rafmacalaba/datause-encoder-data huggingface dataset:
types of costs such as system design rigidity (that is, not being able to accommodate certain atypical cases) and IT system breakdowns. Once again, given the overall positive effects documented by past studies, we propose the following hypothesis: H3: E-procurement improves efficiency of public tendering administration.

# 4. DATA AND INDICATORS

# 4.1 DATA SOURCES

37. The sample for this study comprises tenders in the respective district-level PEs of BWDB, LGED and RHD, that is for a total of 185 PE clusters. To ensure homogeneity, our sample only includes public works projects, which represent the bulk of procurement spending in these organizations. In BWDB, most works projects consist of the construction of embankments, canals, riverbank protection work, dredging and flood control work. In LGED, most works projects comprise of construction and maintenance of small roads, schools, buildings etc. In RHD, most works projects consist of the construction of big district and subdistrict roads and highways.

38. Administrative data on e-procurement tenders come from the e-procurement database, exported by the government for the research team. It captures the full population of e-procurement tenders for FY 201112 to FY2017-18, totaling 191,785 tenders. A survey firm collected data from procurement records for a representative sample of paper-based tenders processed between 2011-12 to 2016-17. The sampling frame consisted of the full population of about 31,500 manual tenders. A stratified sampling method was used where the strata were defined by district and method of procurement (open tendering method and limited tendering method), and financial year. Strata with few tenders were oversampled, with the sample size of the stratum being equal to the population of that stratum if the population was not greater than 3 tenders. The physical files for about 15% of the tenders that were sampled could not be located (e.g. because flooding destroyed the records); hence, they had to be replaced from the same stratum. Some of the replacements had to be made from different strata, especially in the cases where the population of the stratum was low. These tenders were collected from the same Procuring Entity but from a different financial year. We sampled about 33% of the population resulting in a sample of 10,319 paper-based tenders. Figure 3 shows the number of observations collected for manual and e-procurement tenders by FY, and the number of firms registered in the e-GP system, reflecting the gradual uptake of e-procurement.

13
25 average length of stay for all hospitals; the number of beds; and the bed occupancy rate. Except for the number of beds which was taken from the World Bank’s _World Development Indicators_ (WDI) database, the source for hospital-level data was the World Health Organization’s _Health for All_ database.

Annual health spending per capita (total and disaggregated as public and private spending) was obtained from the WDI database and is measured in constant 2000 dollars adjusted for purchasing power parity. The _Health for All_ database was the source for the seven amenable mortality indicators investigated here: these are the standardized death rates for ischemic heart disease, diabetes, cerebrovascular diseases, female breast cancer, appendicitis, hernia and intestinal obstruction, and adverse effects of therapeutic agents.

Descriptive statistics are shown in Table 2 for the full sample—across the whole period of study and all countries—and separately according to the predominant hospital reimbursement method observed. Country-year combinations with reimbursement by PBP and, especially, FFS present higher hospital activity rates, as reflected by more inpatient admissions and generally higher numbers of diseasespecific discharges. PBP systems exhibit lower average length of stay and fewer hospital beds than either FFS or budget systems, and FFS observations present the highest bed occupancy rates. In our sample, total health expenditures are considerably higher for PBP and FFS country-year combinations than in those combinations where historical budgets were observed, with FFS associated with the highest average total spending and substantially higher public spending. Finally, budget systems exhibit higher amenable mortality rates than FFS and PBP for four of the seven causes of death, yet neither FFS nor PBP observations clearly dominate in terms of mortality
|**Table 3.13**<br>**Quintile**|**: Ranking of selecte**<br>**Municipality**|**d municipalit**|**ies, Welf**|**are index, an**|**d Schools**<br>**Rankings **||
|---|---|---|---|---|---|---|
|||**Population**<br>**2000**|**HDI**|**HDI**<br>**(1 is highest**<br>**170 lowest)**|**Total**<br>**schools****|**Schools**<br>**per capita**|
|**1**<sup>**st**</sup>**, top 5**|**João Pessoa**|**597,934**|**0.767**|**1**|**1**|**167**|
||Campina Grande|355,331|0.618|2|2|164|
||Cabedelo|42,832|0.591|3|60|168|
||Patos|91,761|0.534|4|3|159|
||Cajazeiras|54,715|0.521|5|5|149|
|**2**<sup>**nd**</sup>**, top 5**|**Jericó**|**7,416**|**0.402**|**43**|**97**|**63**|
||Queimadas|36,032|0.402|44|7|112|
||Pedras de Fogo|25,861|0.400|45|49|155|
||Frei Martinho|2,923|0.399|46|147|28|
||Pedra Branca|3,692|0.399|47|125|17|
|**3**<sup>**rd**</sup>**, top 5**|**Lagoa**|**4,844**|**0.377**|**85**|**96**|**78**|
||Lucena|9,755|0.377|86|133|146|
||Borborema|4,730|0.375|87|159|133|
||Prata|3,425|0.375|88|167|123|
||São José do Bonfim|2,838|0.375|89|170|117|
|**4**<sup>**th**</sup>**, top 5**|**Mogeiro**|**13,231**|**0.350**|**127**|**69**|**105**|
||Cuité|19,946|0.348|128|20|96|
||Montadas|3,969|0.348|129|160|113|
||Santana dos Garrotes|7,882|0.348|130|84|47|
||Alagoinha|11,833|0.346|131|100|134|
|**5**<sup>**th**</sup>**bottom 5**|**Aroeiras**|**19,520**|**0.301**|**166**|**6**|**30**|
||São João do Tigre|4,481|0.299|167|126|38|
||Salgadinho|2,823|0.298|168|151|33|
||São Miguel de Taipu|6,086|0.297|169|149|126|
|_Source:_Censu|Passagem<br>s 2000, Atlas do Desenvolvi|1,979<br>mento Humano, Ed|0.295<br>ucation Cens|170<br>us 2001.|166|43|

