rafmacalaba/datause-encoder-data huggingface dataset:
Bamberger, Michael. “Introduction to mixed methods in impact evaluation.” Interaction.org 2012. Available at http://www.interaction.org/impact-evaluation-notes.

Bamberger, Michael, Vijayendra Rao, and Michael Woolcock. "Using mixed methods in monitoring and evaluation: experiences from international development", in Abbas Tashakkori and Charles Teddlie (eds.) _Handbook of Mixed Methods in Social and Behavioral Research_ (2<sup>nd</sup> revised edition) Thousand Oaks, CA: Sage Publications, pp. 613-641, 2010.

Bamberger, Michael. “Conducting quality impact evaluations under budget, time and data constraints.” World Bank, Independent Evaluation Group/Poverty Analysis, Monitoring and Impact Evaluation Thematic Group, PREM Network, 2006.

Bamberger, Michael. “Integrating quantitative and qualitative research in development projects.” World Bank Publications, 2000.

Barron, Patrick, Rachael Diprose and Michael Woolcock. _Contesting Development: Participatory Projects and Local Conflict Dynamics in Indonesia_ . New Haven: Yale University Press, 2011.

Bennett, Andrew. “Process tracing and causal inference”, in Henry Brady and David Collier (eds.) _Rethinking Social Inquiry_ (2<sup>nd</sup> ed.) Rowman and Littlefield, 2010.

Bloom, Nicholas, Benn Eifert, Aprajit Mahajan, David McKenzie and John Roberts. “Does management matter? Evidence from India” _Quarterly Journal of Economics_ 128(1): 1-51, 2013.

Biddulph, Robin. “Cambodia’s Land Management and Administration project” WIDER Working Paper No. 2014/086. Helsinki: UNU-WIDER, 2014.

Bruhn, Miriam and David McKenzie. “Using administrative data to evaluate municipal reforms: an evaluation of the impact of Minas Fácil Expresso.” Washington DC: World Bank Policy Research Working Paper No. 6368, 2013.

Boyce, C and Neale, P. _Conducting In-Depth Interviews: A Guide for Designing and Conducting In-Depth Interviews for Evaluation Input, Monitoring and Evaluation_ – 2, 2006. Pathfinder International. Available at <u>http://www2.pathfinder.org/site/DocServer/m_e_tool_series_indepth_interviews.pdf?docID=630 1</u> Cadot, Olivier, Ana M. Fernandes, Julien Gourdon and Aaditya Mattoo. “Are the benefits of export support durable? Evidence from Tunisia.” World Bank, 2012. <u>https://openknowledge.worldbank.org/handle/10986/12189</u> 32
Figure 5. Pensions Granted by the Program of Incentives. Years 2012-2017 <!-- Start of picture text --> 30000<br>25000<br>20000<br>15000<br>10000<br>5000<br>0<br>2012 2013 2014 2015 2016 2017<br>Public Private Volunteer<br><!-- End of picture text --> Source: IESS The inequality patterns in terms of active coverage identified above are reproduced at retirement. In this sense, the passive coverage provided by the compulsory insurance is clearly biased in favor of top income quintiles (Figure 6). In this sense, in year 2016, while 5% of older people in the first income quintile were receiving a contributory social security benefit, this percentage was 53% among those in the fifth quintile.

Figure 6. Percentage of Older people over 65 Years of Age Receiving a Contributory Benefit per Household Income Quintile. 2003 - 2016 <!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>I II III IV V<br><!-- End of picture text --> Source: author elaboration based on ENEMDU 12
use of these deduction options, the taxpayer can make a refund request through form 402. This form requires detailled information on the withholding agent, amount of tax withheld and timing of withholding, and asks the taxpayer to certify that she has no outstanding liabilities in any other tax.

