rafmacalaba/datause-encoder-data huggingface dataset:
Canada and the United Kingdom. Thirty-five responses were received of which 33 were usable, a response rate of 67 percent. Supplementary interviews (both in person and on the telephone), were conducted to seek further insight and clarification to the survey results.

In the first section of this paper, after providing a summary of the role of legislatures in financial oversight, we present a general concept of public financial accountability. We underline the critical role that PACs play, noting their origin in the 19<sup>th</sup> century and their widespread use throughout the Commonwealth and elsewhere.

Through the data collected by the WBI, we are able to deepen our knowledge about how PACs work and, in particular, what – in the views of the chairs of these committees – constitutes successful performance and what factors influence such performance. The second section of the paper presents the survey instrument and summarizes the broad findings of the survey.

Section three investigates the success factors (and constraints) for PAC effectiveness, based on the survey results. In section four, we identify possible benchmarks for measuring the effectiveness of PACs, and discuss how developing countries may be able to think about strengthening their PACs.

In the fifth and final section of the paper, we draw some conclusions and propose what an ideal PAC might be like.

# <u>Section 1: Legislatures and Public Financial Accountability</u>

# _i) Role of legislatures_

Legislatures perform three functions - representative, legislative, and oversight. They perform a representation function in that they represent the will of the people, which is the legitimate source of authority in democratic countries. They perform a legislative function because, in addition to introducing legislation on their own, they have the power to amend, approve or reject government bills. And they perform an oversight function, ensuring that governments implement policies and programs in accordance with the wishes and intent of the legislature. They undertake this oversight function in two ways: they oversee the preparation of a given policy ( _ex ante_ oversight) or can oversee the execution and the implementation of a given policy ( _ex post_ oversight).

Though most legislatures have the power to keep the government accountable for its actions and its policies, there is considerable variation in the legislative tools that legislatures can employ to perform their oversight function. This variation reflects to a large extent differences in the form of government and other constitutional arrangements. These means include parliamentary committees, questions in the legislature, interrogations, urgent debates, the estimates process, scrutiny of delegated legislation, private members’ motions and adjournment debates that allow legislators to raise issues relating to the use or proposed use 2
in gross income for farmers annually and the increased wheat production generates an additional 49 million | in gross income.

Using data on production costs from Bhattarai et al. (2021) and CACP (2011) we can also estimate the change in farm profits that comes from this increase in production. Accounting for production costs suggests that the increase in production results in an additional 9 million | from rabi rice production and 12 million | from rabi wheat production. These estimated changes in profits are similar to the benchmark that a back of the envelop calculation using data from Ryan and Sudarshan (2020) would suggest. They find that an increase in depth to groundwater in Rajasthan reduces farm profits by 155 |/HA per meter. Using this estimate and the total area planted on average in rabi rice and rabi wheat in Star states prior to MNREGA we estimate an increase in farm profits of approximately 17 million | annually based on our actual change in depth to groundwater.

All of these estimates of the change in gross farm income and farm profits are a rough approximation of the benefits of the reduction in depth to groundwater. These calculations make significant assumptions about the symmetry of effects of reducing vs. increasing depth to groundwater and use state or nationwide averages for production, profits, and costs. However, our estimates provide a ballpark figure for the benefits that farmers realized from easier access to groundwater after the implementation of MNREGA.

# **7 Conclusion**

Access to reliable sources of groundwater for irrigation remains a critical challenge for Indian farmers. Ensuring long-term sustainability of groundwater withdrawals will likely require policies that encourage conservation (Ryan and Sudarshan, 2020). However, in areas with aquifers that have high surface level connectivity, the construction of surface level infrastructure that encourages recharge may provide a short-term solution to alleviate pressure on groundwater levels.

