rafmacalaba/datause-encoder-data huggingface dataset:
The following figures show the average between the _bid and ask prices_ for the sol/USD observed at 11.00 am (Figure 1) and the percentage change in the exchange rate at different times of the day represented by the difference in the logs of the exchange rate (Figures 2 and 3). Figure 2 shows the differences between the logs of the average bid and ask prices observed at _11.00 am at day t_ and the logs of the average bid and ask prices observed at _1:00 pm at day t-1. ∆Xt11-13_ is the only period in which it is known that the BCRP can be active in the forex. Figure 3, however, shows the difference between the logs of the average bid and ask prices observed at _1:00 pm on day t_ and the logs of the average bid and ask prices observed at _11.00 pm on day t_ , _∆Xt13-11_ , the period in which the central bank is absent in the forex. The development of the (100 times) log difference of the bid and ask at 11.00 am is displayed in Figure 4. Each of the figures also includes the patterns of the net interventions by the BCRP in millions of USD. Net positive interventions indicate net buying of foreign reserves by the BCRP, and negative interventions indicate net selling of foreign reserves.

**Figure 1. Sol/USD observed at 11.00 am Figure 2. Changes in logs of Sol/USD and Net Interventions by BCRP and Net Interventions by BCRP:** **_∆Xt11-13_**

<!-- Start of picture text --> 11:00 am Interbank Rates. 2004 - 2009 Interbank Rates. 2004 - 2009<br>3.6 500 9 500<br>8<br>3.4 250 7 250<br>6<br>3.2<br>5<br>0 0<br>4<br>3.0<br>3<br>2.8 -250 2 -250<br>1<br>2.6 -500 0 -500<br>3200 3400 3600 3800 4000 4200 4400 3200 3400 3600 3800 4000 4200 4400<br>SOLUSD1100 Net InterventUSD StandDevSol/USD BCRP NetInterven<br>mill USD % Change mill USD<br><!-- End of picture text --> 11
# **Access to Water, Women’s Work and Child Outcomes**

Gayatri Koolwal and Dominique van de Walle<sup>*</sup> World Bank

> * Financial support from the World Bank’s Gender Action Plan is gratefully acknowledged.  We thank Martin Ravallion for his advice and insights.  Helpful comments were also received from Laura Chioda, Thomas LeGrand, Ren Mu, Josefina Posadas and participants of the PAA 2010 and the World Bank’s Economists’ Forum 2010. The findings, interpretations, and conclusions expressed in this paper do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Corresponding author:  van de Walle: World Bank, 1818 H ST, NW, Washington, DC 20433. (dvandewalle@worldbank.org).
By examining the employment structure in the Turkish economy as of 2003, we see that 34 percent of total employment is still in agriculture, generating only 12 percent of total income, as implied in the previous table.

**<u>Table 3.2. Employed persons by branch of economic activity, 2003</u>**

||(000)|Share (%)|
|---|---|---|
|Total|21 148|100|
|Agriculture, forestry, hunting and fishing|7 165|33.9|
|Mining and quarrying|83|0.4|
|Manufacturing|3 663|17.3|
|Electricity, gas and water|99|0.5|
|Construction|965|4.6|
|Wholesale and retail trade, restaurants and hotels|4 052|19.2|
|Transportation, communication and storage|1 022|4.8|
|Finance, insurance, real estate and business serv.|738|3.5|
|Community, socialand personalservices|3 359|15.9|

|_Source: TurkSTAT (2005)_|
|---|

Among the agricultural activities, field crops and livestock are further disaggregated into production in four main regions and one micro region of the country, as given in Figure 3.1: West, East, Central, Southeastern, and the micro-region, LSCB (Lower Seyhan-Ceyhan Basin). Fishing, forestry and non-agricultural activities remain at the national level. Geographically, The West region includes Istanbul, Marmara, Aegean and the Mediterranean regions (except for Adana and Osmaniye provinces), the region East includes Northeastern Anatolian, Mid-eastern Anatolian and Eastern Black Sea Regions, Central region includes Western Anatolian, MidAnatolian and the Western Black Sea, and the LSCB micro region includes Adana and Osmaniye provinces. Figure 3.1 depicts the geographical distribution of the regions defined in the SAM. Appendix Table A.1 and Table A.2. give a list of regions and the provinces included in the model.