# 3.4 Data

Our analysis combines anonymized tax return data and third-party and withholding declarations from the General Directory for Taxation in Costa Rica. The tax return data contains the universe of income tax declarations (D101) for 2006-2014 and sales tax declarations (D104) for 2008-2014, as well as the corresponding payment returns (D110) for the income and sales tax. Since 2006, all tax returns have been digitzed, and electronic ling has gradually been introduced for the dierent declarations, ensuring that the data have nearly complete coverage and a high degree of acurracy. The ling software EDDI-7 conducts automatic validation checks to ensure the internal consistency of led returns. The data contain all line items of the tax return, including rm type and sector, income sources, cost items, deductions, gross and net liability and payment.The nal data set contains 112,000 to 250,000 self-employed per year, 90,000 to 150,000 corporations and 58,000 to 70,000 sales tax lers per month.<sup>21</sup> We merge the tax records with the informative declarations D150, D151, D153 and D158, also for the period 2006-2014. These data have been led eletronically through the DECLAR@7 system, which conducts similar validation checks as EDDI-7. Table III provides an overview of the number of records and their coverage for each of the informative declarations.

Declaration D151 registers both the largest number of observations, and the widest coverage, being available for approximately half of all rms. The coverage is similar for the self-employed and corporations. The ling of informative declarations is more concentrated than the coverage, meaning that an even small share of rms act as informants (results available upon request). Note that information reporters are slightly more likely to report their own costs rather than their own sales, as evidence by the fact that 54.3% of the D151 records represent sales records. Declaration D158 is similar to D151 in that sense, but has much lower coverage, given the specic nature of the transactions it covers (agricultural auctions). In our analysis, we thus use the sum of third-party information on sales/costs from D151 and D158. We henceforth refer to these reports as information reports by other rms or D151 reports.

Withholding by state institutions and nancial institutions, as reported in D150 and D153, has a

> 21Only the tax records for 2012-2014, and a small share of records for 2010 and 2011 have rm type indicators (selfemployed or corporation). During this period, we observe only a handful of rms switching rm type. We therefore use the 2010-2014 tax return data and the tax register to assign a rm type to the tax returns for 2006-2011. We drop returns for which we cannot determine the rm type with this strategy.

13
disturbance term distributed as N(O,cy). Because our main interest is predicting the value of ln(y) rather than assessing the impact of each explanatory variable, we are not concerned about the possible endogeneity of some of the explanatory variables.

Hentschel _et al._ (2000) show that the probability that household i with characteristics Xi is poor can be expressed as: where Pi is a variable taking a value of 1 if the household is poor and 0 otherwise, z is the poverty line expressed in terms of consumption expenditure per capita, and 1D is the cumulative standard normal function.

# **2.3 Applying regression results to the census data**

In the second step, the estimated regression coefficients from the first step are combined with census data on the same household characteristics to predict the probability that each household in the Census is poor. This is accomplished by inserting the household characteristics for household i from the census, Xic, into equation 2: For a given area (such as a district or province), Hentschel et al (2000) show that the proportion of the population living in households that are below the poverty line is estimated as the mean of the probabilities that individual households are poor: 8
steps (such as issuing indexed government<sup>bondsandsetting upa riskadjustment</sup> mechanism) will be taken to make<sup>them feasible.If survivors' benefitsare weakened,if</sup> joint annuities are not required, if women<sup>mustchoosebetweenownbenefitandthe</sup> widow's benefit from the public<sup>pillar,andif fewerwomenaremarriedanyway, then</sup> older women may face a problem<sup>inmaintainingtheirstandardoflivingandveryold</sup> women may find themselves living<sup>close to the poverty line.</sup>