_Source:_ Census 2000, Atlas do Desenvolvimento Humano, Education Census 2001. * IDH municipal, calculated based on the 1991 census and the 170 municipalities that existed in Paraíba in 1991. ** Include all types of schools (crèches, literacy, primary, and secondary).

There does not appear to be a clear pattern in the distribution of expenditures as measured by the per-capita number of teachers, schools, or classrooms. For example, Salgadinho, São Miguel de Taipu, and Passagem, the municipalities with the lowest HDI, do not appear to get more resources. Spending does not appear to be definitively regressive or progressive, with the exception of a regressive spending pattern for João Pessoa, which receives more resources. Spearman rank order correlations confirm that none of the correlations between the HDI and expenditure proxies show a significant association.

# **4. Education and Poverty**

Many of the poverty-reducing assets discussed above, such as education, experience, and labor market association, are important correlates of poverty and the dynamics thereof. This section takes the analysis a step further and investigates the marginal impact of each individual attribute on the likelihood of a household falling below the indigence poverty 32
domestic banking system. The financial sector functions as a financial intermediary among various actors. It obtains assets in the form of international reserves and claims on domestic actors and issues its own liabilities in the form of money to the private sector.

These budget constraints are not independent; they represent an overall resource constraint on the economy. This resource constraint can be expressed as follows: the difference between gross national disposable income and domestic absorption is equal to the current account balance.<sup>12</sup> The linkages among these sectoral accounts imply that a deficit in any sector must be financed by savings in other sectors. For the whole economy, excess spending is possible only if financing is available from the rest of the world. The relationship between excess absorption and the current account balance shows that an improvement in the latter may be achieved through an increase in the country’s output or a reduction in absorption. Furthermore, the sum of the current account balance and net capital inflows is equal to the change in net official reserves. This identity links excess absorption (over income) to two financing sources: capital inflows to the nonbanking sector and official international reserves. This clearly shows that _the current account balance acts as a resource constraint on the whole economy_ (IMF 1987).

The basic structure of the financial programming model can be understood by focusing on the case of a small open economy with a fixed exchange rate, which is known as the Polak model (Agénor 2004a; Polak 1998; IMF 1987). In this context, the money supply is viewed as an endogenous variable that depends on changes in the balance of payments.

The core model contains four relationships. The first relationship is an _accounting identity_ based on the balance sheet of the banking system.<sup>13</sup> Accordingly, the change in the money supply (a liability for the banking system) equals the sum of the change in net official reserves and the change in net domestic credit (that is, the sum of changes in the foreign and domestic assets held by the banking system). The second relationship defines the change in the nominal demand for money as the change in the nominal income times the inverse of the income velocity of money assumed constant over time. The third relationship defines flow equilibrium in the money market. The last relationship restates the balance-of-payments identity as follows: the change in official reserves is equal to exports, minus imports, plus the change in net capital inflows to the nonbanking system.