Our results suggest that surface level water conservation infrastructure constructed as part of the MNREGA program led to meaningful reductions in depth to groundwater in the states that implemented the largest number of these projects. This reduction likely occurred as the construction of new soft-bottomed water storage, flood management, and irrigation infrastructure allowed greater recharge of groundwater by pooling surface water after the completion of these MNREGA projects. This is possible because the high degree of connectivity between surface water and groundwater in much of India.

The reduction in the depth to groundwater we document likely provided farmers with more reliable and less expensive access to groundwater for irrigation, especially during the rabi season. This more reliable access to groundwater likely relaxed constraints on farmer behavior with respect to irrigation and planting choices. We observe that soil moisture, an aggregate measure of irrigation, increases noticeably after the implementation of MNREGA both in the annual average and specifically during the rabi season. We also observe farmers irrigate more area in high value, water 15
# liv INTRODUCTON

Table **11** Long-Run and Stationary Population, Momentum, Doubling Year, and Year When Net Reproduction Rate Reaches 1 by Geographic Region and by Income Group

|||Populatio|n (miltto|n)|Station-<br>ary over|Popu-|Year<br>when||
|---|---|---|---|---|---|---|---|---|
|Region or<br>Income group|1985|2050|2100|Station-<br> <br>ary|1985<br>popula-<br>tion|lation<br> <br>mo-<br>mentun|popula-<br>tion<br>doubles|Year<br>when<br>NRR=1|
|World|4,844|10,035|11,330|<br>11,514|2.4|1.4|2044|2060|
|Less developed (a)|3.666|8,716|10,020|10,200|2.8|1.5|2028|2060|
|MoredevelopedCa)|1.179|1,319|1,310|<br>1.314|1.1|1.1|tc5|2030|
|Africa|559|<br>2,275|2,962|3,0490|<br>5.5|1.6.|2009|2060|
|East Africa|231|1.034|1,383|<br>1,424|6.2|1.6|2008|2055|
|West Africa|204|873|1.152|<br>1,190|5.8|1.5|?008|2060|
|North Africa|124|368|427|<br>435|3.5|';.6|2016|2050|
|America|667|<br>1,146|1,192|<br>1.201|1.8|1.4|(c)|2030|
|Latin America and Caribbean|399|814|864|<br>872|2.2|1.6|2043|2030|
|Northern America|268|332|328|<br>329|1.2|1.2|cc)|ZW3O|
|Asia|2,823|5,728|6,280|6,374|2.3|1.5|2047|2045|
|East and Southeast Asia|1,650|2,719|2,822|2.842|1.7|1.4|Cc)|7030|
|South Asia|1,058|2,614|2,977|3,034|2.9|1.5|2025|2040|
|Southwest Asia|115|396|490|<br>498|4.3|1.6|2013|2055|
|Europe and U.S.S.R.|771|<br>843|842|<br>846|1.1|1.1|tc|2030|
|Oceania|25|<br>42|44|<br>45|1.8|1.3|(c)|2030|
|Income group (b)|||||||||
|Low|2,711|6,343|7,315|<br>7,458|2.8|1.5|2030|2060|
|Lower-middle|580|1,416|1,609|<br>1,632|2.8|1.6|2026|2055|
|Upper-middle|422|891|989|<br>999|2.4|1.5|2040|2050|
|High|767|s,og|869|<br>871|1.1|1.2|Cc)|2030|
|Nonreporting nonmerber|365|515|<br>548|<br>554|1.5|1.2|Cc)|2030|

a. "More developed" cooprises Europe, U.S.S.R., Northern America CUnited States and Canada), Australia, New Zealand,'and Japan. "Less developed" comprises the rest of the world.

b. Criteria for incom groups are given In World Bank 1989. See ltstirr In Exhibit 1.

c. Population will not double with projected rates.
constructed comparison group by testing whether the distribution of the covariates between the two groups given the predicted propensity score. Table A2 presents the result of the balancing test and suggests the balancing property is satisfied.

