rafmacalaba/datause-encoder-data huggingface dataset:
|This|user guide is divided intothreemain sections:|
|---|---|
|1.|Inputting project data into INFRISK|
|II.|Using the dialog boxes to specify desired setings for the simulation|
|III.|Customizing and understanding the simulation output|

# 1. **Inputting Project Data into** **_INFRISK_**

In _INFRISK,_ the project data for the simulation is inputted both through an input sheet and the _INFRISK_ dialog boxes. The input sheet contains fundamental project data for each year of the project. The data on this sheet is divided into two sections: one for the construction period and one for the operational period.

The program offers a large degree of flexibility when the user is creating her own input sheet. Different types of inputs can be placed anywhere within the appropriate section (construction or operation) and in the order of the user's choice. However, the two sections must be separated, and each of them starts with the _heading line_ that labels the subsequent columns with the year identifiers (i.e., 1992, 1993, etc.). For the _Construction Phase_ table, the heading line must contain _YRCON_ in the second column (as shown on the example sheet below). Correspondingly, for the _Operation Phase_ table, the heading line must contain _YROPER_ in the second column. To identify the data, the user must include the appropriate label (for example, "OR" for operating revenues) in the second column as in the example below. The label in the first column is for descriptive purposes only and may be customized as desired by the user. Annex 1 provides a typology of flow and stock variables familiar to _INFRISK._ Figure 7: _INFRISK_ Input Sheet <!-- Start of picture text --> FinancongCoslsdoringC .. n.t-o-o IK USD 0 .50.6 98.e88<br>C.pilaIConsW1lIoeCos- .CK USO 109.8825 105.6825 1096825 109 825<br>R.44e98el1.nges89rCKdeorandorn1Ru00uellons Cl2oKAng, USD 21.9355 219385 211340 21.93G5<br>DBA-t2 US0 0 12 B o<br>DBA-11 USD 0 113 0 3<br>Othe -. i841 -~edft8 COlK USO 49249 49.849 41.849<br>Eq8A8 ES 9US 109.88 30.32 0 0<br>End,gEEhR.t. El US9 1 I 1<br>DBA-1 USO 0 4.21 0.157 0<br>DBA.2 USD 0 8 4.39e 0<br>6BA.3 USO 0 8 4.8S 0<br>DBA.4 US 8 0 4.C5M 8<br>DBA-S US9 0 0 5.132- 0<br>DBA-6 USD 0 0 S.133. 0<br>DBA-7 USO 0 8 9828 0<br>OBA-S USO 0 0 4.998 0<br>MEAS LUSO 0 0 197.839 0<br>00A-10 l8o U 0 819.01 250.392<br>Ph.s. 298oPh~ 1wr .I3W7 7W 1JAW .ftw wt JW<br>Filed Cap.o Pp-n,t US9 118.412 123.575 124212 124.937 125.79 126.W31 12A40S ¶2Y337<br>V.i.b,R P.... USO 49.4041 61.844 63.834 888894 688572 84831 738974 70819<br>1MM8 18 38.419 189248 188.891 184284 191.442 201.38 228 18<br>s7R-d,-P aq#i3Av? OR US9 49.404 61.844 83.834 68884 66.575 64.931 73974 76.818<br>ORR.7 g. US0 24.702 30.9.2 31.917 33.047 342875 32A255 35.587 36.4095<br>0US 14.503 1.485- 18.007 17.795 16.757 20.025 21.3=3 18.81<br><!-- End of picture text --> The program also permits the user to switch easily between different sets of projections for a project. This is done on the main dialog (shown on the next page) by selecting the name of the sheet containing the desired data in the combo box labeled "input sheet." 14
|||disregard for rule<br>of law||
|---|---|---|---|
|Accountability|Snakes and ladders|“Gotcha”|Learning and|
|culture|||improving|

Source : Shah (2005) This contrasts with the picture that can be portrayed for a typical developing country. In such a country, there is discordance in the society’s goals, authorizing environment and operational capacity. As a consequence of this disharmony, not much gets accomplished and citizens’ expectations are belied. Lack of accountability and focus of the evaluation culture leads to a systemic malaise, blunting any self-correcting mechanisms which may exist. Semi-formality imposes additional costs on doing business and does not lead to any benefits in business relations due to a lack of respect for the law. Contracts may not be honored and therefore carry little value. In view of the dysfunctional nature of the public sector in many developing countries, it is important for these to leapfrog forward (or even backwards) to a public sector culture that puts a premium on client orientation and accountability for results.

