rafmacalaba/datause-encoder-data huggingface dataset:
# **2.2 Data**

Follow-up data were collected from November 2010 to January 2011 via a household questionnaire as well as an individual questionnaire administered to recent (two week recall) fever cases. The household-level questionnaire recorded demographic, socioeconomic and health characteristics, health seeking behavior, knowledge on malaria, and the utilization of bed nets. The individual fever questionnaire collected information on treatment-seeking behavior from recent fever cases. In each village a full enumeration was first conducted and all households with a reported recent fever case were noted. Ten households were randomly selected from each enumeration list and interviewed for general household information and net usage in each village. An additional ten households were randomly chosen from the complete list of fever cases in the villages for the individual questionnaire.

Little baseline information was available at the time of the village-level randomized assignment into treatment arms, so researchers selected the 30 most endemic villages in each of the four study sub-districts and randomly assigned them to arms A, B, or K. With the collected endline survey data we can investigate the balance of characteristics that may be relevant for intervention performance but are unlikely to be affected by the intervention activities themselves. These characteristics include various demographic and socioeconomic measures of the household, as well as basic characteristics of the ASHA worker.<sup>3</sup> Appendix Table 1 presents these mean characteristics for the population representative sample and Appendix Table 2 for the sample of households with recent fever. Each of the tables in turn present results separately by district since this study focuses on differences in performance across districts and thus needs to investigate any difference in sample balance within the two districts that may confound cross-district comparisons of intervention effectiveness.

The village randomization resulted in a largely balanced sample. Besides the mean characteristics in each study arm (A, B, and K), the tables also report the standardized mean difference in characteristics for each pairwise comparison and the p-value of the standard t-test of difference. Very few of the standardized mean-differences exceed the 25 percent threshold discussed in Imbens and Wooldridge [2009] or are significantly different as suggested by traditional t-tests of equality. In

> 3We note that no reassignment of ASHA workers occurred during the study period.

7
|P124848|2013|2018|6/20/2018|11 Closed|China|
|---|---|---|---|---|---|
|P124848|2013|2019|12/16/2018|12 Closed|China|
|P124848|2013|2021|7/10/2020|13 Closed|China|
|P124864|2011|2011||0 Closed|Uganda|
|P124864|2011|2012||0 Closed|Uganda|
|P124864|2011|2013||0 Closed|Uganda|
|P124904|2011|2011||0 Closed|Bhutan|
|P124904|2011|2012||0 Closed|Bhutan|
|P124904|2011|2013||0 Closed|Bhutan|
|P124904|2011|2020|3/9/2020|1 Closed|Bhutan|
|P124905|2012|2012||0 Active|Nigeria|
|P124905|2012|2013|3/26/2013|1 Active|Nigeria|
|P124905|2012|2014|10/23/2013|2 Active|Nigeria|
|P124905|2012|2014|4/23/2014|3 Active|Nigeria|
|P124905|2012|2015|11/3/2014|4 Active|Nigeria|
|P124905|2012|2015|6/4/2015|5 Active|Nigeria|
|P124905|2012|2016|12/7/2015|6 Active|Nigeria|
|P124905|2012|2016|6/17/2016|7 Active|Nigeria|
|P124905|2012|2017|12/26/2016|8 Active|Nigeria|
|P124905|2012|2017|6/25/2017|9 Active|Nigeria|
|P124905|2012|2018|12/26/2017|10 Active|Nigeria|
|P124905|2012|2018|4/27/2018|11 Active|Nigeria|
|P124905|2012|2019|11/8/2018|12 Active|Nigeria|
|P124905|2012|2019|5/13/2019|13 Active|Nigeria|
|P124905|2012|2020|12/26/2019|14 Active|Nigeria|
|P124905|2012|2020|6/29/2020|15 Active|Nigeria|
|P124939|2011|2011|3/1/2011|1 Closed|St. Vincent and the Gr|
|P124939|2011|2012|12/19/2011|2 Closed|St. Vincent and the Gr|
|P124939|2011|2012|6/25/2012<br>|3 Closed|St. Vincent and the Gr|
|P124939|2011|2013|1/26/2013|4 Closed|St. Vincent and the Gr|
|P124939|2011|2013|4/6/2013|5 Closed|St. Vincent and the Gr|
|P124939|2011|2014|11/3/2013|6 Closed|St. Vincent and the Gr|
|P124939|2011|2014|12/18/2013|7 Closed|St. Vincent and the Gr|
|P124940|2011|2012|2/15/2012|1 Closed|Egypt,Arab Republic o|
|P124940|2011|2012|5/10/2012|2 Closed|Egypt,Arab Republic o|
|P124940|2011|2014|7/9/2013|3 Closed|Egypt,Arab Republic o|
|P124940|2011|2015|10/1/2014|4 Closed|Egypt,Arab Republic o|
|P124942|2014|2014|5/27/2014|1 Closed|Vietnam|
|P124942|2014|2015|11/23/2014|2 Closed|Vietnam|
|P124942|2014|2015|6/12/2015|3 Closed|Vietnam|
|P124942|2014|2016|12/16/2015|4 Closed|Vietnam|
|P124942|2014|2016|5/16/2016|5 Closed|Vietnam|
|P124942|2014|2016|6/20/2016|6 Closed|Vietnam|
|P124942|2014|2017|12/20/2016|7 Closed|Vietnam|
|P124942|2014|2017|5/31/2017|8 Closed|Vietnam|
|P124942|2014|2018|12/7/2017|9 Closed|Vietnam|
|P124942|2014|2018|6/13/2018|10 Closed|Vietnam|
**Table A11. Survey Methods’ Heterogeneous Effects by Respondent’s Sex and Age, Excluding Controls**

