rafmacalaba/datause-encoder-data huggingface dataset:
responsibilities can take up the majority of women’s time in rural Honduras, the interviews suggest that it is probable that many of these women are also including agriculture and market activities in their daily domestic tasks and not reporting them.

Based on the interviews conducted for this study, more detail should be added to the agricultural activity in the follow-up question to explicitly include the gathering and sale of forestry products (like the blackberries picked by women in the communities) and rearing animals to sell (like chickens and eggs). If the survey is attempting to capture production for own consumption, then the question should also explicitly mention animals and vegetables usually grown by women close to the home. In addition, it is likely that the listing of the task does n o t overcome the bias by respondent or interviewer that a job needs to be full time and/or outside the home. To address this issue requires also training interviewers to engage with the respondents and explain that activities can be undertaken in the home or irregularly.

Using the EPHPM 2018 data, we can simulate what rural labor force rates would look like if these activities were accurately measured. Figure 3 presents three simulations. Each of these simulations assign employment for women who fit the following conditions: 1) she reports being too busy with household tasks as the reason for not searching for work, 2) she is not enrolled in school, and 3) there are no children under the age of 1 in the household. Under these conditions and given the results of the interviews, it is likely that these household tasks include market-oriented activities or agriculture for own-consumption. The most conservative is Simulation 1, which allocates employment to women in agricultural households, specifically those who satisfy the above conditions and are the spouse/partner of the household head and he reports running an agricultural enterprise (either as an employer or self-employed). These are households where it is probable that the wife is contributing to the agricultural enterprise. This would increase female labor force participation rates by 6.5 percentage points to 50.1. Simulation 2 assumes employment for women outside of agricultural households – that is, female heads and wives of household heads in rural areas who otherwise fulfill the initial three conditions. This would increase the female participation rate by 10.8 percentage points to 60.9 percent. Finally, the third simulation applies the same three conditions to all adult women living in these rural households. This increases the female labor force participation of women in rural Honduras by 5.5 percentage points to reach 66.4 percent.

Figure 3. Simulations of hypothetical rural labor force participation rates <!-- Start of picture text --> 80<br>66.4<br>60.9<br>60<br>50.1<br>43.6<br>40<br>20<br>0<br>Baseline Sim 1: Reports Sim 2: Reports Sim 3: Reports<br>housework & lives in housework, housework, other<br>an agricultural household head and adult women<br>household, spouse only<br>household head and<br>spouse only<br>LFP (%)<br><!-- End of picture text --> 24
**_In the tournaments, we cannot disentangle framing effects from strategic responses to equilibrium play_** _._ Recall that making caste public eliminated the positive response to tournament incentives compared to piece rates.  This finding is consistent with an interpretation based on framing effects:   when caste is salient, H may feel less need to excel whereas L feel they can’t or don’t dare to excel.  The potential prize for winning a lottery is high in absolute terms:  90 rupees, equivalent to almost two days’ adult wages.  As noted above, in the traditional caste order, attempts by an individual low-caste person to better himself trigger punishment.

However, strategic responses to equilibrium play can also explain the non-response of H to tournament incentives in Revealed Segregated. If we make the plausible assumption that H believe that low-caste boys will choose not to compete because for an L to win against an H would violate the traditional caste order (or that L cannot compete effectively since they are less able than high-caste boys), then compared to the mixed tournaments, the high-caste-segregated tournaments would have low expected returns to effort because the participants know they are competing against five H boys.

Another seemingly plausible explanation of the underperformance of H in Revealed Segregated tournaments —which we can rule out—is that H do not wish to differentiate themselves from others in their community. This explanation is not germane because an individual’s community is his specific endogamous caste, whereas the sessions were composed of individuals from many specific castes at the high end of the caste hierarchy—Thakur (36%), Brahmin (32%) Kshatriya 29%, and others (less than 1%). Thus, a high-caste boy in Revealed Segregated would not find himself among boys of only his own specific caste. Earlier experiments in the state of Uttar Pradesh reveal solidarity among men of the same specific highstatus castes (Hoff, Kshetremade, and Fehr, 2011) and spite between men of different specific castes (Fehr, Hoff, and Kshetremade 2008).