# 5 Estimation Results

We begin our analysis at the household level by comparing female and male employment among refugees and host communities. Estimation results from Equation 1 are reported as average (marginal) effects for female and male-headed sub-samples by displacement status. The average effects show the change in the probability of being in employment which is associated with a one-unit change in the explanatory variables; for indicator variables it is the difference with the reference category.

The results confirm significant gender differences in both the refugee and host community populations. Specifically:

- Adult refugees living in female headed households are significantly - 7.5 percentage points - less likely to participate in the labor market compared to their male headed counterparts:

- Adults from host communities living in female headed households are also less likely to participate in the labor force – the gender gap is 3.1 percentage points.

There are also important differences between refugees and host communities: in particular, education appears to have a stronger effect on the probability of being in employment among refugees, male and female. This is true for primary, secondary, and tertiary education. In contrast, we do not find strong significant effect of education on the probability of being in employment among people in host communities, except for tertiary education. This points to the importance of expanding access to education to enable refugees to gain technical and vocational skills. Education can thus play a significant role in increasing refugee women’s participation in the labor market by providing them with the skills needed for the labor market (as Table 2 shows women with secondary or tertiary education are more likely to be employed).

Access to land also significantly increases the probability of female-headed households’ participation in employment – by 16 percentage points among host communities.

_Table 2: Participation in employment: probit model_

||All|Refugees<br>Female|Male|All|Hosts<br>Female|Male|
|---|---|---|---|---|---|---|
|||HHH|HHH||HHH|HHH|
|Female head|-0.075<sup>***</sup>|||-0.031<sup>*</sup>|||
||(0.019)|||(0.017)|||
|Age of household head|0.001|-0.001|0.001|0.000|-0.001|0.001|

15
the cumulative percentage of a characteristic on the vertical axis. When the concentration curve lies above the 45 degree line this means that the characteristic is concentrated among the poor—with larger deviations indicating higher concentration among the poor. The left panel of Figure 1 shows the concentration curves for disability among youth ages 6 to 17 for the 4 surveys with an extensive definition of disability and more than 50 children identified as having a disability. The right panel of Figure 1 shows the deviation of the concentration curve from the 45 degree line—a transformation that sharpens the distinction between the lines. In this set of countries, disability is concentrated among the poor in Indonesia and Romania. It is concentrated among the wealthy in Mozambique. In Cambodia 1999 it is evenly spread across the economic distribution.

**<u>Figure 1. The distribution of disability across economic quintiles among youth 6 to 17</u>**

<!-- Start of picture text --> Concentration curves Deviation of concentration curves from the 45 degree line<br>100 20<br>15<br>80<br>10<br>45 degree line Cambodia 1999#<br>60 Cambodia 1999#Indonesia 2003# 5 Indonesia 2003#<br>Mozambique 1996#<br>Mozambique 1996# 0 Romania 1995#<br>Romania 1995#<br>40 -5<br>-10<br>20<br>-15<br>0 -20<br>0 20 40 60 80 100 0 20 40 60 80 100<br><!-- End of picture text --> The Concentration Index (CI) is a summary measure of the entire distribution of an indicator by the welfare ranking—in this context it is therefore a summary statistic for inequality in disability by economic status. Intuitively, the CI is defined as twice the area between the concentration curve and the 45 degree line, with area below the 45 degree line counted as positive and area above the 45 degree line as negative. Note that while Figure 1 is drawn in terms of quintiles, the CI is derived on the basis of the full (continuous) distribution of the welfare ranking variable. In all but 3 of the surveys, the CI is negative indicating a concentration of people with a disability among the poor (Table 4). In Indonesia and Romania this negative value is statistically significantly different from zero; in Mozambique the positive value is statistically significantly different from zero.<sup>11</sup> It is hard to determine whether these numbers are “high” or “low”: there is no “expected” degree of concentration of disability among the poor. A comparison to a different outcome—child mortality—provides a sense of the order of magnitudes.

