rethink Moldova: Priorities for Medium Term Development. Report for the Consultative Group Meeting in Brussels. March 24. Available at: <u>www.onu.mfa.md/img/docs/rethink-moldova-2010-2013-en.pdf</u> Government of Moldova (2012a) Moldova 2020 – National Development Strategy: 7 Solutions for Economic Growth and Poverty Reduction. Available at: <u>http://www.gov.md/public/files/Moldova_2020_ENG.pdf</u> Government of Moldova and United Nations Moldova (2012b) Towards Unity In Action: United Nations – Republic Of Moldova Partnership Framework 2013 – 2017. Available at: <u>www.un.md/news_room/pr/2012/undaf/United_Nations_Republic_of_Moldova_Partnership_Frame work.pdf</u> Huang, Wen-yuan (2009 _) Factors Contributing to the Recent Increase in U.S. Fertilizer Prices, 2002-08,_ Outlook AR-33, United States Department of Agriculture.

International Monetary Fund (2005 _) Republic of Moldova._ IMF Country Report No. 05/54, February. Washington D.C.

International Monetary Fund (2008 _) Republic of Moldova._ IMF Country Report No. 08/39, April. Washington D.C.

International Monetary Fund (2010 _) Republic of Moldova_ . IMF Country Report No. 10/232, July. Washington D.C.

International Monetary Fund (2012 _) Republic of Moldova._ IMF Country Report No. 12/38, February. Washington D.C.

Lofgren, Hans (2011) MAMS – A guide for users, draft. January 7, 2011.

Lofgren, Hans, and Carolina Diaz-Bonilla (2010) _MAMS: An Economy-wide Model for Development Strategy Analysis_ , Development Economics Prospects Group, World Bank. Draft June 10. Available at: 37
different databases, including the one presented here, to benchmark countries’ financial systems over time.

This database contains a select number of financial system indicators that are readily available for a large number of countries over extended periods of time. This necessarily implies exclusion of certain indicators that are available only for a small number of countries (such as detailed stock market liquidity or primary bond indicators) or are available only for one or few points in time, such as most access indicators. Compared to the original version of database – as described in Beck, Demirguc-Kunt and Levine (2000) -, we left out several indicators that were rarely used in the literature and can be easily constructed as ratios of other variables and several indicators where it is difficult to access raw data across a large number of countries and a longer time period. Finally, we would like to note that our data are on the yearly frequency and can thus do not capture short-term trends.

The remainder of the paper is organized as follows. Section II presents and discusses indicators of the size of financial systems. Section III introduces indicators of the structure, efficiency and stability of commercial banks. In section IV we define indicators of the size and activity of capital markets and insurance sectors, while section V presents several indicators of financial globalization. Section VI discusses recent trends in financial structure, i.e. the degree to which financial systems are market- or bank-based, while section VII offers concluding remarks. Table 1 provides an overview over the different variables includes as well as the respective periods of coverage.

# **II. The Size of the Financial System**

This section presents several basic indicators of the size of the financial system. Here, we focus on banks and bank-like financial institutions, equity markets and private bond markets. The indicators on financial intermediary development are based on raw data from the International Financial Statistics (IFS) from the IMF, the equity market indicators on raw data from the Emerging Market Database and the bond market indicators on raw data from the BIS.

> 3 To access these two databases, please see:  www.doingbusiness.org and www.enterprisesurveys.org.

3
large scale commercial production considerably earlier. After arrival of second generation biofuels, their production increases by 300 million toe, and the biofuels share in liquid fuel production increases to reach 47% in 2100. Agricultural area dedicated to cellulosic feedstocks increases by additional 30 million Ha in 2100, whereas areas dedicated to food crops, animal feed and natural forests decline. GHG ows increase with the additional conversion of natural lands, and later decline with biofuels' osets. The latter eect dominates in the long run, and net GHG accumulation declines by 40 GtCO 2 e in 2100. The consumption of energy services increases, and the consumption of other land based goods and services declines.