**_Observational studies of school enrollment point to a causal effect of high salience of the caste order on low school enrollment by both high- and low-caste students._** Consider again our conjecture that when the social division between high and low castes is salient, a highcaste boy may think, “I don’t need to excel.”  If this is correct, it would predict that when caste boundaries are eroded, a high-caste boy (or his family on his behalf) may think, “I need to work harder to better myself.”  This prediction is consistent with the findings in Kochar (2004).  She analyzes an Indian government policy to construct schools in low-caste hamlets.  The policy led to an increase in low-caste enrollment.  The increased school enrollment of low-caste children

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TABLE A3: NQ SPECIFICATION WITH CASSETTE TAPES INSTRUMENT

|Any|Parsimonious<br>Any|specifications<br>Any||Basel<br>Any|ine Specificati|on|
|---|---|---|---|---|---|---|
|Dependent Variable (panels A, B, C):<br> <br>Conflict<br>(1)<br>|<br>Conflict<br>(2)|<br>Conflict<br>(3)|Any Conflict<br>(4)|<br>Conflict<br>(5)|Intrastate<br>(6)|Interstate<br>(7)|
|_Panel A: OLS Estimates_|||||||
|U.S. wheat aid (1,000 MT)<br>-0.00006|-0.00007|-0.00005|-0.00007|-0.00011|-0.00005|-0.00011|
|(0.00018)|(0.00018)|(0.00017)|(0.00017)|(0.00017)|(0.00017)|(0.00004)|
|R-squared<br>0.5083|0.50846|0.51751|0.53352|0.54904|0.52336|0.38477|
|_Panel B: Reduced form estimates (x 1,000)**_|||||||
|<br>Cassette Tape Sales (Million USD)<br>0.23451|0.30509|0.28718|0.23429|0.18690|0.11036|-0.01709|
|x avg. prob. of any US food aid<br>(0.08095)|(0.09193)|(0.09405)|(0.11322)|(0.11204)|(0.10821)|(0.03398)|
|R-squared<br>0.51847|0.52033|0.52820|0.53982|0.55435|0.52577|0.38194|
|_Panel C: 2SLS Estimates_<br>0.00575|0.00370|0.00352|0.00294|0.00221|0.00131|-0.00020|
|U.S. wheat aid (1,000 MT)<br>(0.00227)|(0.00172)|(0.00158)|(0.00135)|(0.00124)|(0.00111)|(0.00036)|
|Dependent variable (panel D):|||US wheat aid(1|,000 MT)|||
|_Panel D. First-stage estimates_|||||||
|<br>Cassette Tape Sales (Million USD)<br>0.04078|0.08249|0.08154|0.07977|0.08442|0.08442|0.08442|
|<br>x avg. prob. of any US food aid<br>(0.01242)|(0.03276)|(0.03084)|(0.02474)|(0.02877)|(0.02877)|(0.02877)|
|KP F-Stat<br>10.77|6.34|6.99|10.40|8.61|8.61|8.61|
|Controls (for all panels):|||||||
|Country FE<br>Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region-year FE<br>Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|US real per capita GDP x avg.<br>No<br>Prob. of any US food aid|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>US Democratic president x.<br>No<br>avg. prob. of any US food aid|Yes|Yes|Yes|Yes|Yes|Yes|
|Oil price x avg. prob. of any US<br>No<br>food aid|Yes|Yes|Yes|Yes|Yes|Yes|
|Monthly recipient temperature and<br>No<br>Precipitation|No|Yes|Yes|Yes|Yes|Yes|
|Monthly weather x avg.<br>No<br>prob. Of any US food aid|No|Yes|Yes|Yes|Yes|Yes|
|<br>Avg. US military aid x year FE<br>No|No|No|Yes|Yes|Yes|Yes|
|<br>Avg. US economic aid x year FE<br>No|No|No|Yes|Yes|Yes|Yes|
|Avg. recipient cereal imports x<br>year FE<br>No|No|No|No|Yes|Yes|Yes|
|Avg. recipient cereal<br>No<br>production x year FE|No|No|No|Yes|Yes|Yes|
|<br>Observations(for allpanels):<br>3,896|3,896|3,896|3,896|3,896|3,896|3,896|

Notes: An observation is a country and a year. The sample includes 125 non-OECD countries for the years 1973-1984. The first two years of the NQ dataset are excluded because cassette sales data were only available starting in 1973. Coefficients are reported with standard errors clustered at the country level in parentheses. The controls included are indicated in the table by Y (yes) or N (no). Coefficients are reported with standard errors clustered at the country level in parentheses. **In panel B, the point estimates and standard errors are multiplied by 1,000 for presentation purposes. Panel D reports Kleibergen-Paap F-statistics. Data are from the NQ replication file and International Federation of the Phonographic Industry.