> 11 Standard errors are obtained through bootstrapping the calculation of the concentration index 1,000 times for each survey. The standard deviation of the estimate of the CI across those replications is reported here as the standard error of the CI.

8
April 2012 and January 2013.<sup>25</sup> Third, an endline survey was conducted in all schools from January 2015 to March 2015,<sup>26</sup> and in households between January and June 2017.<sup>2728</sup>

**<u>Figure 4</u>** <u>summarizes the experiment sampling frame and timeline.</u>

In both the baseline and endline, the school surveys collected detailed data on school-level variables such as pupil enrollment and attendance, school infrastructure, facilities and resources, the school SIP, and SMC functioning and membership. The questionnaire also collected data on the total number of teachers employed in school and a count of those present in school on the day of the visit. The visits were held on a day when schools were supposed to be open but were not announced in advance. Information on enrollments, teacher and student presence, and SMC members was collected from school records in interviews with the head teachers. In addition, for the endline round of school surveys, a random sample of 20 students from Grade 3 and 4 from each school were interviewed and given standardized tests for Math, English and Sindhi.

In addition, household surveys for both baseline and endline gathered information on household demographic and socioeconomic characteristics, household schooling choices and engagement

> 25 The initial plan was to collect all baseline data in 2012. However, due to heavy flooding in Sindh province, the data were collected in two rounds from April to June 2012, and November-December 2012. The flooding delayed implementation of the pilot activities but did not significantly disrupt data collection beyond this delay. We control for a flood dummy in all regression estimates.

> 26 The endline survey exercise included two phases: surprise visits to schools in January 2015, and announced visits made to schools in the sampled villages between January 2015 and March 2015.

27 Following analysis of the school endline survey data, the endline household survey was conducted for a sub-sample of 160 randomly selected baseline villages. Power calculations and budget limitations motivated the decision to reduce the endline household sample. Initial visits were conducted at the household level in January 2017 to obtain tracking data on household members and children, with the full survey conducted from March to June 2017.

28 The World Bank worked with local survey firms: Research Consultants (RCons) and Weitek Group on various rounds of data collection. The baseline and endline surveys were conducted by Weitek and RCons, respectively, to mitigate any risks associated with biased reporting in interviews by the same firm for both rounds.

28
Maitra, P. and R. Ray (2006). "Household expenditure patterns and resource pooling: evidence of changes in post-apartheid South Africa." Review of Economics of the Household **4** (4): 325-347.

McElroy, M. B. and M. J. Horney (1990). "NASH-BARGAINED HOUSEHOLD DECISIONS: REPLY." International Economic Review **31** (1): 237.

Schultz, T. P. (1990). "Testing the Neoclassical Model of Family Labor Supply and Fertility." Journal of Human Resources **25** (4): 599-634.

Thomas, D. (1990). "Intra-Household Resource Allocation An Inferential Approach." Journal of Human Resources **25** (4): 635-664.

Thomas, D. (1994). "Like Father, Like Son; Like Mother, Like Daughter." Journal of Human Resources **29** (4): 950-988.