# VlHI. 0oEndm¢onof

Our empirical investigations show<sup>that(1)women'sown-annuitiesarelowerthan</sup> those of men in multi-pillar pension<sup>schemes,but (2)women arerecipientsof net public</sup> transfers and private intra-household<sup>transfersthatraisetheirrateofreturnon</sup> contributions above that of men. As a<sup>resultof theseforces(3)womenarethemajor</sup> recipients of redistributions and<sup>havegained morethanmenfromthereforms-the</sup> lifetime gender ratio has improved.<sup>Theseredistributionsandimlprovedgender ratios</sup> stem from the targeting of the<sup>publicpillarstowardlowearnersand,evenmore</sup> important, from regulations over payouts<sup>from the private pillar.Womentendto below</sup> earners, hence beneficiaries of targeting<sup>in the new publicpillars.Women tendto outlive</sup> men, so restrictions on payouts systematically<sup>redistributefrom husbands to wives.Joint</sup> annuity requirements play the largest<sup>role.Thesetransfers,takentogether,raise women's</sup> lifetime pensions to a level that is 60-80%<sup>thatof men and100%or morefor fullcareer</sup> married women. Women get a higher<sup>rateof return than men in the newsystemsanda</sup> higher relative benefit than they did<sup>intheoldsystems.Thegendergapisnarrowed</sup> further for women who live during<sup>periodsofslowgrowth,since disparateown-</sup> accumulations are smaller and the equalizing<sup>public pillar is relatively larger.</sup> Some caveats: While women as a group<sup>gained in our three Latin Americancountries,</sup> different groups of women benefited<sup>the most ineach case andsome gapsemerge.Single</sup> women and those cohabiting without<sup>aformal marriagecontractreceivemuch lower</sup> lifetime benefits than men or married<sup>women, because they have lower wages and greater</sup> longevity than men and don't gain<sup>fromthe joint annuity,as do marriedwomen.Even if</sup> they work full career, their pensions<sup>will be relatively low solong as their wage ratesand</sup> 37
|P125120|2014|2015|6/11/2015|4 Active|Mozambique|
|---|---|---|---|---|---|
|P125120|2014|2016|12/22/2015|5 Active|Mozambique|
|P125120|2014|2016|6/3/2016|6 Active|Mozambique|
|P125120|2014|2017|12/22/2016|7 Active|Mozambique|
|P125120|2014|2017|6/27/2017|8 Active|Mozambique|
|P125120|2014|2018|12/28/2017|9 Active|Mozambique|
|P125120|2014|2018|6/27/2018|10 Active|Mozambique|
|P125120|2014|2019|10/4/2018|11 Active|Mozambique|
|P125120|2014|2019|5/13/2019|12 Active|Mozambique|
|P125120|2014|2020|11/6/2019|13 Active|Mozambique|
|P125120|2014|2020|5/8/2020|14 Active|Mozambique|
|P125127|2011|2011||0 Closed|Mozambique|
|P125127|2011|2012|9/21/2011|1 Closed|Mozambique|
|P125127|2011|2012|6/5/2012|2 Closed|Mozambique|
|P125127|2011|2013|12/22/2012|3 Closed|Mozambique|
|P125127|2011|2013|6/22/2013|4 Closed|Mozambique|
|P125127|2011|2014|12/23/2013|5 Closed|Mozambique|
|P125127|2011|2014|6/10/2014|6 Closed|Mozambique|
|P125127|2011|2015|11/30/2014|7 Closed|Mozambique|
|P125127|2011|2015|3/14/2015|8 Closed|Mozambique|
|P125127|2011|2016|10/22/2015|9 Closed|Mozambique|
|P125127|2011|2016|3/31/2016|10 Closed|Mozambique|
|P125127|2011|2017|7/29/2016|11 Closed|Mozambique|
|P125127|2011|2017|2/12/2017|12 Closed|Mozambique|
|P125127|2011|2017|3/21/2017|13 Closed|Mozambique|
|P125127|2011|2018|9/21/2017|14 Closed|Mozambique|
|P125127|2011|2018|3/27/2018|15 Closed|Mozambique|
|P125127|2011|2019|10/3/2018|16 Closed|Mozambique|
|P125127|2011|2019|4/12/2019|17 Closed|Mozambique|
|P125127|2011|2020|9/17/2019|18 Closed|Mozambique|
|P125127|2011|2020|12/30/2019|19 Closed|Mozambique|
|P125132|2011|2011||0 Closed|Cameroon|
|P125132|2011|2012||0 Closed|Cameroon|
|P125132|2011|2013||0 Closed|Cameroon|
|P125135|2013|2013|3/12/2013|1 Closed|Yemen,Republic of|
|P125135|2013|2014|12/23/2013|2 Closed|Yemen,Republic of|
|P125135|2013|2014|6/21/2014|3 Closed|Yemen,Republic of|
|P125135|2013|2015|12/28/2014|4 Closed|Yemen,Republic of|
|P125135|2013|2015|6/17/2015|5 Closed|Yemen,Republic of|
|P125135|2013|2016|2/5/2016|6 Closed|Yemen,Republic of|
|P125135|2013|2017|11/8/2016|7 Closed|Yemen,Republic of|
|P125135|2013|2017|6/27/2017|8 Closed|Yemen,Republic of|
|P125137|2011|2012||0 Closed|Moldova|
|P125137|2011|2013||0 Closed|Moldova|
|P125137|2011|2020|6/30/2020|1 Closed|Moldova|
|P125150|2012|2011||0 Dropped|Haiti|
|P125151|2011|2011||0 Active|Argentina|
# _B. Impacts on Durables and Housing_