34
and 1), we need to determine cut-off values for the classification (i.e., to generate a value of 1, which equals default and a value of 0 otherwise).

To determine the cut-off values from the continuous predictions, we follow Gabriele et al. (2017) (it expands Kaminsky (1999)). This test computes a noise-to-signal ratio to obtain critical cut-offs for when an indicator points to a default. As an example, the objective here would be to find a critical value of the estimated default probabilities that minimizes the noise-to-signal ratio, which is our objective function.

The approach of Gabriele et al. (2017) attempts to minimize the noise-to-signal ratio by varying the cut-off points of an indicator (in this case the estimated probability of default from either the logit, Lasso or random forest regressions). A simple illustration is presented in Table 1. The noise-to-signal ratio is calculated as: _NTSR_ = [ _B/_ ( _B_ + _D_ )] _/_ [1 _−_ ( _C/_ ( _A_ + _C_ ))], where the capitalized letters correspond to the blocks in the table. This test thus attempts to minimize Type 2 errors. An alternative approach defined in Gabriele et al. (2017) (which is used in this paper) is to take the weighted average of both errors, _θ CC_ + _A_<sup>+</sup> (1 _− θ_ ) _B_ + _<u>BD</u>_<sup>,definedasthepolicymaker’slossfunction(</sup><sup>_PMLF_)where</sup><sup>_θ_measuresthe</sup> preference between the two types of errors and is determined endogenously by maximizing _min_ ( _θ_ ; (1 _− θ_ )) _− PMLF_ . The parameter _θ_ is the value that minimizes average loss.

**Table 1:** Noise-to-signal ratio

||Default|No Default|
|---|---|---|
|Signal|A|B(Type II)|
|No Signal|C(Type I)|D|

The cut-offs of the Logit, Lasso and random forest regressions are summarized in Table 3. For the different cut-offs, the missed crisis and the false alarms are calculated (Appendix F). These cut-off values are then used to determine the accuracy of the test case. As an example, the accuracy of the DVA random forrest regression is equal to 72% - i.e., it correctly classified up to 72% of actual defaults. The distinction between the regressions for the different variables are not that large.

**Table 2:** Cut-offs

||Logit|Random Forest|Lasso|
|---|---|---|---|
|DVA|0.58|0.55|0.51|
|Debt|0.58|0.62|0.50|
|Spread|0.62|0.64|0.50|
|Classic|0.52|0.58|0.50|

Making a Type 2 error is not as serious in this case as making a Type 1 error. If the model predicts a crisis and no default ensues then the economic consequences are likely smaller compared to the case where a default occurs and no signal was given. Furthermore, the costs of a type 2 error (i.e., indicating a crisis when none occurs) can be a reflection of the consolidation of governments to avoid a crisis - and hence there is an endogenous anticipation response where the averted crisis due to good fiscal policy would could have resulted in a crisis without the policy intervention. This implies that type 2 errors are not really knowable when a policy action followed a signal.

24
# Appendix 3: Sampling and survey design

## Catchment areas

The project implementation unit of the Health Services Improvement Project in the Tajikistan Ministry of Health and Social Protection provided the research team with the lists of rural health centers and the villages in their catchment areas. Few RHCs were randomly excluded from the sample when the total number of RHCs was not divisible by three. The study was set up to also evaluate the impact of two additional intervention, collaborative quality improvement and citizen scorecard, that were randomly introduced at the RHC level. Therefore, having the number of RHCs in each district divisible by three was desirable. The effects of these interventions are not discussed in this paper. The exposure to these additional interventions is balance across PBF and control districts and all regressions in our analysis include controls for exposure to these interventions.

In total, this selection process resulted in a sample of 216 RHCs/ catchment areas. There were 108 chosen catchment areas in the seven PBF districts and 108 areas in the nine control districts. In each treatment group, 33 areas were in the Sughd region and 75 in Khatlon.

Six catchment areas were removed from the follow-up survey because they were either closed for renovation, closed or downgraded to health house level. The catchment areas were removed from the analysis sample.

## Health Facility Assessments

A detailed facility assessment was conducted in each selected rural health center. In catchment areas where there was at least one health house, a single health house was randomly selected for an abridged facility assessment. If the catchment area had more than one health house, all health houses had equal probability of being selected.

In each facility, interviews with health workers providing maternal and child health services or hypertension-related services were conducted. Four health workers were interviewed in each rural health center and 2 health workers in each health house.