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# Table 32: INTERNAL RATES OF RETURN FOR VTE, BY LOCATION

||Secondary Level|Post Secondary<br>and<br>Teacher<br>Training|Apprenticeships|
|---|---|---|---|
|||AbidJan||
|Social Rate of Return||||
|Base Case<br>Costs+25X<br>Costs-<br>25%|3.19Z<br>2.01%<br>4.83%|8.53%<br>6.98%<br>10.76%|3.00X<br>1.85%<br>4.60X|
|Private Rate of Return||||
|Base Case<br>Costs+25%<br>Costs-<br>25%|13.19%<br>11.15%<br>16.19%|30.04%<br>26.04%<br>36.05%|3.00%<br>1.85Z<br>4.60X|
|||Other Cities||
|Social Rate of Return||||
|Base Case|18.83%|27.03%|_|
|Costs+25%|16.11%|23.30%|_|
|Costs-<br>25%|22.97%|32.69%|_|
|Privat6 Rate of Return||||
|Base Case|47.75%|51.34%|_|
|Costs+25%|41.05%|44.56%|_|
|Costs-<br>25%|57.87%|61.37%|_|
Table 1: GDP implications of various scenarios - cumulative impacts (% deviations from the benchmark)

||**Global**<br>**pandemic**|**Amplified**<br>**global**<br>**pandemic**|
|---|---|---|
|China|-3.69|-4.31|
|DevelopingEAP excludingChina|-2.38|-4.76|
|Cambodia|-3.21|-6.57|
|Lao PDR|-2.15|-4.05|
|Malaysia|-2.09|-4.23|
|Thailand|-3.03|-6.21|
|Vietnam|-2.65|-4.49|
|Philippines|-2.46|-4.80|
|Indonesia|-1.74|-3.51|
|HongKongSAR,China|-2.31|-4.82|
|Korea,Rep.|-2.44|-4.89|
|Singapore|-2.08|-4.45|
|Taiwan,China|-2.81|-5.67|
|Canada|-1.57|-3.18|
|Europe|-1.85|-3.85|
|Japan|-2.23|-4.57|
|United States|-1.67|-3.40|
|Middle East & North Africa|-1.38|-2.95|
|Sub-Saharan Africa|-1.44|-2.95|
|Brazil|-1.71|-3.42|
|Rest of Latin America & Caribbean|-1.85|-3.73|
|Russian Federation|-1.94|-3.99|
|Rest of Europe & Central Asia|-2.21|-4.60|
|India|-2.41|-4.93|
|Rest of South Asia|-2.31|-4.68|
|Oceania|-1.70|-3.37|
|Developingcountries|-2.49|-4.00|
|High-income countries|-1.84|-3.77|
|World Total|-2.09|-3.86|

_Source:_ Envisage simulations

- 9 -
WPS3929

# **AN EMPIRICAL ANALYSIS OF THE ANNUITY RATE IN CHILE**

**Roberto Rocha, Marco Morales, and Craig Thorburn**<sup>**1**</sup>

# **Abstract**

Empirical analyses of annuities markets have been limited to a few developed countries and restricted by data limitations. Chile provides excellent conditions for research on annuities due to the depth of its market and the availability of data. The paper utilizes a panel of life insurance company data to examine econometrically the main determinants of the annuity rate, defined as the internal rate of return on annuities. The results indicate that the annuity rate is determined by the risk-free interest rate, the share of privately-issued higher yield securities in the portfolio of providers, as a proxy for the spread over the risk-free rate, the leverage of providers, the level of broker’s commissions, the market share of individual providers, the level of the premium, and the degree of market competition. The results also show that efforts to improve market transparency produced structural shifts in the parameters of the annuity rate equation. The results are consistent with separate research on money’s worth ratios, and indicate the need to develop appropriate financial instruments, allowing providers to hedge their risks while extracting higher returns, and also to ensure competition and transparency in annuities markets, in order to ensure good outcomes for annuitants.