||(1)<br>D = F<br>|(2)<br>emale<br>|(3)<br>D = Y<br>|(4)<br>outh<br>|
|---|---|---|---|---|
||Employed|Working|Employed|Working|
|LOA|-0.000|-0.008|0.023|0.033|
||(0.031)|(0.027)|(0.027)|(0.025)|
||[0.983]|[0.774]|[0.380]|[0.165]|
|ESR|-0.020|-0.020|-0.033|-0.042|
||(0.034)|(0.029)|(0.028)|(0.026)|
||[0.561]|[0.477]|[0.252]|[0.111]|
|LOA x D|0.064|0.084**|0.053|0.028|
||(0.044)|(0.042)|(0.054)|(0.054)|
||[0.185]|[0.055]|[0.314]|[0.593]|
|ESR x D|0.033|0.016|0.095*|0.089*|
||(0.047)|(0.044)|(0.054)|(0.054)|
||[0.473]|[0.694]|[0.094]|[0.128]|
|LOA + LOA x D|0.063*|0.075**|0.076|0.061|
||(0.033)|(0.032)|(0.046)|(0.047)|
||[0.065]|[0.022]|[0.104]|[0.193]|
|ESR + ESR x D|0.013|-0.005|0.062|0.047|
||(0.034)|(0.034)|(0.048)|(0.048)|
||[0.707]|[0.884]|[0.214]|[0.347]|
|Observations|2,480|2,480|2,480|2,480|
|Outcome(Control Group)Mean|0.571|0.670|0.571|0.670|

_Notes:_ This table presents the treatment heterogeneity of including the LOA module (LOA) or enforcing self-reporting (ESR) by sex and age for the reporting of employment or work status. We present the ITT estimates of 1 - 4 based on the following equation: 0 1 2 3 + 4 . We also include estimations of the total effect of each survey method (iihss hss hss iihss1 + 3 for the LOA module, and iihss ββ iihss ββ ss iihss 2 4 for proxy-reporting) at the end of each table. The indicator YY = αα + ββ TT1 + ββ TT2 + ββ TT1 × DD takes ββ TT2 × DD + ƟDD + bb + εε ββ the value of 1 if the respondent is a woman (Columns [1] and [2]) and if the respondent is a youth between iihss ββ ββ + ββ DD the ages of 15 to 24 years (Columns [3] and [4]). All estimations include strata fixed effects. Each column is a separate dependent variable as defined in Section 3. The control mean refers to the mean of the control group for each outcome. Standard errors clustered at the household level (randomization level) are presented in parentheses, and the randomization inference _p_ -values are shown in brackets. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1 48
**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (-50% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|6.634|0.364|0.0%|
|LN full-time employees|0.089|0.036|1.3%|
|LN of q55 after imputed values were introduced (imputed "no|0.196|0.027|0.0%|
|answer")||||
|Share of activities completely outsourced|-0.447|0.131|0.1%|
|Share of activities partially outsourced|-0.036|0.127|78.0%|
|Guateng provincial dummy|0.186|0.076|1.5%|
|Western Cape provincial dummy|0.117|0.099|23.4%|
|sole proprietor dummy|-0.568|0.116|0.0%|
|close corporation dummy|-0.256|0.088|0.4%|
|Manufacturing|-0.165|0.105|11.8%|
|VAT|0.255|0.121|3.5%|
|PAYE|0.209|0.102|4.1%|
|Adj. R2|0.415|||
|Mean of the dependent variable|9.591|||