_Commitment Problem in the Public Sector: Perspectives of the New Institutional Economics (NIE)_ Finally, neo-institutional economics (NIE) presents a refreshing perspective on the lack of access to public services in developing countries. The NIE treats citizens as principals and public officials as agents. The principals (citizens) have bounded rationality – they act rationally based upon the incomplete information they have. In order to have a more informed perspective on public sector operations, they face high _transaction costs_ in acquiring and processing the information. On the other hand, agents (public officials) are better informed. This asymmetry of information allows agents to indulge in opportunistic behavior which goes unchecked due to high _transaction costs_ faced by the principals and lack or inadequacy of countervailing institutions to enforce accountable governance<sup>2</sup> .

> 2 Following this line of thought, Lambsdorff et al. note that in fighting corruption from a NIE perspective policy makers should aim to “encourage betrayal among corrupt parties, to destabilize corrupt agreements, to disallow corrupt contracts to be legally enforced, to hinder the operation of corrupt middlemen and to find clearer ways of regulating conflicts of interest.”

21
# **Figure 3 Heightened Price Volatility during Three Previous Oil Price Crashes**

<!-- Start of picture text --> 35US/bbl 1985/86 Vol8 35US/bbl Price (left) 1990/91 Vol 8<br>Price (left) Volatility (right) Volatility (right)<br>30<br>6 30 6<br>25<br>4 25 4<br>20<br>2 20 2<br>15<br>10 0 15 0<br>1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71<br>100 8<br>150 2008/09 8 2014/15<br>90<br>130<br>6<br>6<br>80<br>110<br>90 4 70 4<br>70 60<br>2 2<br>50 50<br>30 0 40 0<br>1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81<br><!-- End of picture text -->

**Notes** : Volatility is the standard deviation of the oil price (WTI) changes and is presented as a 30‐day trailing window.

<!-- Start of picture text --> 11 —<br><!-- End of picture text --> _— 11 —_
# **_4.3 The Mandatory FDC Scheme and the Market_**

Individual choices in the mandatory FDC scheme administered by the PPM have favored Sweden’s seven largest private insurance providers. Together these seven companies held 43 percent of total PPM assets in the end of September, 2007 (Table 10). The default fund (the Seventh AP-Fund) held an additional approximately 29 percent. Thus, the seven major companies and the public default fund manage about three quarters of all PPM assets.

**Table 10.  PPM assets held by the seven largest private insurance companies and the Sevent AP fund (default fund), September 28, 2007**

||Percent of total PPM||
|---|---|---|
||assets|Percent of GDP|
|_Seventh AP-fund_|_29%_|_2.7 %_|
|_The Seven Major Private Insurance_<br>_Companies_|||
|1. Swedbank Robur Försäkring|14%|1.3 %|
|2. Handelsbanken & SPP|9%|0.9 %|
|3. AMF|9%|0.8 %|
|4. Folksam|5%|0.5 %|
|5. Länsförsäkringar|3%|0.3 %|
|6. SEB|2%|0.2 %|
|7. Skandia|1%|.1 %|
|Total|43 %|4.2 %|
|Seventh AP-fund and the seven major<br>private companies|72%|6.9 %|
|Total PPM assets|100%|9.6 %|

Sources. The author’s calculations using data from the PPM.

The largest company in the market for PPM assets is Swedbank Robur Insurance, owned by Swedbank, with a 14 percent market share. Robur was one of the first companies to provide investment funds on the private market in Sweden, beginning in 1967, and has apparently been able to benefit from its early lead in this market. The second, third and fourth largest companies are SPP (part of which is now a subsidiary of Handelsbanken), AMF and Folksam, all of which have been major players historically in the market for occupational pension schemes. Undoubtedly, this gave them an initial advantage in attracting PPM clients.

Table B6 in the Appendix shows in detail how the total assets of the PPM scheme are distributed among the 50 largest funds. It is noteworthy that the top four private providers manage 29 of the largest 50 funds, ranked in terms of asset values. The next three major providers manage an additional six of the top 50 funds, which means that 35 of the largest 50 funds are managed by Sweden’s seven largest private insurance providers. The second and third largest funds in the system, _AMF Pensions Aktiefond – Sverige_ and _AMF Pensions_ 30
**(b) Impacts on sectoral outputs in 2030 (% change from the baseline)**