<!-- Start of picture text --> Change in Land Use Relative to Baseline Change in GHG Emissions Relative to Baseline<br>50<br>1.5 20 Natural Land<br>40 Food Crops 15 Conversion<br>30 Livestock Feed 1 Livestock<br>20 10<br>10 Biofuels Crops 0.5 5 Biofuels<br>Pasture Land Offsets<br>0 Protected Forests 0 0 Use of<br>-10 -5 Fertilizers<br>-20 Managed Forests -0.5 Forest<br>-30 Unmanaged Forests -1 -10 Sequestration<br>-40 -15 Net GHG<br>Accumulation<br>-50 -1.5 -20<br>2005 2030 2055 2080 2105 2005 2030 2055 2080 2105<br>Change in Consumption of Land Based Good Change in Consumption of Biofuels Relative to<br>and Services Relative to Baseline Baseline<br>30 250 20%<br>25 200 15%<br>150<br>2015 Processed Livestock 10050 10%5% Consumption of 2gBiofuels<br>Ecosystem Services<br>10 0 0% Consumption of 1g<br>5 Processed TimberEnergy Services -100-50 -5% Biofuels<br>0 Processed Grains -150 -10% Share of Biofuels inLiquid Fuel<br>-5 -200 -15% Consumption<br>-10 -250 -20%<br>2005 2030 2055 2080 2105 2005 2030 2055 2080 2105<br>Million Ha GtCO2e/yr GtCO2e<br>USD Per Person Million toe<br><!-- End of picture text --> Figure E.4: Sensitivity Analysis: 50% Increase in Fixed Factor Specic Technological Change Figure E.4 shows the eects of a 50% increase in xed factor specic technological change. Similar to the improvements in biofuels' production technology, faster technological progress in biofuels penetration make biofuels a more competitive alternative to petroleum products. Biofuels production increases by 150 million toe, and the biofuels share in liquid fuel production increases to reach 32
# **Comparative Analysis of Labor Market Dynamics Using Markov Processes: An Application to Informality**

Mariano Bosch _London School of Economics_ _<u>m.bosch@lse.ac.edu</u>_ William Maloney _World Bank_ _<u>wmaloney@worldbank.org</u>_

**Keywords** : Labor market dynamics, Markov processes, Informality **JEL Codes** : C14,  J21, J24, J64, 017
**Table 5. Transfers and Sexual Activities (Daily Level)**

||(1)<br>Dependen|(2)<br>t Variable: G|ifts from|(3)<br>Regular Clie|(4)<br>nts(Daily)|
|---|---|---|---|---|---|
|Sex Worker Saw at least 1 Client|-11.57<br>(10.08)|||||
|# of Clients Seen||-3.74<br>(4.68)||||
|# of Regular Clients Seen||||6.95<br>(7.05)||
|# of Casual Clients Seen||||-8.14<br>(4.63)*||
|# of Clients Vaginal Sex<sup>1</sup>|||||-13.86<br>(5.75)**|
|# of Clients Anal Sex|||||1.35<br>(5.83)|
|# of Clients Oral Sex|||||-4.64<br>(5.03)|
|# of Clients Manual Stimulation|||||-2.95<br>(6.32)|
|# of Clients Massage|||||-1.28<br>(5.97)|
|# of Clients Kissing|||||8.04<br>(4.69)*|
|# of Clients Giving Company|||||7.40<br>(4.75)|
|# of Clients Stripping|||||-1.47<br>(5.22)|
|# of Clients Sex in Thighs|||||-1.37<br>(7.16)|
|# of Clients "Other" Activities|||||5.46<br>(18.27)|
|# of Clients Unprotected Sex|||||18.06<br>(7.27)**|
|Activity Controls|No|No||No|Yes|
|<br>Mean of Dependent Variable<sup>2</sup>|||102.21|||
|Observations|12460|12460||12460|12460|
|Number of Women|192|192||192|192|

Notes: These are fixed effects regressions (at the daily level) with controls for the day of the week and the month of the year. Clustered standard errors (at the individual level) in parentheses.

1This variable measures the number of clients with whom the woman had vaginal sex at least once in a given day. It is not the total number of times she had vaginal sex that day, since we did not collect information on the number of sex acts per client for all activities in all diary versions. All sexual behavior variables are coded in this manner. See text for more details. 2Mean is for days on which woman does not see clients.

Exchange rate was roughly 70 Kenyan shillings to US $1 during the sample period.

* significant at 10%; ** significant at 5%; *** significant at 1%