rafmacalaba/datause-encoder-data huggingface dataset:
This model allows us to look at the impact of deaths that occurred 0-4 years ago, 5-8 years ago, and 9-13 years ago. We report the results in Table 5. The dependent variable is the same as in Table 4: the change in farm hours from 1991 to 2004. We report results from 4 regressions, each separated by the dotted line in the table. In these regressions, we examine the association of farm labor hours with adult deaths in the recent and more distant past. Estimating the extent of this association over time adds to our understanding of potential dynamics in the effects of adult mortality on the elderly. This aspect of our analysis is novel, and is made possible by the length of our panel data set.

The results of the first regression suggest a pattern over time in the association between farm labor hours and adult death. For co-resident prime-age adult deaths, this association is increasing (and measured more precisely as well) over time. There is no significant association between non-co-resident adult deaths and labor supply changes over time, but the standard errors are large for all three categories.

In the second regression, whose results are presented in the second set of rows in Table 5, we interacted the PHHM death variables with a dummy that equals 1 if the elderly respondent is female. We find again that the association between co-resident deaths occurring in the more distant past, which we found in the first regression’s results, is strongest for elderly women. On the other hand, elderly men are the ones whose labor supply appears to be most strongly tied to non-co-resident adult deaths, especially those occurring in the more distant past.

As in Table 4b, the gender of the deceased matters with respect to changes in farm hours (3<sup>rd</sup> regression of Table 5). We find that there are small and statistically insignificant impacts of a co-resident male adult death in the household, but the impact of co-resident female deaths is large and statistically associated with more distant death shocks. In the final regression reported in Table 5, we investigate how the effects of adult deaths over time vary by whether or not the deceased adult was the elderly respondent’s own child. Co-resident deaths of own children are associated with increases in labor hours for deaths in the middle and more distant past categories, whereas co-resident deaths of adults who were not the children of the elderly are associated with increases in 20
31 : The earlier discussion predicts that the “Number of years from first year of crisis to year of next election” should be positively related to the size of fiscal transfers: the further in the future that citizens can use the vote to punish political actors who cater excessively to special interests, the larger are the benefits to special interests. The variable has the predicted positive sign in all of the regressions of Table 1, but is significant only in Regression 3.5° However, the sample includes countries where elections, even when they are held, are not necessarily competitive and therefore pose less of a threat to incumbent politicians. When regressions 1 and 2 are run only on the 21 countries for which the variable Executive Index of Electoral Competitiveness (E/EC) from the Database on Political Institutions is at its highest value (seven), the coefficients on the proximity of elections variable rise substantially and are more significant.>! For example, in regression 1, the coefficient rises from 1.6 to 2.7 (p ~0.10). The £- coefficient of 0.29 in the sub-sample indicates that a 1.3 year increase in the time to the next election is associated with a 2.6 percentage point increase in fiscal transfers.

The two control variables are not significant and have little effect on the other coefficients. Income per capita is highly insignificant in regression 2: the wealth of countries apparently has no effect on the fiscal costs of banking crises as a fraction of national income. Moreover, although the number of veto players (checks) and the size of the financial sector are positively correlated with income per capita (at approximately 0.23 in this sample), all of the terms from regression 1 retain the predicted signs and approximately the same statistical and economic significance in regression 2.32 30 It is not the specification in Regression 3, which controls for terms of trade shocks, but rather the particular sub-sample, that is responsible for this result, since the election coefficient is even more significant when the terms of trade is omitted in that regression. 31 The maximum score means that multiple parties and candidates compete for executive office and no single candidate receives more than 75 percent of the vote. 32 Using the log of per capita income yields the same result.
management services available for residents. In all communities included in the sample, city authorities provide central communal containers. Households transport their household waste to the disposal point and pay a user fee. In the community survey, the skip containers are reported being emptied twice a week or more. There are also formal and informal garbage pick-up options for residents of slum areas. In Mamobi, Nima and Pigfarm, formal service providers such as Zoom Allianz and ABC provide households with dustbins and collect refuse directly from households. Informal door-to-door waste collection is reported in Nima, Gbegbeyise, Jamestown and Mamobi and is carried out by tricycle and donkey operators. In the case of Jamestown, it is reported that informal collectors dump waste in the Lagoon. In Nima and Mamobi, city authorities discourage the use of informal operators and instead encourages households to register with the Assembly contracted private providers previously mentioned. In addition, most neighborhoods report having community cleaning days with regularity.

Households in the Poverty-DRM survey tend to have less secure tenure arrangements than in the GLSS6. Among the households in the Poverty-DRM survey, fewer households claim ownership of their dwelling unit. Renting and “rent free” are more common arrangements and are closely related to land ownership, which will be discussed in more detail below. In Accra, “rent free” housing is commonly referred to as family house (Korboe, 1992) and it is a right to a house/room that one derives from being a member of a family. Such right is not always limited to the immediate family but also accommodates external family members as well. Family houses are safeguards against homelessness. As poor family members who may not be able to afford rent can always fall back on the family housing for free accommodation.