# **<u>Sensitivity analysis: Polarized model</u>**

**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (+50% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|6.861|0.379|0.0%|
|LN full-time employees|0.172|0.039|0.0%|
|LN of q55 after imputed values were introduced (imputed "no|0.141|0.030|0.0%|
|answer")||||
|dummy for full in-house (0) and full outsourcing|-0.571|0.106|0.0%|
|Guateng provincial dummy|0.361|0.087|0.0%|
|Manufacturing|-0.238|0.119|4.6%|
|PAYE|0.544|0.140|0.0%|
|Adj. R2|0.320|||
|Mean of the dependent variable|9.749|||

**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (+40% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|6.792|0.378|0.0%|
|LN full-time employees|0.172|0.039|0.0%|
|LN of q55 after imputed values were introduced (imputed "no<br>answer")|0.142|0.030|0.0%|
|dummy for full in-house (0) and full outsourcing|-0.506|0.106|0.0%|
|Guateng provincial dummy|0.362|0.087|0.0%|
|Manufacturing|-0.238|0.119|4.6%|
|PAYE|0.543|0.140|0.0%|
|Adj. R2|0.311|||
|Mean of the dependent variable|9.693|||

45
a necessary condition to attain macroeconomic adjustment. Although often complicated by labor market regulations, the labor allocation between tradable and non-tradable industries is the fundamental ingredient to achieve structural adjustment. The presence of formal mechanisms of wage indexation has been usually mentioned as a deterrent of the wage flexibility needed for structural adjustment. In particular, the dynamic role of unions and minimum wages in pushing the entire wage structure ( and inflation) has also been pointed out as a crucial mechanism for creating wage rigidity in many countries. It is in this context that we examine whether the ERP was helped or hindered by the labor market and whether the reliance on market forces to reallocate and absorb labor was indeed justifiable.

This labor market study uses the Ghana Living Standards Survey (GLSS) data which provided sufficient information to monitor the trend in employment patterns, unemployment, migration, earnings, and the demand for labor. The study uses all three rounds of the GLSS data sets. The available data relate to the periods from September 1987-August 1988 (GLSS 1), October 1988-September 1989 (GLSS 2), and September 1991-September 1992 (GLSS 3)3.

This paper provides an overview of characteristics of labor markets in Ghana. Section 1 deals with the Supply of labor, labor force participation and its determinants. Section 2 is concerned with the absorption of labor- the sectoral pattern of employment, characteristics of the wage and non-wage workers, their sources of income- wages and earnings, characteristics of child labor, turnover rates and trends in earnings and employment. Section 3 deals with unemployment rates and its distribution by region. Section 4 deals with migration and the characteristics of migrant workers, while Section 5 deals with literacy rates, drop outs, characteristics of university graduates, returns to schooling and private and social returns to education. Section 6 concludes with a summary of major findings and implications.

> 3 One of the main issue with these three rounds of survey is the that of comparability, since the format of the questionnaire has changed slightly in GLSS 3, rnaking some of the modules incompatible with the earlier formats of GLSS l& GLSS 2.

7
have an important role to play in advancing the prevention of integrity crimes. Approaches, reforms, and enforcement capacity relating to improper conduct such as improprieties surrounding tendering processes (particularly public sector contracts), bribery, and financial sector integrity (Financial Action Task Force (on Money Laundering) (FATF) compliance), for example, may best be prioritized via objective observation of costs and benefits.

# **_Engaging the economics of access to justice_**

Like policy settings relating to FATF, a CBT may also be employed to identify broader justice sector policy cost-benefit implications. Evidence regarding human decision making in developing country contexts is scarce (World Bank, 2017). The World Bank observes three principals informing understanding of human cognition in these contexts. Firstly, that most judgments and choices are made automatically, not deliberatively. Second, people often “think socially – they act and think dependent on what others around them do and think. Third, individuals share a common perspective on making sense of the world based on persons in the society around them” (World Bank, 2017). Banerjee and Duflo (2011) identify the constrained capacity of the poor to take a longer view of their lives because of the extra mental energy used just to address the most basic of needs, like shelter, within a heirachy of needs (McLeod, 2007). Looking beyond a heirachy of needs and relying on randomized control trials in 18 countries, Banerjee and Duflo note that the constrained mental energy of the poor limits their ability to even envisage a future. Failure in criminal or civil justice processes exaggerates exclusion by increasing obstacles to low-level needs like shelter. The opportunity cost for disproportionately time-poor households of pursuing justice outcomes exaggerates barriers to accessing justice processes and just outcomes (Vickery, 1977). Inability to access justice processes also exaggerates other inequalities that render the poor time-poor. The wealthy enjoy chlorinated water, regular and directly bank-deposited salaries often with automatically deducted pension and health care contributions. The poor expend time and scarce resources to organize and engage justice processes to obtain these items (Banerjee and Duflo, 2011). A CBT needs to understand and respond to the logic of how poor people cope individually and collectively in specific contexts. Existing measures of access to justice (e.g. the World Justice Project Rule of Law Index) (The World Justice Project, 2019) alongside data collected by governments and development actors, may be incorporated into a CBT. Using CBA tools that incorporate heterogenous outcomes is important in order to determine how resources directed to eliminate barriers to justice can be efficiently and effectively allocated.