Direct clinical observation of consultation of children under-5 and adults 40 and above were conducted in rural health centers. In the baseline survey, the target set for the survey teams was to observe 5 consultations of each type in each facility. The survey was conducted in November and 38
as a result of late payments). A more rigorous analysis of this phenomenon is not possible due to the low number of relevant survey responses who reported such experiences. Nevertheless, were possible we tried to provide few illustrations of SMMEs’ post-filing experiences in part four of this paper.

# **3.  Modeling the costs of outsourcing**

Tax compliance is a time-consuming and costly endeavor. A number of South African SMMEs complained that it suppresses their growth. The results of the Formal SMMEs Tax Compliance Cost Survey provided a rich database that allows for analysis of the incidence and cost of tax compliance in South Africa.

In order to understand what drives the cost of tax compliance we formulated a base hypothesis:

## **_<u>Hypothesis 1:  Outsourcing of tax compliance work to tax professionals saves money.</u>_**

In order to test this hypothesis in several settings we created an estimate of total compliance cost (see Annex 1 for details), indexes of outsourcing of tax compliance work (see Annex 2 for details) and a number of dummy variables.

To test the hypothesis we used the following OLS model: ln( _C_ )  ln( _Turn_ )  ln( _Emp_ )  _H_  _O part_  _O_  _D province_  _Dlegal_  _Dtaxes_  _Dactivities_  _d e_  _file_  _d full_  _e_ where:

**_ln(C)_** – ln total cost of tax compliance, ln(Turn) – ln total turnover, ln(Emp) – ln of total employment H – share of tax compliance work done in house,

Opart – share of tax compliance work that is partially outsourced, O – share of tax activities fully outsourced; Dprovince – dummy variables for provinces, Dlegal – dummy variables for legal of businesses, Dtaxes – dummy variables for taxes paid, Dactivities – dummy variables for tax compliance activities undertaken, de-file – dummy for businesses using e-filing, dfull – dummy for businesses using either only in-house (0) expertise or fully outsourcing tax compliance work.

From the beginning we have noticed that while both ln(Turnov) and ln(Empl) variables are significant and robust in the regression model, and the former has 266 missing values (out of 998 observations in the sample), while the latter has none. We tested logarithms of full-time employment, total employment and turnover as controls for firms’ size and came to the conclusion that using logarithm turnover and full-time employment as controls provides for a better model fit.

15
- 18 -

28. Even so, Radhakrishna et al. (1988), who compared three advanced agricultural districts in Uttar Pradesh with three laggards, found a higher nonfarm employment share In the agriculturally prosperous areas. PaPola (1985), comparing two different districts In the same state, found no correlation. But since he covered only a portlon of rural nonfarm activity -- and the least buoyant at that, household manufacturlng -- the lack of associatlon cannot be considered persuasive.

29. Khandker (1988) has used pooled time serles, cross-section district data to examine the relatlonships among rural employn,ent, wages, agriculture and Infrastructure. He finds both agricultural output and nonfarm employment higher In regions with higher agrocilmatic potential, but he does not attempt to measure the direct connection belween the two.

30. The labor market llnks between agricultural and rural nonfarm activity seem consistently robust In the cross-section studies. All comparlsons to date have confirmed the positive relationship between earnings in agriculture and earnings In rural nonfarm activity.<sup>1</sup> i C. Comparisons Across States 31. To further explore the elfect of agriculture on nonfarm activity, this section undertakes a descriptive analysis using state-level data. After ranking states according to agricultural productivity, It selects six for careful revlew, two high-, two low- and two medlum-productivity states. Initlally, cross-section comparisons examine how the size, compositlon and location of nonfarm activity vary across productivity zones. Then discussion turns to the time-serles evidence from these same six states to see how growth In agriculture affects growth of the nonfarm economy.

32. Table 9 ranks states according to several measures of agricultural productivity. It shows that per capita agricultural income, per capita total income, per capita 11 See Papola (1985), Chadha (1986) and Radhakrishna at al (1988).
- [23] Carla E. Humud, Robert Pirog, and Liana Rosen. Islamic state financing and u.s. policy approaches. Technical report, Congressional Research Service, 2015.

- [24] IHS Energy Intelligence, Global Exploration & Production Service. Field Reports for Iraq and Syria . Field reports, IHS Energy Intelligence, 2015.

- [25] International Energy Agency. Oil market report. Technical report, October 2014.