|Province|Free A|llowances|(Grandfathe|ring)||Aucti|oning||
|---|---|---|---|---|---|---|---|---|
||Without|linkage|With li|nkage|Witho|ut linkage|Wi|th linkage|
||Emission<br>based|Output-<br>based|Emission<br>based|Output-<br>based|Revenue<br>GOV|Revenue<br>HH|Revenue<br>GOV|Revenue<br>HH|
|AGRI|0.0|-0.1|0.0|-0.1|0.3|<br>1.0|0.1|<br>0.4|
|COAL|-28.6|-32.0|-13.3|-15.0|-35.5|<br>-35.5|-16.7|<br>-16.7|
|OILNG|-9.9|-6.3|-3.8|-2.3|-8.4|<br>-7.1|-3.4|<br>-3.1|
|MINE|-1.5|-1.7|-0.4|-0.6|-3.9|<br>-3.0|-1.5|<br>-1.1|
|FTPMF|-1.3|-0.5|-0.4|-0.2|-1.3|<br>-0.5|-0.5|<br>-0.2|
|PETRO|-9.7|-8.7|-3.3|-3.3|-10.9|<br>-10.7|-4.3|<br>-4.3|
|CHEMI|-2.1|-2.3|-0.6|-0.9|-4.2|<br>-3.6|-1.7|<br>-1.4|
|NMETA|-2.9|0.1|-1.1|0.6|-4.5|<br>-3.8|-1.5|<br>-1.2|
|METAL|-1.7|-1.3|-0.5|-0.5|-3.6|<br>-2.7|-1.4|<br>-1.0|
|OTHMF|-1.7|-0.5|-0.6|-0.2|-2.4|<br>-1.6|-1.0|<br>-0.6|
|ELECT|-4.3|-1.6|-1.5|-0.6|-6.3|<br>-5.7|-2.7|<br>-2.4|
|GAS|-17.9|-22.9|-7.7|-10.6|-23.4|<br>-22.9|-10.7|<br>-10.6|
|WATER|-1.5|-2.0|-0.5|-0.8|-2.9|<br>-2.3|-1.2|<br>-0.9|
|CONST|-1.1|-0.8|-0.3|-0.3|-2.3|<br>-1.4|-0.9|<br>-0.5|
|TRANS|-1.8|-2.0|-0.6|-0.8|-2.1|<br>-2.6|-0.8|<br>-1.0|
|SERVI|-1.2|-0.5|-0.4|-0.2|1.5|<br>-0.5|0.6|<br>-0.2|

Since different allocation rules are favorable for different provinces as they either increase the GDP or minimize the reduction if the ETS causes a GDP loss, a single criterion for emission allocation would not be preferred. A mixed allocation scheme would be desirable. There could be many possibilities for mixing the allocation rules; finding the best mix is beyond the scope of this study. It could be part of the future extension of the study.

Table 6(b) shows that sectoral outputs of each sector drop under all schemes of allocating/distributing emission allowances except the agriculture and service sector under auctioning. Auctioning and output based free allocation cause relatively higher drops of sectoral outputs of the fossil fuel industries (i.e., coal, petroleum refinery, gas processing) and emission-intensive industries (e.g., chemicals, electricity, non-metallic minerals, metals). On the other hand, auctioning and output based free allocation of allowances are favorable to less emission-intensive industries (e.g., service, agriculture).

Under the auctioning, national level GDP impact is slightly worse when auction revenues are recycled to households than when it is used by the governments for public consumption and public investment. This is because the average ratio (across provinces) of government 49
Turning to personality characteristics, we find that of the big-5 traits only two – extraversion and openness – are positively correlated with employability assessments. A one standard deviation increase in extraversion reduces the likelihood of being rated _less employable_ by 1.8pp and increases the likelihood of being rated highly employable by 1.6pp. Similarly, a one standard deviation increase in openness reduces the likelihood of being rated _less employable_ by 2.4pp and increases the likelihood of being rated highly employable by 2.2pp. It is important to highlight that personality traits were measured using an independent survey, and not by the counselors themselves.

# **6. Conclusion and Policy Implications**

Outsourcing employment intermediation and matching to the private sector offers greater flexibility and can play an important role in improving employment outcomes for vulnerable groups. This paper studied the effects of an intervention where jobseekers were randomly assigned to receive less intensive (profiling and counseling) or more intensive (profiling, counseling, and matching) employment services. The overall results from the evaluation point to significant positive effects of the matching treatment. Twenty-three percent of jobseekers in the control group who received less-intensive services found employment, and access to matching services in addition to counseling resulted in 5.8 percentage point (25.2 percent) higher likelihood of formal employment. These results were strong for harder to employ jobseekers including youth and jobseekers with low skills, were driven by specific matching, where jobseekers were directly connected with employers and benefitted from an introduction to the employer. This suggests that matching can play an important role in removing certain information asymmetries and related constraints for employers and jobseekers, and improving employment outcomes for vulnerable groups.