19
||Cata<br>Set<br>Variables<br>and<br>Sources|
|---|---|
|AFRITA|Dummy<br>for<br>Subsaharan Africa|
|AREA|Land area<br>(200s<br>sq KM)<br>S:WBSI|
|BMP|Black Market<br>Exchange<br>Rate<br>Premium<br><br><br><br>|
||S:Picks Currency Yearbook,<br>Wood<br>(38),<br>WB updates|
|BMS|Standard<br>Dev.<br>BMP|
|ISD<br>(Lh)|Central Government Gross Capital Formation<br>S:IMFGFS|
|CIVL|Index<br>of Civil<br>Liberties<br>S:Gastil,<br>Barro|
|cTX<br>(Ll)|Ratio Central Govt Corporate<br>Income Tax Revenue<br>to GDP<br>S:IMFGFS|
|DEE<br>(L)|Ratio Central Govt Defence Expenditure to GDP<br>S: IMFGFS|
|DEF<br>(L)|Ratio Central Govt<br>Deficit<br>to GDP<br>S: IMFGFS|
|RERD|Real Exchange Rate Distortion<br>S:Dollar<br>(90),<br>Calculated with<br>SH|
|RERDB|RERD<br>for<br>SH benchmark countries|
|EDE<br>(L)|Ratio Government Educational Expenditures to GDP<br>S:IMFGFS|
|SBC|Growth<br>rate<br>of Domestic<br>Credit<br>S:IMFIFS|
|SGCFD|Government Capital formation deflated with SH prices<br>S:Barro<br>fromIMFGFS,<br>Summers-Heston<br>(88)<br>[SH]|
|GG|<br><br><br><br>Growth of government consumption expenditures<br>S:WBNA|
|GM|Growth of<br>imports<br>S:WBNA|
|SOV|Government Consumption<br>share<br>of GDP<br>S:WBNA|
|SOVYK<br>(L)|Government Consumption<br>less Defense<br>and Education<br>share<br>of<br>GDP|
||GOV<br>-<br>DIE<br>-<br>EDE|
|SPO|Growth of Population<br>S:WBSI|
|SR|Growth of<br>real per capita GDP<br>S:SH|
|SSG|Growth<br>of<br>the<br>Share<br>of Government<br>S:Growth of Share of Government Consumption|
|SX|Growth of Exports<br>S:WBNA|
|SYP|Growth of per capita GDP<br>S: WENA|
|HSGVX|Share real Government Consumption expenditures minus defense<br>education expenditures<br>S:Barro<br>fromSH,IMFGFS|
|INV|<br>Investment<br>share<br>of GDP<br>S:WBNA|
|ITX<br>(L)|Share of central govt individual income tax to GDP<br>|
||S: IMFGFS|
|LAAM|Dummyvariable<br>forLatinAmerica|

32
**Table E3: Descriptive Statistics of Key Variables (Bangladesh)**

|**Variable**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|
|_Z-scores_|||||
|HAZ|-1.5|1.1|-6|5.73|
|WAZ|-1.68|1.03|-5.44|3.75|
|_Food Security measures_|||||
|HFIAS Score|7.45|6.39|0|26|
|Child DDS|3.8|1.98|0|8|
|FCS|64.59|226.61|0|112|
|MDDS|4.02|1.28|1|9|
|Age in months|28.4|16.76|0|59.9|
|Age squared|1087.39|1010.76|0|3588.01|
|Child is female|0.48|0.5|0|1|
|HH size|5.38|2.16|2|27|
|Share of kids under 5|0.26|0.11|0.06|0.67|
|_HH Head Characteristics_|||||
|Female-headed HH|0.11|0.31|0|1|
|_HH Head’s Education_|||||
|No education|0.44|0.5|0|1|
|Literate, or < 5 yrs of schooling|0.14|0.35|0|1|
|Completed [5-7] yrs of schooling|0.19|0.39|0|1|
|Completed [8-10] yrs of schooling|0.17|0.37|0|1|
|Completed 11 + yrs of schooling|0.05|0.23|0|1|
|_Mother’s Education_|||||
|No education|0.27|0.44|0|1|
|Literate, or < 5 yrs of schooling|0.16|0.37|0|1|
|Completed [5-7] yrs of schooling|0.28|0.45|0|1|
|Completed [8-10] yrs of schooling|0.25|0.43|0|1|
|Completed 11 + yrs of schooling|0.03|0.18|0|1|
|_Water & Sanitation_|||||
|Improved water|0.94|0.24|0|1|
|Improved sanitation|0.38|0.49|0|1|
|Asset index 1 (poorest)|0.23|0.42|0|1|
|Asset index 2|0.19|0.39|0|1|
|Asset index 3|0.2|0.4|0|1|
|Asset index 4|0.19|0.39|0|1|
|Asset index 5|0.18|0.39|0|1|
|_Region_|||||