- [26] ISW. Isis sanctuary map, 25 May 2016.

- [27] Xin Jing, Xi Shao, Changyong Cao, Xiaodong Fu, and Lei Yan. Comparison between the suomi-npp day-night band and dmsp-ols for correlating socio-economic variables at the provincial level in china. _Remote Sensing_ , 8(1):17, 2015.

- [28] Patrick B. Johnston, Jacob N. Shapiro, Benjamin Bahney, Danielle Jung, Howard J. Shatz, Patrick K. Ryan, Jonathan Wallace, and Barbara Sude. _Foundations of the Islamic State: Management, Money, and Terror in Iraq, 2005-2010_ . RAND Corporation, Forthcoming.

- [29] John Kearns, Kit Armstrong, Les Shirvill, Emmanuel Garland, Carlos Simon, and Jennifer Monopolis. Flaring & venting in the oil & gas exploration & production industry. Technical Report 2.79/288, International Association of Oil & Gas Producers (OGP), 2000.

- [30] Katherine J. LaJeunesse Connette, Grant Connette, Asja Bernd, Paing Phyo, Kyaw Htet Aung, Ye Lin Tun, Zaw Min Thein, Ned Horning, Peter Leimgruber, and Melissa Songer. Assessment of mining extent and expansion in myanmar based on freely-available satellite imagery. _Remote Sensing_ , 8(11), 2016.

- [31] Xi Li, Huimin Xu, Xiaoling Chen, and Chang Li. Potential of npp-viirs nighttime light imagery for modeling the regional economy of china. _Remote Sensing_ , 5(6):3057, 2013.

20
# **Table 5**

# **Cooperativeness and the short route to accountability (individual-level)**

||Dummy equa<br>the cla<br>|**Dependen**<br>l to 1 if parent p<br>ssparent repres<br>|**t variable:**<br>articipated in t<br>entatives,0 oth<br>|he elections of<br>erwise<br>|
|---|---|---|---|---|
||Probit|Probit|Probit|Probit|
||(1)|(2)|(3)|(4)|
|**Invested in group account in the game**|0.09***|0.078**|0.084**|0.083**|
||(0.005)|(0.023)|(0.013)|(0.014)|
|Age||0.003|0.003|0.003|
|||(0.144)|(0.163)|(0.147)|
|Female||0.047|0.068**|0.063**|
|||(0.132)|(0.025)|(0.037)|
|Wealth index||0.054***|0.047***|0.044***|
|||(0.001)|(0.004)|(0.008)|
|Years of schooling||0.031***|0.028***|0.027***|
|||(0.000)|(0.000)|(0.000)|
|Rural location||-0.071*|-0.084**|-0.079**|
|||(0.067)|(0.033)|(0.045)|
|Distance from school (Km)||-0.002|-0.001|-0.002|
|||(0.801)|(0.885)|(0.807)|
|Member of a social organization|||0.182**|0.173**|
||||(0.021)|(0.035)|
|Participated in community activities|||0.131***|0.127***|
||||(0.001)|(0.001)|
|Thinks village members would work together|||0.049*|0.046|
||||(0.096)|(0.111)|
|Generalized trust|||-0.127***|-0.133***|
||||(0.003)|(0.002)|
|% of relatives and friends among other parents|||0.161|0.171*|
||||(0.103)|(0.083)|
|Help pupil with homework||||0.008<br>(0.813)|
|Number of meetings with head teacher||||0.015***<br>(0.001)|
|District fixed effects|YES|YES|YES|YES|
|_Joint significance of district f.e.(p-value)_|_0.000_|_0.000_|_0.000_|_0.000_|
|Geographical areas dummies|YES|YES|YES|YES|
|_Joint significance of geographical areas (p-value)_|_0.720_|_0.479_|_0.974_|_0.966_|
|Observations|1,800|1,800|1,800|1,800|

Note: Robust standard errors have been clustered at the school level. P values in parentheses: *** p<0.01, ** p<0.05, * p<0.1. We report marginal effects of continuous variables and the effect of a change from 0 to 1 for dichotomous variables. In all regressions we control for the four geographical areas dummy variables; Tirana is the excluded geographical area.

One important factor, which we left out from the specifications employed in Table 5, is the extent to which parents are informed about the elections of parent class representatives. From the survey, we know whether parents are aware of the existence of both parent class representatives and school boards. Since being unaware of the existence of parent class representatives precludes participation in their election, we 19