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# **Figure 14. Growth Decomposition**

<!-- Start of picture text --> (a) LatAm South (b) Chile<br>0.50<br>0.60<br>0.40<br>0.50<br>0.30 0.40<br>0.30<br>0.20<br>0.20<br>0.10<br>0.10<br>0.00 0.00<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>‐0.10<br>‐0.10<br>‐0.20<br>‐0.20<br>‐0.30<br>‐0.30 ‐0.40<br>G EP EL DR TOT G EP EL DR TOT<br><!-- End of picture text --> Note: The variables G, EP, EL and DR are calculated according the growth decomposition method explained in Section 2a. All variables are computed taking the logs of the average yearly growth rates over a ten-year moving window. _Source: WDI._
# **6. References**

- Abebe, Girum, Stefano Caria, Marcel Fafchamps, Paulo Falco, Simon Franklin, and Simon Quinn (2016). “Anonymity or Distance? Experimental Evidence on Obstacles to Youth Employment Opportunities.” Stanford University. Processed

- Akram, Agha Ali, Shyamal Chowdhury and A. Mushfiq Mobarak (2017) “Effects of Emigration on Rural Labor Markets”, Mimeo. Yale.

- Ambler, Kate (2016). “Bargaining with Grandma. The Impact of the South African Pension on Household Decision-Making.” _The Journal of Human Resources_ 51(4): 900-932.

- Araujo, M Caridad, Mariano Bosch, and Norbert Schady (2016). “Can Cash Transfers Help Households Escape an Inter-Generational Poverty Trap?” _NBER Working Paper 22670._ National Bureau of Economic Research.

- Ardington, Cally, Anne Case, and Victoria Hosegood (2009). “Labor Supply Responses to Large Social Transfers: Longitudinal Evidence from South Africa.” _American Economic Journal: Applied Economics_ 1(1): 22-48.

- <mark>Banerjee, Abhijit V., Rema Hanna, Gabriel E. Kreindler, Benjamin A. Olken (2017). “Debunking the Stereotype of the Lazy Welfare Recipient: Evidence from Cash Transfer Programs”,</mark> _<mark>The World Bank Research Observer</mark>_ <mark>32(2): 155–184.</mark>

- Barham, Tania, Karen Macours, and John A. Maluccio (2013). “Boys' Cognitive Skill Formation and Physical Growth: Long-Term Experimental Evidence on Critical Ages for Early Childhood Interventions.” _American Economic Review_ 103 (3): 467-471.

- Barrera-Osorio, Felipe, Andreas de Barros, and Deon Filmer (2018). “Long-term Impacts of Alternative Approaches to Increase Schooling: Evidence from an Experimental Scholarship Program in Cambodia.” _Unpublished manuscript_ .

Becker, Gary (1965) “A theory of the allocation of time”, _Economic Journal_ 75(299): 493-517.

- Behrman, Jere. R., Susan W. Parker, and Petra E. Todd (2011). “Do Conditional Cash Transfers for Schooling Generate Lasting Benefits?” _The Journal of Human Resources_ 46(1): 93-122.

- Berge, Lars Ivar Oppedal, Kjetil Bjorvatn, and Bertil Tungodden (2014) “Human and Financial Capital for Microenterprise Development: Evidence from a Field and Lab Experiment", _Management Science_ 61(4): 707-22.

- Bernhardt, Arielle, Erica Field, Rohini Pande, and Natalia Rigol (2017) “Household Matters: Revisiting the Returns to Capital among Female micro-entrepreneurs”, Mimeo. MIT.

- Bertrand, Marianne, Sendhil Mullainathan, and Douglas Miller (2003). “Labor Supply Responses to Large Social Transfers: Longitudinal Evidence from South Africa.” _World Bank Economic Review_ 17(1): 27-50.

- Bianchi, Milo, and Matteo Bobba (2013). “Liquidity, Risk, and Occupational Choices,” _Review of Economic Studies_ 80: 491-511.

21
_Role of NGOs in .Yealth Delivery Africa Health Policy Study_ 1852. Second, from the point of view of NGOs, do i&' see themselves as an integral part of the _naional_ health care system? Do they recognize the potential influence they have In the policy arena, especially within the context of decentralized, district-focussed health care organization? This paper would argue that It is now time for a selfexamination on the part of NGOs to consider what the implications are for staffing, professional recruitment and management structure to enable them to Increase their legitimacy and play a greater, more influential role in policy making to which they have such a valuable contribution to make.