44
|P114204|2010|2017|12/20/2016|12 Closed|Brazil|
|---|---|---|---|---|---|
|P114204|2010|2017|5/16/2017|13 Closed|Brazil|
|P114236|2010|2010|6/25/2010|1 Closed|Burkina Faso|
|P114236|2010|2011|3/23/2011|2 Closed|Burkina Faso|
|P114236|2010|2012|12/12/2011|3 Closed|Burkina Faso|
|P114236|2010|2013|7/4/2012|4 Closed|Burkina Faso|
|P114236|2010|2013|4/22/2013|5 Closed|Burkina Faso|
|P114236|2010|2014|12/1/2013|6 Closed|Burkina Faso|
|P114236|2010|2015|7/2/2014|7 Closed|Burkina Faso|
|P114236|2010|2015|1/12/2015|8 Closed|Burkina Faso|
|P114236|2010|2015|6/6/2015|9 Closed|Burkina Faso|
|P114236|2010|2016|12/18/2015|10 Closed|Burkina Faso|
|P114236|2010|2016|6/22/2016|11 Closed|Burkina Faso|
|P114236|2010|2017|12/29/2016|12 Closed|Burkina Faso|
|P114236|2010|2017|6/25/2017|13 Closed|Burkina Faso|
|P114236|2010|2018|11/28/2017|14 Closed|Burkina Faso|
|P114236|2010|2018|6/29/2018|15 Closed|Burkina Faso|
|P114236|2010|2019|11/27/2018|16 Closed|Burkina Faso|
|P114236|2010|2019|5/22/2019|17 Closed|Burkina Faso|
|P114236|2010|2020|12/5/2019|18 Closed|Burkina Faso|
|P114240|2011|2011|3/25/2011|1 Closed|Gambia,The|
|P114240|2011|2012|9/17/2011|2 Closed|Gambia,The|
|P114240|2011|2013|7/7/2012|3 Closed|Gambia,The|
|P114240|2011|2013|10/25/2012|4 Closed|Gambia,The|
|P114240|2011|2013|5/23/2013|5 Closed|Gambia,The|
|P114240|2011|2014|12/7/2013|6 Closed|Gambia,The|
|P114240|2011|2014|6/16/2014|7 Closed|Gambia,The|
|P114240|2011|2015|12/23/2014|8 Closed|Gambia,The|
|P114240|2011|2015|6/29/2015|9 Closed|Gambia,The|
|P114240|2011|2016|12/10/2015|10 Closed|Gambia,The|
|P114253|2010|2010|4/10/2010|1 Closed|Yemen,Republic of|
|P114253|2010|2011|1/12/2011|2 Closed|Yemen,Republic of|
|P114253|2010|2012|10/22/2011|3 Closed|Yemen,Republic of|
|P114253|2010|2012|3/21/2012|4 Closed|Yemen,Republic of|
|P114253|2010|2013|10/3/2012|5 Closed|Yemen,Republic of|
|P114253|2010|2013|11/25/2012|6 Closed|Yemen,Republic of|
|P114253|2010|2013|6/1/2013|7 Closed|Yemen,Republic of|
|P114253|2010|2014|8/28/2013|8 Closed|Yemen,Republic of|
|P114253|2010|2020|4/8/2020|9 Closed|Yemen,Republic of|
|P114264|2012|2012||0 Active|Ghana|
|P114264|2012|2013|11/17/2012|1 Active|Ghana|
|P114264|2012|2013|6/17/2013|2 Active|Ghana|
|P114264|2012|2014|4/6/2014|3 Active|Ghana|
|P114264|2012|2015|9/16/2014|4 Active|Ghana|
|P114264|2012|2015|6/9/2015|5 Active|Ghana|
|P114264|2012|2016|1/11/2016|6 Active|Ghana|
|P114264|2012|2016|4/14/2016|7 Active|Ghana|
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