SD estimates provide some indications of increased investments in durable goods among beneficiary households (Table 5, Panel B). Beneficiaries have more of some durable goods than non-beneficiaries in 2013, and both the total value of durables and the value of durables purchased in the last 3 years are greater (by 59,432 FCFA and 22,693 FCFA respectively). These results stem from increased investments in durable goods such as plows, carts, motorcycles and pirogues, whose ownership significantly increased.

Results are more mixed on housing quality. DID estimates suggest improvements for several housing quality indicators, including better cooking fuel (instead of wood), improved source of lighting, access to toilets, and number of assets owned (Table 4, Panel A). However, these results are not robust when focusing on the common support sample, where only the impact on access to toilets remains significant (Table 4, Panel B). In addition, SD estimates reveal no robust significant impacts on housing quality (Table 5, Panel C).

# _C. Impacts on Household Enterprises and Agriculture_

Beneficiaries are not found to be more likely to participate in non-agricultural household enterprises (HEs) activities or to have higher HE profits in the SD estimates (Table 5, Panel D). Thus, despite impacts on productive assets, participation in the cash transfer program does not appear to lead to livelihood diversification outside agriculture. This is, perhaps, not surprising given that less than 15% of households in the sample have a household enterprise, and household enterprises are very diverse in terms of activities and scale.<sup>21</sup> There is indication of an increase in the number of household enterprises among beneficiaries, but this effect is not robust to the inclusion of village fixed-effects. Equipment purchased in the last 3 years increases significantly for beneficiaries, but the magnitude is small.

There is, however, evidence of impact on agricultural activities (Table 5, Panel E), and in particular agricultural productivity. Beneficiaries use more agricultural inputs, particularly by spending more on fertilizers. Strong impacts on quantities produced and yields are also found 18 months after the end of the transfers. Some impacts are observed on land area cultivated,

> 21 The point estimates are large and positive for revenues and profits from HEs, but the standard errors are large due to the heterogeneity of household enterprises.

16
and/or reserves. The countries experiencing the largest impacts were Peru, India, Indonesia, Thailand, and Chile. See Table 4 for further details.

**Figure 2: Distribution of Market Pressure Indices I and II**

<!-- Start of picture text --> A: Weighted average of exchange rate B: Weighted average of exchange rate<br>depreciation and decline in reserves, April- depreciation, decline in stock index, and<br>July, 2013 decline in reserves, April-July, 2013<br>-3 0 3 6 9 12<br>-3 0 3 6 9 Capital Market Pressure Index II (Exchange Rate, Stock, Reserves)<br>Capital Market Pressure Index I (Exchange Rate, Reserves) April-July, 2013 April-July, 2013<br>10 8<br>8<br>6<br>6<br>4<br>Frequency<br>Frequency<br>4<br>2<br>2<br>0<br>0<br><!-- End of picture text -->

**Table 4: Market Pressure Indices**

||**April-June**|**April-July**|**April-August**|
|---|---|---|---|
|**Market Pressure Index I: weighte**|**d changes in ex**|**change rate a**|**nd reserves**|
|Mean|1.07|1.37|1.61|
|Median|0.67|0.91|1.17|
|Number of Countries<br>With positive values of Index I|36/51|35/51|31/51|
|Mean for Countries with Positive Values|1.88|2.48|3.56|
|Median for Countries with Positive Values|1.37|1.96|2.87|
|**Market Pressure Index II: weighted chan**|**ges in exchang **|**e rate, reserve**|**s and stockprices**|
|Mean|1.77|2.20|2.50|
|Median|1.49|1.87|2.22|
|Number of Countries<br>With positive values of Index II|28/37|30/37|26/37|
|Mean for Countries with Positive Values|2.74|3.01|4.18|
|Median for Countries with Positive Values|2.41|2.64|3.14|