_Table 16: Tenure arrangement Poverty-DRM survey and GLSS6 (GAMA)_

|Ownership of a housing unit|DRM|GLS|
|---|---|---|
|Owning|20.8%|35.3%|
|Renting|44.2%|41.0%|
|Rent free|35.0%|23.8%|

There are three main types of tenure arrangements observed in the Poverty-DRM survey results – (i) owners of both land and dwelling, (ii) renters of dwellings on land owned by private individual and (iii) rentfree households living in dwelling and on land owned by non-household relative. A few households also report owning the dwelling on land owned by a non-household relative and a small group of households report paying rent to non-household members owning the land. The community survey results show that traditional leaders are the predominant owner of land inside the communities. It is reported that the land is mainly obtained through purchase and inheritance. Tenure structure differs markedly across neighborhood, due to the different histories of these places. Jamestown and Nima, for example, have existed since colonial times and are very established neighborhoods in Accra. Community leaders are not reporting on any recent push for relocation of slum dwellers in the areas included in the survey.

40
Kaseke, N. and S. Hosking (2012). Cost of electricity load shedding to Mines in Zimbabwe: Direct Assessment Approach. International Journal of Physical and Social Sciences, _2_ (6), 207-237.

Nepal Electricity Authority (NEA). Annual Reports for the period between 2007 and 2017.

Oseni, M. and M. Pollitt (2013). The Economic Costs of Unsupplied Electricity: Evidence from Backup Generation among African Firms, Department of Economics Working Paper #1351. University of Cambridge.

Shoven, J.B. and J. Whalley (1984). Applied General Equilibrium Models of Taxation and International Trade: An Introduction and Survey. Journal of Economic Literature XXII, pp. 1007-1051.

Shoven, J.B. and J. Whalley (1992). Applying General Equilibrium. Cambridge University Press. Shrestha, R. S. (2010). Electricity Crisis (Load Shedding) in Nepal, Its Manifestations and Ramifications, Hydro Nepal, Issue 6, pp. 7-17.

Stone, R. (1954). Linear Expenditure Systems and Demand Analysis: An Application to the Pattern of British Demand. The Economic Journal, Vol. 64, No. 255, pp. 511-527.

18
Ministry of Irrigation and Water Development ( _M.Water_ ), the Malawi Red Cross Society ( _Red Cross_ ), and the National Smallholder Farmers’ Association of Malawi ( _NASFAM_ ).

Only MET has explicit responsibilities in the realm of climate change specified in its _de jure_ mandate (as the technical focal point for the United Nations Framework Convention on Climate Change). While the remaining institutions have a role to play in climate adaptation, they do not have a mandate to do so. A summary of their stated missions and potential role for adaptation is presented below.

**Table 1:** The six institutions examined in this study

|**Institution **|**Mission **|**Main role inclimate adaptation **|
|---|---|---|
|**MET**<br>**_Meteorological Services_**|Provide weather and climate<br>information and services|Produce scenarios of climate change<br>and provide early warnings; improve<br>communicationof forecasts|
|**Dis. Mgment**<br>**_Department ofPoverty and_**<br>**_Disaster Management_**<br>**_Affairs_**|Direct and coordinate disaster<br>management activities in Malawi|Improve preparedness and response for<br>expected changes in disaster profile|
|**M.Agr**<br>**_Ministry of Agricullture_**<br>**_annd Food Security_**|Accelerate broad-based agricultural<br>and rural development|Inform farmers about climate change;<br>promote farm practices better suited for<br>expected climate conditions|
|**M.Water**<br>**_Ministry of Irrigation and_**<br>**_Water Development_**|Ensure equitable access to water at<br>all times for all Malawians|Ensure reliability of water supply<br>systems under different climate|
|**Red Cross**<br>**_Malawi Red Cross Society_**|Alleviate human suffering|Prepare for more frequent disasters;<br>reduce vulnerability ofthose atrisk|
|**NASFAM**<br>**_National Smallholder_**<br>**_Farmers’ Association of_**<br>**_Malawi_**|Empower farmers through<br>collective action|Promote adaptation practices among<br>members; scale up risk sharing schemes|

# _Meteorological Department (MET)_

The mission of MET is to provide reliable, responsive and high quality weather and climate services to meet national, regional and international obligations through timely dissemination of accurate and up-todate data and information for socio-economic development, including early warnings. Specifically MET functions include (a) safety of life and property through natural disaster early warnings; (b) safety and regularity of air transport through weather reports at airports; (c) production of food and fiber through agro-meteorological advisories; and (d) management and maintenance of national climate data bank.