7
parliament, seemed to call for a decentralization program with heavy emphasis on local political control and expenditure autonomy.

Mexico’s decentralization was also a reaction to a longstanding perception of too much central control. In 2004, a National Finance Convention was convened with the objective of developing a national consensus on the direction of future decentralization reforms in Mexico.<sup>3</sup> The impetus for the new drive for decentralization in Mexico has enhanced political competition and demands for greater political decentralization and administrative efficiency. The current policy design of fiscal federalism emphasizes expenditure autonomy and local political control and control over the civil service.

Tanzania’s decentralization is being driven more by dissatisfaction with the quality of local public services, which has been attributed to too little expenditure discretion at the local government level. India is likewise attempting to address a problem of too little discretion at the local government level, as well as a problem of fiscal indiscipline at the state government level.

# _3.3. Who Should Participate in the National Discussion?_

A national debate about the merits of fiscal decentralization ideally would involve all relevant interest groups, i.e., all of those who see potential benefits or costs from a move toward fiscal decentralization. The discussion will be led by the central government, which ultimately will make the rules of any new decentralization. But the discussion must be inclusive. A basic idea behind decentralization is to empower those at the state and local government level by making them part of the decision-making process. It would be self-defeating to leave them out of the debate. The flip side of this is that much of the central bureaucracy will lose power with decentralization. It would be equally foolish to leave them out of the debate. The goal is to develop as much of a consensus as is possible and to forestall later criticism that the process was not transparent.

But there are important tradeoffs involved. The broader the audience in the national debate, the harder it will be to gain consensus for a program. The result may be a prolonged discussion that finally dies of inertia, or a watered-down program that may not address the underlying problems.

The national debate may be spontaneous, as it was in Indonesia, and all parties can have voice. In other cases, the national discussion is more formal and orchestrated from the top, as in the cases of Tanzania, China, Bulgaria or Vietnam. It is not unusual for the national discussion about decentralization to be the responsibility of a commission or even a Ministry. In such cases, the discussion is led by the higher level government (central or state), as was the case in South Africa. It is a mistake, however, to leave the

> 3 See the discussion in Webb and Gonzalez (2003) on the difficulties and opportunities Mexico faces in reaching a national consensus for future reforms.

8
18 percent) and medium firms (5 percent), while large and mega large firms account for only 3 percent together of bank clients.

**Figure 14:  Evolution of Bank Loans and Clientele by Size of Borrowing Firms (1994-2003)**

<!-- Start of picture text --> Bank loans Vs corporate sector evolution Evolution of the total number<br>of bank corporate debtors and loans<br>N umber o f Volume of loans Number of borrow ers<br>B ank lo ans (P eso s) co mpanies Avg growth<br>35 700 2001-03<br>10%<br>30 600 8%<br>25 500 8%<br>20 400 6% 5%<br>15 300<br>4%<br>10 200<br>5 100 2% 1%<br>- - 0%<br>Mega Large Medium Small Micro<br>-2% -2% -1%-2% -2% -2%-1%<br>Bank loans Total number of companies -4% -3%<br>Source: SBIF<br>199419951996199719981999200020012002e09/2003e<br>Millions<br>Thousands<br><!-- End of picture text --> In addition, the financial structure of companies of different sizes suggests that bank financing goes in the largest proportions to the largest firms. The proportion of bank loans in companies’ long-term financial structure decreases directly with their size, from 15% for mega large companies to only 1% for micro companies; the deepest drop occur at the level of micro firms. As a result, smaller firms rely nearly exclusively on own resources for their long-term financing needs (97% for micro firms, compared to 65% for larger firms). For short-term financing needs, small and medium firms seem to get access to the same proportion of bank loans than larger firms (about 15 to 20%), but micro firms not (4%). Micro firms also seem to have less access to supplier credit than larger firms (31% compared to 40% for mega firms). Micro firms seem to rely on intra-company loans (19%), probably from the large conglomerates some of them belong to, and on other short-term financing sources (46% in total) such as factoring. Compared to US firms, only mega large Chilean firms seem to have access to a somewhat similar (although slightly lower) proportion of debt financing (bank loans and bonds).
# **Table 7:  Global Trends in Governance Indicators 1996-2007 for Selected Sources**