9
# _Monitoring of CO_

The GasBadge Pro Single Gas Monitor (Industrial Scientific) was used to record minuteby-minute kitchen concentrations of Carbon Monoxide (CO) in the 0-1,500 ppm range in 1ppm increments. All GasBadge monitors were calibrated prior to each round in Berkeley, California, using 50ppm span gas. Drager Carbon Monoxide Diffusion Tubes 50/a-D (50-600 ppm*h) were collocated with the GasBadge monitors in a sub-set of kitchens in each round in order to establish a relationship between the CO readings from the GasBadge Pro and the Drager tubes.

Table 3 presents the descriptive statistics of the 24-hour average concentrations of PM2.5 and CO from the detailed readings of the UCB Particle Monitor and GasBadge Pro. Average concentrations of PM2.5 recorded in the households studied were 0.776 mg/m<sup>3</sup> (baseline), 0.320 mg/m<sup>3</sup> (round 2), 0.444 mg/m<sup>3</sup> (round 3) and average concentrations of CO were 28 ppm (baseline), 21.6 ppm (round 2) and 21 ppm (round 3).

**Table** **<u>3: Concentration of PM2.5 and CO recorded in kitchen (24-hour average)</u>**

||**Mean**|**s.d.**|**Maximum**|**Minimum**|
|---|---|---|---|---|
|**Baseline**<br>|||||
|PM2.5(mg/m<sup>3</sup>)|0.776|1.847|23.803|0.028|
|CO (ppm)|28.0|30.8|227.0|0.1|
|**Round 1**<br>|||||
|PM2.5(mg/m<sup>3</sup>)|0.320|0.398|2.542|0.014|
|CO (ppm)|21.6|28.9|167.7|0.0|
|**Round 2**|||||
|PM2.5(mg/m<sup>3</sup>)|0.444|0.527|4.441|0.037|
|CO(ppm)|21.0|29.3|222.7|0.0|

Source: World Bank household survey (2009-2010) Inter-regional variations in pollutant concentrations were observed: average concentrations of PM2.5 concentrations were generally higher and concentrations of CO were lower in Vatomandry compared to Ambositra. Average concentration of CO declined from the baseline period to round 2 in both locations and did not change much from round 2 to 3. PM2.5 concentrations, on the other hand, did not change over time in Ambositra; but dropped significantly in Vatomandry from the baseline to round 2 and thereafter went up to some extent in round 3.

11
faithful while the other is not, one can go for an HIV test and the other not. For some other behaviors like condom use and discussions with the regular partner, however, one would, in theory, expect a concordant answer<sup>11</sup> . This would be the case for whether or not the two partners used a condom during their last sexual intercourse with each other. But table 2 reveals, when adding the rows for discordant male and discordant female, that in Burkina Faso, Cameroon, Ghana and Tanzania between 5 and 10 percent of couples give a different answer on whether or not they used a condom the last time they had sex<sup>12</sup> . Men are more likely to report using a condom. Similarly, table 11 reports on the combination of answers for whether not the partners ever discussed about AIDS with their spouse<sup>13</sup> . For that question, discordant reports among the partners in the couple are widespread. Between 31 (in Kenya) and 47 percent (Burkina Faso) of the couples have discordant answers to that question. Men are much more likely to report that they have had such a discussion with their spouse. It might be that men and women have a different definition of what constitutes a discussion with their spouse about AIDS (the questions in the surveys were formulated identically, however). But it remains that the level of discordance is sizeable and suggests that there might be systematic biases, different by gender, in the way sexual and HIV/AIDS related behavior are reported.