13
# **Table A5. Model 3: Odds Ratios of Logistic Regression Predicting Employment of Married Women Controlling for the Presence of Other Females and Males in the Household**

||**198**|**3**|**199**|**3**|**200**|**4**|**200**|**7**|**200**|**9**|**201**|**1**|**AL**|**L**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**or **|**se**|**or **|**se**|**or **|**se**|**or **|**se**|**or **|**se**|**or **|**se**|**or **|**se**|
|**Child aged 0–5**|**1.119***|**_0.074_**|**1.003**|**_0.062_**|**0.998**|**_0.083_**|**0.828***|**_0.083_**|**0.712*****|**_0.066_**|**0.828***|**_0.088_**|**0.887*****|**_0.033_**|
|Females aged 6–9|1.109*|_0.065_|1.190***|_0.068_|1.203**|_0.088_|1.148|_0.096_|1.219**|_0.103_|1.330***|_0.140_|1.202***|_0.042_|
|Females aged 10–15|1.299***|_0.074_|1.363***|_0.070_|1.542***|_0.099_|1.250***|_0.090_|1.261***|_0.094_|1.213***|_0.088_|1.305***|_0.037_|
|Females aged over 50|1.551***|_0.116_|1.581***|_0.108_|1.333***|_0.114_|1.257**|_0.121_|1.366***|_0.136_|1.365***|_0.137_|1.387***|_0.053_|
|Males aged 6–9|1.163***|_0.067_|1.118**|_0.060_|1.093|_0.079_|1.021|_0.089_|1.115|_0.091_|1.117|_0.094_|1.100***|_0.035_|
|Males aged 10–15|1.165***|_0.064_|1.018|_0.051_|1.421***|_0.096_|1.113|_0.080_|1.243***|_0.093_|1.252***|_0.093_|1.202***|_0.035_|
|Males aged over 50|0.905|_0.070_|0.935|_0.070_|1.043|_0.102_|1.079|_0.116_|0.903|_0.101_|0.965|_0.096_|0.967|_0.041_|
|**Husband’s education:**|||||||||||||||
|Below primary|0.779***|_0.058_|0.800***|_0.058_|0.881|_0.087_|0.715***|_0.085_|0.732**|_0.095_|1.106|_0.132_|0.828***|_0.037_|
|Primary|0.593***|_0.046_|0.705***|_0.053_|0.766***|_0.071_|0.536***|_0.061_|0.715***|_0.082_|0.680***|_0.077_|0.652***|_0.027_|
|Secondary|0.347***|_0.031_|0.442***|_0.036_|0.528***|_0.051_|0.398***|_0.046_|0.421***|_0.046_|0.446***|_0.047_|0.428***|_0.019_|
|Post-secondary|0.299***|_0.039_|0.291***|_0.028_|0.364***|_0.041_|0.308***|_0.046_|0.296***|_0.042_|0.329***|_0.040_|0.312***|_0.017_|
|**Husband’s**<br>**employment:**|||||||||||||||
|Regular|0.462***|_0.037_|0.442***|_0.034_|0.441***|_0.045_|0.529***|_0.059_|0.580***|_0.061_|0.603***|_0.063_|0.516***|_0.022_|
|Self-employment|0.639***|_0.051_|0.590***|_0.043_|0.512***|_0.052_|0.594***|_0.065_|0.555***|_0.056_|0.649***|_0.067_|0.583***|_0.024_|
|Casual|1.105|_0.099_|1.062|_0.090_|0.715***|_0.077_|0.988|_0.122_|0.704***|_0.076_|0.824*|_0.095_|0.856***|_0.039_|
|_cons|0.062***|_0.037_|0.015***|_0.008_|0.054***|_0.041_|0.092***|_0.081_|0.031***|_0.025_|0.047***|_0.037_|0.046***|_0.015_|
|_N_|25,00|1|28,68|4|28,3|36|26,9|28|26,3|40|26,24|7|161,5|36|

Notes: *** p<0.01, ** p<0.05, * p<0.1 In the NSS, data for OBCs were not available before 2004.

The model also includes controls for the same woman’s characteristics as in the previous models.