# World Bank Policy Research Working Paper 3929, June 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1World Bank, Diego Portales University and World Bank, respectively. This paper was derived from a comprehensive report of the Chilean market for retirement products, coordinated by Roberto Rocha and Craig Thorburn, and part of a broader World Bank project on the payout phase of private pension systems involving several country studies. The authors are grateful to Sara Zervos and Ying Lin for valuable inputs provided in the early stages of the analysis. The authors are also grateful to Eduardo Walker, Dimitri Vittas, Augusto de la Torre, Augusto Iglesias, Guillermo Martinez, Solange Berstein, Guillermo Larrain, Alejandro Ferreiro, Ernesto Rios, Osvaldo Macias, Richard Hinz, and several industry participants for helpful comments on earlier versions of the paper.
provincial revenues. The Gini index is reduced from 0.365 to 0.283 after adding the grants to per capita provincial revenues. Surprisingly, the equalizing effect of the earmarked grants is even stronger than the equalization transfer. It may be partly because the per capita equalizing transfer is very small, only about one percent of the per capita earmarked grants. While the program appears to promote provincial equity in a limited sense, the grant design does not score well on most criteria such as transparency, predictability, simplicity and objectivity.

18
**Table S1.** Comparison of SGRY and NREGA

|Description|SGRY|NREGA|
|---|---|---|
|Centrally Sponsored Scheme|Yes|Yes|
|Centre:State Cost Ratio|75:25|90:10|
|Female participation target|33%|33%|
|Districts covered|All|All|
|Primary Objectives:|||
|a) Wage Employment|Yes|Yes|
|b) Infrastructure Creation|Yes|Yes|
|Restrictions on public works:|||
|a) Ban on contractors|Yes|Yes|
|b) Ban on heavy machinery|Yes|Yes|
|c) Wage:Capital Cost Ratio|60:40|60:40|
|Implementing Authority|Village<br>Panchayat|Village<br>Panchayat|

_Source_ : Annual reports (2001 – 2010), Ministry of Rural Development, Government of India. _Note_ : The table compares various program attributes of SGRY and NREGA. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act.

**Table S2.** Results with AWI data: Impact of public works on wages with binary treatment

||(1)|(2)|(3)|
|---|---|---|---|
|_NREGAd,t_|7.197<sup>_∗∗∗_</sup>|-2.302|-3.045|
||[2.319]|[1.946]|[1.989]|
|District effects|No|Yes|Yes|
|Year effects|No|Yes|Yes|
|Trend effects|No|No|Yes|
|Observations|1340|1340|1340|
|Adjusted_R_<sup>2</sup>|0.009|0.853|0.918|

_Source_ : Author’s analysis based on AWI data (for field wages). _Notes_ : All estimates are of the order 10<sup>_−_2</sup> . For a district, _NREGAd,t_ takes the value zero (one) before (after) the implementation of NREGA. All regressions are weighted by district-level population. The standard errors reported in square brackets are clustered at the district level and are robust to heteroskedasticity. NREGA: National Rural Employment Guarantee Act. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01
_Figure 1: Market Towns, Production Centers, and Creative and Financial Services_ <!-- Start of picture text --> Distribution of Cities by Region and City Type<br>200<br>150<br>100<br>50<br>0<br>EAP ECA LAC MENA OECD SAR SSA<br>Region<br>City Type Market Town Production Center Creative and Financial Services<br><!-- End of picture text --> Looking at the distribution of PM 2.5, we find that pollution varies widely across regions. Figure 2 depicts the distribution of pollution for each region and at two points in time, 2006 and 2015 (initial and end points of our period of analysis). Each of the graphs in the figure corresponds to one region. In each graph, we show in orange the distribution corresponding to the year 2006, and in blue the one corresponding to 2015.

The first thing to notice is that the distribution in some regions spans throughout the whole range of pollution (for example in East Asia Pacific), while pollution in other regions is concentrated around certain levels (for example, in Latin America maximum levels of pollution reach approximately 13). This is interesting because regions have certain development patterns that can be associated with pollution levels. This then motivates our next analysis: is there a relationship between city pollution and development or income levels?

_Figure 2: Distribution of PM 2.5 annual means by region_ 6
Currently, all Ugandan MNOs offer mobile money services.<sup>23</sup> The mobile phone density has also contributed to growth of mobile money. The number of registered customers of mobile money services increased from 1,683,713 in 2010 to 2,879,968 during 2011, while the amount transferred by customers rose from Ushs.962.7 billion to Ushs.3.7 trillion over the same period. In terms of volume, the service registered a 204 percent increase in number of transactions from 28.8 million transactions in the year to December 2010 to 87.5 million transactions in December 2011.<sup>24</sup> According to Global Findex Data, Uganda is one of the countries with a largest share of the population using mobile phones to pay bills and send or receive money (about 27 percent of total adult population). About half of the users of mobile money services are unbanked.