53. Finally, caution needs to be exercised by donors to consider creative solutions so that NGOs do not become merely conduits for funds, but that their real strengths are allowed to prevail. This is particularly the case in Africa where the absorptive capacity of NGOs (in contrast to Latin America and Asia, perhaps) tends to be weak and governments' Implementation or administrative capacity Is greatly constrained. In such a context, then, there is a critical need for strengthening institutions, and this is necessarily a slow process.
codes CIUO 2008, CIUO 88 and Cota70 in the surveys in Uruguay. Therefore, the mapping of the O*NET data in their different versions with the corresponding information to the household surveys from Argentina and Uruguay required the correspondence tables SOC 2010- SOC 2000,6 SOC-2010-CIUO 08,7 CIUO 08- CNO 01,8 CNO 01 – CNO 91,9 CIUO 08- CIUO 8810 and CIUO 88- Cota 70.11 In many cases the correspondence tables do not determine a one-to-one correspondence between the occupation categories of O*NET and the household surveys. In these cases, the strategy used by Hardy et al. (2015) was followed. Four situations can be identified.

In the first case, there are situations where an occupational code of a specific classification corresponds to only one occupational code of the classification we want to map to. In this case the characteristics are attributed directly to the first code of the second classification.

In the second case, a specific code of a classification corresponds to more than one code of the classification we wish to map to. In this case, the characteristics of the same original code are attributed to all the occupations of the second classification.

In third case, various occupations from the original code correspond to the same code in the classification we are mapping to. In this case, the average value of the characteristics associated to the codes of the original classification are attributed to this last code.

The final case is where various codes in the original classification correspond to various codes in the mapped classification. In this situation, again an average value of the characteristics associated to the corresponding codes of the original classification is attributed to each code in the mapped classification.

Once the mapping has been done, following Acemoglu and Autor (2011) and Hardy et al. (2015) five measures of content or intensity of main tasks are constructed: non-routine cognitive analytical and interpersonal, routine cognitive and manual and non-routine manual. These are formed by the attributes of the activities which they involve. In this sense, some attributes were selected (elements) which are representative of each task. These are represented in Table 1.

> 6 To make the different O*NET bases compatible.

> 7 To perform the mapping with the Uruguayan household survey 2015.

> 8 To perform the mapping with the Argentinian household survey 2015.

> 9 To perform the mapping with the Argentinian household surveys 2003 and 1998.

> 10 To perform the mapping with the Uruguayan household survey 2003.

> 11 To perform the mapping with the Uruguayan household survey 1995.

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We begin with a short literature review, describing the current state of the literature on defaults, expropriation and the relationship between them. In Sections 3 and 4 we describe the expropriation and default dataset. In Section 5 we explore in more detail a number of correlates of expropriations and defaults, both in the long and short run. In Section 6 we describe in more detail the evolution of FDI and debt around expropriation and default events to investigate in more detail the joint retaliation hypothesis. Section 7 concludes.

# **2.  Literature Review**

There is a large theoretical and empirical literature on sovereign default crises. On the theoretical front, questions that have been studied include: Why do sovereign debt markets exist when there is no straightforward legal mechanism to enforce repayment? (pioneered by Gersovitz (1981)); What are the punishments in case of default? (Bulow and Rogoff (1989), Fernandez and Rosenthal (1990) and Rose (2005)); What are the enforcement mechanisms for repayment? (Kletzer and Wright 2000, Cole and Kehoe 1998); and, What is the role of secondary markets in limiting sovereign default risk? (Broner et al. 2010), to mention a few. For the purpose of this paper, we focus more on the empirical literature that studies the determinants of sovereign defaults.

Default usually occurs when the country suffers a series of bad shocks, as is shown in LevyYeyati and Panizza (2006) and Tomz and Wright (2007). However, the empirical relationship between bad output realizations and defaults is not as tight as would be predicted by standard calibrated theoretical models of default. Other factors, such as behavior of creditors and international capital markets (as in Reinhart and Rogoff (2008)), might also influence countries’ default decision. This is because when taking the sovereign debt as given, conditioning on a given level of debt, tighter international financial conditions will make borrowing in bad times more expensive, and defaulting becomes a more attractive option. Another perspective on debt crises centers around the idea that some countries over-borrow, that is, they accumulate debt that is too high from a welfare perspective. Given the high level of debt, small shocks could then trigger a default. One possible reason for excessive debt accumulation might be that parties that contract the debt do not bear the full costs of repayment or crises (Perotti (1996), 4
quite low compared to in-person interviews and attempts to correct for nonresponse bias may not be perfect. Third, the scope and type of questions that can be asked is often limited compared to in-person surveys in order to keep the survey short and to minimize survey refusal.