24
Policy Research Working Paper 6902

# **Abstract**

An audit study was conducted in peri-urban Mexico to understand the quality of information and products offered to low-income potential customers. Trained auditors visited multiple financial institutions seeking credit and savings products. Consistent with Gabaix and Laibson (2006), staff voluntarily provides little information about avoidable fees, especially to auditors trained to reveal little knowledge about the market. In addition, clients are almost never offered the cheapest product, most likely because staff is incentivized to offer more expensive products that are thus more profitable to the institution. This suggests that disclosure and transparency policies may be ineffective if they undermine the commercial interest of financial institutions.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at xgine@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> @ THE WORLD BANK<br><!-- End of picture text --> <!-- Start of picture text --> Candidate Information<br><!-- End of picture text --> ID Number: CID67929 <!-- Start of picture text --> Age: 34<br><!-- End of picture text --> Location: Other Date of Assessments: 2016-12-06 The candidate obtained the following assessment results: <!-- Start of picture text --> Numeracy1 ‘Communication2 Concept Formation3 Planning4 Ability Focus5 Grit Scale6<br><!-- End of picture text --> The candidate completed assessments in Numeracy, English Communication (listening, reading, comprehension), and Concept Formation:

1. The Numeracy test measures a candidate's ability to apply numerical concepts at a National Qualifications Framework (NQF) level, such as working with fractions, ratios, money, percentages and units and performing calculations with time

- and area. This score is an average of two numeracy tests the candidate completed.

2. The English Communication test measures a candidate's grasp of the English language through listening, reading and comprehension. It assesses at an NOF level, for example measuring the ability to recognise and recall literal and nonliteral text.

3. The Concept Formation Test is a non-verbal measure that evaluates a candidate's ability to understand and solve problems. Those with high scores are generally able to solve complex problems, while lower scores indicate an ability to solve less complex problems.

The candidate also completed standardised questionnaires to assess their soft skills: 4. The Flexibility Scale measures whether candidates actively consider several approaches to solving a problem. Those with high scores generally explore several avenues to find the best possible solution, while low scores indicate considering fewer approaches. In lieu of the Flexibility scale, some candidates will have the Planning scale listed. Flexibility and Planning should be used interchangeably.

5. The Control Scale measures whether candidates react impulsively or systematically when faced with problems. Candidates with high scores generally deal with problems systematically, while those with lower scores tend to react spontaneously. In lieu of the Control scale, some candidates will have the Focus scale listed. Control and Focus should be used interchangeably.

6. The Grit Scale measures whether candidates show determination when working on challenging problems. Those with high scores generally spend more time working on challenging problems, while those with low scores choose to pursue different problems.

Ready to contact the candidate? Click to view contact information:
# **Table 6: Mean reported forgone wages for casual work compared to reported actual wages**

**<u>for those working in the same month</u>**

|||Reporte<br>worki<br>|d forgone<br>ng on BRE<br>|wage for<br>GS (Rs/d<br>|those<br>ay)<br>|Casual<br>work<br>|wage earning<br>ing on BRE<br>|s for tho<br>GS (Rs/d<br>|se not<br>ay)<br>|
|---|---|---|---|---|---|---|---|---|---|
|||Mean|Median|SD|N|Mean|Median|SD|N|
|||||**All ind**|**ividuals**|||||
|R1|May 09|67|70|21|49|71|70|30|418|
||June 09|68|70|17|32|72|70|30|571|
|R2|May 10|85|85|33|45|87|100|38|361|
||June 10|81|100|27|31|83|90|41|337|
|||||**Men**|**only**|||||
|R1|May 09|71|70|20|38|78|75|29|325|
||June 09|68|70|17|32|79|80|27|454|
|R2|May 10|96|100|29|29|96|100|36|260|
||June 10|90|100|23|18|95|100|37|240|

_Notes_ : The right-hand panel is based on responses to the wages actually received by casual workers in the week preceding the survey for all non-BREGS work (this could include other public works). _Source:_ Authors’ estimates based on BREGs Survey.

36
depends on the variance in the implementation lags (still given the same mean lag). Greater random variance in the lags, all else equal, results in greater underestimation of the ITT effects because of a larger classical random measurement error in a right-side variable that biases the estimated coefficient more towards zero.

Implementation delays per se do not necessarily affect estimates of treatment-on-thetreated (TOT) effects if the start of the treatment for individual beneficiaries has been identified correctly. In some cases, this date of entry can be relatively easy to identify as, for instance, when the dates on which beneficiaries enroll in a program can be captured through a household or facility survey or administrative records (e.g., school enrollment rosters or clinic logbooks). In other cases, however, the identification may be more difficult, such as when beneficiaries are unable to distinguish among alternative, contemporaneous programs or to recall their enrollment dates or when the facility or central program office does not monitor beneficiary program enrollments.<sup>9</sup> Nonetheless, even if the variation in treatment dates within program areas is handled adequately and enrollment dates are identified fairly accurately at the beneficiary level, nonrandom implementation delays bias TOT estimates. Even a well-specified facility or household survey is still going to leave the concern of unobservables that may be related to the direction and size of the program impact, and the duration of exposure, like program take-up, has to be treated as endogenous. The problem of selection bias motivates the choice of random assignment to estimate treatment effects in social programs.

# **_Learning by providers_**

A different implementation lag is associated with the fact that program operators (or providers of services) themselves face a learning curve that depends on time in training and onthe-job experience, most likely producing some variation in the quality of program 11