**Table 4.  Mobile money services in Uganda**

|MNO|Mobile money service<br>technology|Launch year|
|---|---|---|
|MTN Uganda|USSD / STK|2009|
|Airtel Uganda|USSD / STK|2012(2009)*|
|Uganda Telecom|USSD|2010|
|Warid Telecom|USSD|2012|
|Orange Uganda|USSD|2013|

_* Former Zain which was acquired by Airtel in 2012 launched the service in 2009. Mobile services of both companies were merged in 2013._ All MNO providers used similar mobile technology; an Unstructured Supplementary Service Data (USSD) protocol is used by GSM cellular telephones to communicate with the service provider's computers. USSD can be used for WAP (Wireless Application Protocol) browsing, prepaid callback service, mobile-money services, location-based content services, menu-based information services, etc. USSD is a common mobile financial transaction protocol, convenient enough to cover most mobile handsets used among users and to efficiently serve people living in underserved areas. The SIM Application Toolkit (STK) used by some providers also enables the Subscriber Identity Module (SIM) to initiate actions which can be used for various value-added

> 23 MTN Mobile Money(MTN Uganda), M-Sente from UTL(Uganda Telecom) and Orange Money(Orange Uganda, Close to launch), Airtel Money from Airtel and Warid Pesa (Warid Telecom).

> 24 Bank of Uganda (2011).

15
||individuals’ lives, and treating data as stories, enabling<br>researchers to take account of research participants’ own<br>evaluations|
|---|---|
|Text mining|Analyzing naturally occurring text to discover and capture<br>semantic information|
|Micro-interlocutor analysis|Analyzing information stemming from one or more focus<br>groups about which participants respond to each question,<br>the order in which participants respond, the characteristics<br>of each response, the nonverbal communication used, and<br>the like|

Source: Bergman (2010)

# **_The most common qualitative data collection techniques used in impact evaluations_**

The most common qualitative data collection techniques are in-depth interviews, focus groups and case studies. Unfortunately, the details on data collection techniques are commonly underreported in the few private sector development evaluation reports that have used a mixed methods approach. As such, it is difficult to provide detailed examples of how these methods were selected to answer specific questions or how they proved helpful in dealing with challenges that emerged in the course of conducting the research. The aim of this concluding section is thus to give an overview of the main characteristics, advantages and disadvantages of each method.

# _<u>Focus Groups</u>_

Focus groups are used to collect different points of view through a facilitated group discussion. Focus groups allow interaction among individuals who discuss and listen with others; participants assess their positions or opinions about a specific topic in the light of the views of others, and convey insights regarding their particular knowledge of a specific social context. As such, focus groups are often used to analyze sensitive and/or complex issues (on which, by definition, a range of views are likely to be held), and to explore how people talk about a topic. When carefully moderated, focus groups lead to self-reflection and to participants feeling sufficiently comfortable to articulate and refine their points of views. This dynamic also contributes to eliciting perspectives less influenced by the interaction of the researcher (Finch and Lewis, 2003), or which the researcher may initially have not even considered.

24
dataset that reported awareness of the existence of registration centers for the program. The difference between the two samples was very small; a large majority of households that reported awareness of the program also knew the location of the registration office (see table 1). Thus, the results using either sample were nearly identical. We report only the results obtained from the first sample. We estimate equation (2) and include as explanatory variables the expected benefit of application and all of the explanatory variables from the estimation of equation (1). Note that households are implicitly assumed to use their own characteristics, not only their estimate of their scores, in assessing their probability of enrollment if they apply to the program. Because we use the results from equation (1) to estimate the expected benefit of application, we have bootstrapped the standard errors.

The results are reported in the second column of table 4.<sup>9</sup> The key result is that the expected benefit of application has a significant impact on the application decision. Participation in other social programs has a large and significantly positive impact on participation in the program, suggesting unobservable characteristics related to the cost or benefit of application that are common across social programs. Education has a significantly negative impact on application, which may be due to an income effect or may reflect stigma.<sup>10</sup> Distance to the registration center has a positive impact on application, which is puzzling because distance would be expected to increase the cost of applying. Note that this study focuses on a sample of urban households; the importance of distance may be better identified in rural settings.

Finally, we estimate the determinants of information using the entire sample of households in our dataset. In particular, we estimate equation (3) with the expected benefit of knowledge and all of the explanatory variables from the estimation of equation (1) as explanatory variables. Because we use the results from equation (2) to estimate the expected benefit of knowledge, we have bootstrapped the standard errors. The results are reported in the last column of table 4. Consistent with the model, the 16