Two main sampling strategies were used for the World Bank COVID-19 phone surveys, with important limitations intrinsic to the mode of data collection and leading to implications for the populations they were intended to represent. Surveys in 36 countries, especially those in LAC, used pure Random Digit Dialing (RDD) or assisted RDD. This method samples from all active landline and mobile phone numbers, such that RDD surveys would be representative of the population (over 18 years) with an active phone number if survey completion and response were perfect. On the other hand, surveys in 31 countries used a subsample drawn from a prepandemic nationally representative survey. The latter often collected the contact details of the household head, meaning that phone surveys using this sampling frame tend to overrepresent household heads and underrepresent members who are neither heads nor spouses. This means that individual level indicators such as employment outcomes may be biased due to respondent selection within households. The remainder of surveys use other pre-existing lists to identify the sampling frame.

To help correct for the non-representativeness of surveyed households, household sampling weights were calculated. These adjust for differential response rates among subgroups of the population, with the objective of obtaining estimates as close to nationally representative as possible.<sup>7</sup> Addressing the non-representative selection of individuals poses a greater challenge to generating statistics that are representative of individual level outcomes such as engagement in the labor market. Kugler, Viollaz, et al. (2021) examined this for five countries where surveys collected employment information for all household members and find that phone surveys overstate employment rates for the full population even once sampling weights are used. Encouragingly, their analysis suggests that phone surveys do reasonably well at tracking disparities and changes across gender, education, and urban/rural groups. However, for age group comparisons the bias was greater.<sup>8</sup> We use household sampling weights for all our analysis. Household sampling weights are used even for respondent level employment indicators, so these should be interpreted as the share of households with a respondent having that employment outcome, rather than the share of individuals.<sup>9</sup> Countries are weighted equally such that summary statistics are

> 7Surveys in Democratic Republic of Congo, Central African Republic and Mozambique were conducted only in urban areas or the capital city and are not representative of the rural population.

> 8This paper does not make comparisons across age groups.

> 9Individual level sampling weights are only available in some countries with RDD surveys, and it would be

8
**TABLE 11: Investment financial performance and macroeconomics over the life of the investment.** The dependent variable is the PME vs. S&P 500. Explanatory variables are the log difference in the variable from the year of first cash flow to the date of the last cash flow, divided by the investment duration, times 100, so coefficients may be interpreted as the association of the PME with an annualized 1 percentage point change in the variable over the life of the investment. Standard errors are clustered at the country-year. *** p<0.01, ** p<0.05, * p<0.1

||(1)|(2)|(3)<br>Qu|(4)<br>antile Regre|(5)<br>ssion|(6)|
|---|---|---|---|---|---|---|
||OLS|0.1|0.25|0.5|0.75|0.9|
||PME|PME|PME|PME|PME|PME|
|Panel A) Direct investments, vintage pr|e-2010||||||
|Real GDP|0.049|0.004|0.025***|0.045***|0.045|0.072|
||(0.144)|(0.007)|(0.007)|(0.011)|(0.034)|(0.076)|
|Local currency GDP defator|0.213|0.003|0.025***|0.038***|0.038***|0.078***|
||(0.139)|(0.003)|(0.006)|(0.006)|(0.010)|(0.016)|
|Local currency units per U.S. dollar|-0.226|-0.003|-0.027***|-0.043***|-0.049***|-0.093***|
||(0.138)|(0.003)|(0.006)|(0.006)|(0.011)|(0.019)|
|Constant|2.623***|0.036*|0.322***|0.807***|1.547***|3.151***|
||(0.562)|(0.021)|(0.026)|(0.033)|(0.073)|(0.225)|
|R-squared|0.005|0.004|0.005|0.005|0.004|0.005|
|Number of observations|1,186|1,186|1,186|1,186|1,186|1,186|
|Panel B) Fund investments, vintage pre|-2010||||||
|Real GDP|0.591|0.031|0.018|-0.008|0.009|0.116|
||(0.537)|(0.025)|(0.028)|(0.031)|(0.051)|(0.077)|
|Local currency GDP defator|0.068|0.013|0.048|0.068***|0.097**|0.199**|
||(0.119)|(0.020)|(0.031)|(0.023)|(0.047)|(0.099)|
|Local currency units per U.S. dollar|-0.059|-0.010|-0.049|-0.074***|-0.099**|-0.214**|
||(0.130)|(0.020)|(0.034)|(0.025)|(0.049)|(0.106)|
|Constant|1.368***|0.179**|0.468***|0.758***|1.313***|2.813***|
||(0.268)|(0.078)|(0.091)|(0.077)|(0.183)|(0.570)|
|R-squared|0.068|0.052|0.033|0.015|0.022|0.038|
|Number of observations|150|150|150|150|150|150|
|Country fxed efects|No|No|No|No|No|No|