The second angle through which this study challenges the reliability of self-reported behaviors is by comparing the conclusions taken from the analysis of such reports with the results from the analysis of biomarkers like HIV tests. The previous section has shown that, while married women are very unlikely to report extra-marital sex during the last 12 months, it is extremely difficult to explain the sizeable fraction (between 30 and 40 percent) of HIV infected couples in which only the woman is HIV positive without

> 11 Polygyny, however, might be advanced as reason for the discordance in reported behavior. For example, in a polygynous union, when asked about condom use during the last sexual intercourse with their regular partner, the woman will always refer to the same husband, but the man could refer to another wife than the one who is interviewed. The same could happen when males are asked whether they discussed about AIDS with their partner. I have taken this possibility into consideration and I have performed the same analysis as in tables 2 and 11 for non polygamous unions only (following the example of table 7). The results are very similar to the ones reported for all unions in tables 2 and 11. These results are available on request. 12 De Boer and others (1998) find, similarly, that in Northern Thailand, there are cases of disagreement among couples on their reports about condom use.

> 13 The variable on discussion about AIDS with the partner was not available in the Tanzania AIS 20032004.

17
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

# Annex J Scorecard on cost recovery, efficiency, and equity in tariff structure

||**Co**<br>**reco**|**st**<br>**very**||**Efficiency**|||**Eq**|**uity**|||**To**|**tal score**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Utility|[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|[9]|Equity score|Efficiency<br>score|Cost-<br>recovery<br>score|Total<br>score|
|SONEB|1|0|1|1|1|1|0|0|1|2|3|1|6|
|ONEA|1|0|0|1|1|0|1|0|1|2|2|1|5|
|SODESI|0|0|0|0|0|1|0|1|—|2|0|0|2|
|ELECTRA|1|1|1|1|1|1|1|—|1|3|3|2|8|
|REGIDESO|0|0|1|0|0|1|0|—|0|1|1|0|2|
|AWSA|0|0|1|0|0|1|1|1|1|4|1|0|5|
|Dire Dawa|0|0|1|1|0|1|—|0|—|1|2|0|3|
|ADAMA|0|0|1|0|0|1|—|1|—|2|1|0|3|
|GWCL|1|0|1|1|0|1|0|—|1|2|2|1|5|
|NWASCO|0|0|1|0|0|1|1||1|3|1|0|4|
|KIWASCO|1|0|1|0|1|1||1|0|2|2|1|5|
|WASA|1|0|1|1|0|1|1|0|0|2|2|1|5|
|JIRAMA|0|0|0|0|0|0|0|||0|0|0|0|
|AdeM Pemba|0|0|0|1|0|0|—|0|1|1|1|0|2|
|AdeM Quelimane|0|0|0|1|0|0|1|0|1|2|1|0|3|
|ADeM Beira|0|0|0|0|0|0|—|0|1|1|0|0|1|
|Adem Maputo|0|0|0|0|0|0|—|0|1|1|0|0|1|
|AdeM Nampula|0|0|0|0|0|0|—|0|1|1|0|0|1|
|BWB|0|0|0|1|0|1|—|—|—|1|1|0|2|
|CRWB|0|0|0|1|1|0|0|—|—|0|2|0|2|
|LWB|1|0|0|1|0|0|—|—|0|0|1|1|2|
|Oshakati|1|1|1|1|1|0|—|—|1|1|3|2|6|
|Windhoek|1|1|1|1|1|0|1|—|0|1|3|2|6|
|Walvis Bay|1|0|0|1|1|1|—|1||2|2|1|5|
|SEEN|0|0|0|0|0|0|1|1|1|3|0|0|3|
|Katsina WB|0|0|1|1|1|1|—|1|1|3|3|0|6|
|FCT WB|0|0|1|1|0|1|1|0|1|3|2|0|5|
|Kaduna WB|0|0|1|0|0|1|—|1|1|3|1|0|4|
|Electrogaz|1|0|1|1|0|1|1|1|0|3|2|1|6|
|NWC Upper Nile|1|0|1|0|1|1|0|0|1|2|2|1|5|
|NWC Khartoum|0|0|1|0|0|1|—|0|1|2|1|0|3|
|NWC South Darfur|1|0|1|0|0|1|—|1|1|3|1|1|5|
|SDE|0|0|1|0|0|1|0|1|1|3|1|0|4|
|STEE|0|0|1|1|1|1|1|1|1|4|3|0|7|
|DAEASCO|1|0|1|0|0|1|0|1|1|3|1|1|5|

44