This paper also analyzed the incentives and behaviors of the private employment service provider. Assessment of the jobseekers’ employability by the counselor was a key determinant of services provided to them. Consistent with cream skimming behavior, those assessed to have low employment prospects were significantly less likely to be provided matching services. Interestingly, despite the lower likelihood of being allocated to matching services, treatment effects were positive (though statistically indistinguishable from 0 owing to small sample size) for _less employable_ individuals. For those rated less employable, there are characteristics unobservable to the econometrician but observed by the counselors which was used to assign jobseekers to specific matching resulting in positive effects. Further, the 16
# **Small Businesses in South Africa: Who outsources tax compliance work and why?**<sup>**1**</sup>

Jacqueline Coolidge FIAS Domagoj Ilic Consultant Gregory Kisunko The World Bank

> 1  We thank the National Treasury of South Africa and the South Africa Revenue Service for initiating this research and providing invaluable data and guidance on priority issues. Thanks to the Foreign Investment Advisory Service (FIAS, a multi-donor facility of the World Bank Group) and the Government of Switzerland for financial support. Thanks to Citizen Survey and Professor Rajen Govender for the survey work and preliminary analysis. Thanks to Sharon Smulders for guidance on tax accounting in South Africa. Thanks to Professor Joel Slemrod for guidance in the survey design. Thanks to Richard Bird, Michael Engelschalk and Stephen Knack for peer review. The opinions expressed here do not necessarily reflect those of the World Bank, its Exectuvie Directors or member countries. All errors are our own.
on overall employment and reported hours worked. If we find impact on ICT employment without any concomitant increase in labor market participation, this would suggest that the training program induced sectoral switching. During the phone interview, we also asked respondents if they were currently searching for a job and which sector they were targeting. If an increase in job search accompanied an increase in employment or sector switching, this would suggest that the training program increased participants’ mobility in the labor market.

We also investigate whether the program increased self-reported earnings, although we note that our measure of earnings is very noisy, as would be expected from two questions on earnings administered over the phone.<sup>7</sup> Finally, Project Implicit provided the scores we used for measuring gender related biases. We measured bias both explicitly (for example, the difference between self-reported rankings of women’s and men’s professionalism) and implicitly through the D-score, a within-participant standardized difference between the sorting times for the different pairings of groups (men and women) with concepts (home and office) (Greenwald et al., 2009). For the specific gender-based IATs implemented at baseline, positive D-scores indicate stronger implicit bias against women. For example, a positive D-score in the gender and professionalism IAT corresponds to a relatively longer time required to associate women with professionalism (and men with unprofessionalism) than men with professionalism (and women with unprofessionalism). We also use indicators of bias defined using ranges of D-scores provided by the Project Implicit team to indicate different levels of bias (none, moderate, strong). Scores between 0 and ±0.15 are considered to indicate little to no bias. Positive deviations greater than 0.15 are considered to indicate pro-male bias, while negative deviations less than -0.15 are considered to indicate pro-female bias.

While the program paid for a post-assessment for the entire treatment group, funding constraints did not permit a separate assessment for the control group. Thus, we will not be able to identify whether the training increased industry-recognized skills, as any changes observed in the treatment group could reflect both the impact of training and any learning that the sample might have experienced between the baseline assessment and the training, a period of just under one year and likely a time when individuals in the sample were just entering the labor force.

> 7 We asked separately about wage earnings and self-employment earnings.

11
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31
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31
Table 5: Production function estimation by sector

||||_Depende_|_nt variable:_|||
|---|---|---|---|---|---|---|
||GVA, Ag|riculture|GVA,|Industry|GVA,|Services|
||OLS|IV|OLS|IV|OLS|IV|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Grid Capacity|0.003|0.045|0.020<sup>_∗∗∗_</sup>|0.230<sup>_∗∗_</sup>|0.008<sup>_∗∗∗_</sup>|0.108<sup>_∗_</sup>|
||(0.004)|(0.108)|(0.004)|(0.106)|(0.001)|(0.063)|
|Labor|0.542<sup>_∗∗∗_</sup>|0.509<sup>_∗∗∗_</sup>|0.088<sup>_∗∗∗_</sup>|0.335<sup>_∗∗∗_</sup>|0.664<sup>_∗∗∗_</sup>|0.644<sup>_∗∗∗_</sup>|
||(0.061)|(0.079)|(0.012)|(0.045)|(0.019)|(0.035)|
|Capital|0.500<sup>_∗∗∗_</sup>|0.422<sup>_∗∗∗_</sup>|0.344<sup>_∗∗∗_</sup>|0.238<sup>_∗∗∗_</sup>|0.242<sup>_∗∗∗_</sup>|0.211<sup>_∗∗∗_</sup>|
||(0.037)|(0.055)|(0.044)|(0.016)|(0.017)|(0.024)|
|Observations|5,948|2,556|4,361|2,512|4,457|2,554|
|R<sup>2</sup>|0.170|0.214|0.661|0.243|0.895|0.678|
|Adjusted R<sup>2</sup>|0.167|0.207|0.659|0.237|0.894|0.676|

_Notes:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. The Lipscomb instrument is employed for grid capacity. Reported standard errors are robust. The amount 10<sup>_−_11</sup> is added to all variables, which enter the regression in logs. Regressions are weighted by municipal GVA and include municipality fixed effects and a time trend.

37