42
consumption. Instead of using a per capita monthly consumption measure, we use adult equivalence expenditure and employ an equivalence scale which gives a low weight to young children. This minimizes the impact of the drop in the number of young children in the household between the census and the time of the 2002 HBS. It is important to note that there are a number of alternative equivalence scales in use in Azerbaijan and that the choice here does not reflect any preferred measure but is simply designed to ameliorate the influence effect of recent fertility decline.

Two indicators of community welfare are computed: i) mean adult equivalent monthly consumption of the region – giving an indication of the average level of living standards the population of the region enjoys; and ii) the proportion of individuals living in poverty, where poverty is defined as living in a household with consumption in the bottom two quintiles of the population (i.e. with adult equivalent incomes below the 40<sup>th</sup> percentile or 215,235.2 Manat per month). This gives a measure of relative poverty.

The ‘imputed welfare’ poverty mapping procedure produces estimates of welfare at the rayon, administrative level and enumeration area. However, the estimates are only considered to be reliable for areas containing more than 1,000 households. Estimates at both the rayon level and administrative level could be potentially useful for geographical targeting; however there are some issues regarding the reliability of those estimates for purpose of geographical targeting in the special case of Azerbaijan. These issues are discussed in Section 2.2. Section 2.3 then shows the results at rayon level, whilst results at the administrative level are presented in Section 2.4.

One further issue which need to be adressed is whether the imputed welfare map refers to the time of collection of the survey data (2002) or the time of collection of the census (1999). A key assumption of the imputation procedure is that the two datasets refer to the same population and implicitly the same point in time, which is clearly not the case here. In determining which year the poverty map refers to, there are two aspects of the inputation procedure which deserve 6
Generally speaking, however, unemployment insurance schemes are more efficient and secure than severance pay arrangements in pooling risks related to job loss. This is because their risk pools extend beyond the boundaries of a single enterprise. With regard to the effects of passive labor market programs on unemployment and other labor market outcomes, these are in principle ambiguous. By their intended purpose, passive programs can remove the urgency of finding new work and improve the quality of employer-employee matches. However, if unemployment benefits are overly generous or poorly designed, they can perversely lower peoples’ motivation to look for and accept employment.

**Table 2 – Unemployment Insurance in East Asia Pacific**

|**Country**|**Average unemployment**<br>**Benefit**|**Unemployment insurance**<br>**benefit duration**<br>**(months)**|**Qualifying conditions**|
|---|---|---|---|
|**Thailand**|50 percent of the average<br>daily wage in the highest<br>paid 3 months during the 9<br>months before<br>unemployment; the<br>maximum daily benefit is B<br>250|6 months in any 1 year|6-month contribution<br>within 15 months before<br>unemployment|
|**China**|Flat benefit level set by the<br>local authorities; higher<br>than the local public<br>assistance benefit, but<br>lower than the local<br>minimum wage|12–24 months, depending<br>on applicant’s years of<br>contribution|Maximum of 1 year with<br>less than 5 years of<br>contributions; maximum of<br>1.5 years with 5 or more<br>but less than 10 years of<br>contributions; maximum of<br>2 years with 10 or more<br>years of contributions|
|**Vietnam**|60 percent of the average<br>monthly earnings in the 6<br>months before<br>unemployment|3–12 months, varying by<br>contribution|3 months with 12–35<br>months of contributions; 6<br>months with 36–71 months<br>of contributions; 9 months<br>with 72–143 months of<br>contributions; 12 months<br>with 144 months of<br>contributions or more|

**Source: World Bank (2014a).**

Together with Thailand and China, Vietnam is one of only three developing countries in the EAP region with a national unemployment insurance scheme. Vietnam’s unemployment insurance scheme was introduced in 2009. Its main characteristics as well as those of the Thai and Chinese schemes are summarized in Table 2. As is evident from the table, unemployment insurance benefits in Vietnam are relatively generous. At 60 percent of the average monthly earnings in the six months before unemployment, the benefit level or replacement rate (the portion of previous income replaced by insurance benefits) is higher than those of Thailand or China. As is common in many countries, in

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