|||**Sample**|**Average**|**Std Dev Ac**|**ross Ctrys**|**Change 96-**||
|---|---|---|---|---|---|---|---|
||**Sample**|**1996**|**2007**|**1996**|**2007**|<br>**07***|**T-stat**|
|**Voice and Accountability**||||||||
|EIU|120|0.41|0.47|0.25|0.26|0.06|1.79|
|FRH|194|0.56|0.58|0.29|0.28|0.02|0.66|
|GCS (Press Freedom / Parliament)|94|0.57|0.46|0.15|0.16|-0.11|-4.76|
|HUM|155|0.63|0.62|0.34|0.30|-0.01|-0.33|
|PRS|140|0.63|0.67|0.25|0.25|0.04|1.28|
|WMO|182|0.54|0.58|0.27|0.25|0.04|1.53|
|RSF|137|..|0.71|..|0.22|..|..|
|**Political Stability and Absence of Vi**|**olence**|||||||
|DRI|106|0.82|0.85|0.18|0.15|0.04|1.53|
|EIU|120|0.56|0.60|0.25|0.22|0.04|1.27|
|GCS (cost of terrorism)|95|0.66|0.75|0.17|0.13|0.09|3.98|
|HUM|155|0.63|0.59|0.29|0.26|-0.04|-1.34|
|PRS *|140|0.70|0.73|0.13|0.11|0.03|1.86|
|WMO|182|0.67|0.68|0.25|0.22|0.01|0.54|
|**Government Effectiveness**||||||||
|CPIA|125|0.41|0.50|0.16|0.13|0.09|4.87|
|DRI|106|0.57|0.73|0.28|0.21|0.16|4.75|
|EIU|120|0.43|0.37|0.31|0.26|-0.06|-1.69|
|GCS (infrastructure quality)|95|0.54|0.51|0.24|0.23|-0.03|-0.86|
|PRS *|140|0.58|0.54|0.24|0.28|-0.04|-1.33|
|WMO|182|0.53|0.57|0.24|0.23|0.04|1.54|
|**Regulatory Quality**||||||||
|CPIA|125|0.50|0.55|0.14|0.14|0.05|2.83|
|DRI|106|0.82|0.86|0.16|0.13|0.04|2.21|
|EIU|120|0.54|0.56|0.22|0.22|0.02|0.73|
|GCS (burden of regulations)|95|0.30|0.38|0.13|0.13|0.08|4.44|
|HERITAGE|153|0.54|0.51|0.17|0.19|-0.03|-1.36|
|PRS|140|0.41|0.72|0.13|0.23|0.31|14.23|
|WMO|182|0.55|0.60|0.25|0.25|0.04|1.68|
|**Rule of Law**||||||||
|CPIA|125|0.40|0.42|0.17|0.15|0.02|1.23|
|DRI|106|0.71|0.81|0.20|0.17|0.10|3.86|
|EIU|120|0.49|0.53|0.27|0.24|0.04|1.30|
|GCS (organized crime / police /||||||||
|independent judiciary)|94|0.57|0.60|0.21|0.19|0.03|1.18|
|HERITAGE|153|0.57|0.46|0.23|0.24|-0.11|-4.05|
|HUM|155|0.60|0.40|0.35|0.42|-0.20|-4.54|
|PRS|140|0.72|0.63|0.23|0.22|-0.09|-3.53|
|QLM|115|0.45|0.44|0.29|0.30|-0.01|-0.29|
|WMO|182|0.57|0.59|0.24|0.22|0.02|0.82|
|**Control of Corruption**||||||||
|CPIA|125|0.38|0.42|0.16|0.16|0.04|1.95|
|DRI|106|0.58|0.67|0.26|0.27|0.09|2.55|
|EIU|120|0.35|0.38|0.33|0.31|0.02|0.51|
|GCS (bribe frequency)|95|0.64|0.59|0.19|0.19|-0.05|-1.85|
|PRS|140|0.59|0.43|0.21|0.20|-0.16|-6.61|
|QLM|115|0.39|0.37|0.29|0.29|-0.02|-0.57|
|WMO|182|0.50|0.53|0.27|0.25|0.03|0.98|
|||||Average<br># Significa|nt Increases|0.02<br>9||
|||||<br># Significa|<br>nt Decreases|5||

- _Note that changes for GCS are calculated over 2002-2007 and for WMO and CPIA over 1998-2007_

- _** Note that there are small increases in the number of countries covered between 1996 and 2007 on HER and PRS._

37
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46