rafmacalaba/datause-encoder-data huggingface dataset:
48

|**Country**||**Gov**|**ernmentResolution Appr**|**oach**||
|---|---|---|---|---|---|
||AMCmechanismused|DecentralizedMechanisms|**Growth**|Other**Impediments**toCorporate<br>Restructuring|Results|
|Chile<br>(1981-86)|None|_Capitalpositionofbanks_<br>*<br>Thepublicandprivaterecapitalization<br>programsimprovedthecapitalbaseofbanks<br>toabsorblosses.<br>_Accounrtingframework_<br>BankSuperintendency cracked downon<br>excessiverelatedlendingin198142and<br>imposedloanlimitstosingleenterprises.<br>Such measurescarnetoolate toreversethe<br>deterioatingsituation.Infact,theauthorities<br>mistakenlyperceivedtheproblemnsto be<br>temporaryand allowedaccountingflexibility<br>tobanksinreflectingtheirlosses (e.g.<br>extendingthreholdfordeclaring delinquent<br>loansnon-perfornning).Thesepracticeswere<br>dismantled onlyin1984-85.|Thegovermnentpursuedtight<br>fiscalpolicyintheyears<br>leadingupto andduringthe<br>crisis. Chile alsoexperienced<br>extraordinarilyhighreal<br>interestratesduringthesame<br>period.Beginningin1982,the<br>governientused monetary<br>policyto activelymoderate<br>increasesin interestrates.|Across-the-board restructuringschenmes<br>didnotdistinguish betweenfintswith<br>varyingdegreesofdifficulties.Thelack<br>ofrealistic assessmentoftheconditions<br>oftheschemes erodeditscredibilityin<br>thelongrun.Thus,repeatedschemes<br>reinforcedtheperception heldbyboth<br>debtorsandcreditorsalikeofthe<br>govemment'sreadinesstoprovide<br>additionaldebtreliefandledtoaserious<br>moralhazardproblem."|Thedecentralized approachbailedout<br>borrowersandstrengthenedthecapital<br>positionofbanksatafiscalcost<br>estimatedtoexceed40%ofGDP.Butit<br>helpedtoimprovethecorporatebalance<br>sheet, together with theeconomic<br>recoverybeginningin1985,thereby<br>allowingforthe reductionoflong-term<br>indebtedness. Therestructuring<br>progransprovidedworkingcapitaland<br>financialrelieffor700/oofdebtorsand<br>helpedthemregaintheir<br>creditworthiness.a|
|||_Legalframework_<br>*<br>Bankruptcyframeworkremained outdated<br>andthejudiciarysystemwasslow.Inthe<br>early1980s,privateagents wereallowedto<br>resolvebankruptcycasesbyhiring<br>specializedprivateconsultingfirmsto<br>engageinresolution(insteadofrelyingon<br>thegovernmentagcncy orthe judicial<br>system).<br>_Ownershipstructure_<br>*<br>In1982,theauthoritiesenforcedexisting<br>ownershiplimits.Also, theBanking Actof<br>1986introduced stringentrestrictionson<br>relatedlending.||||

**F** or example, the 1982 loan purchase program was successful because banks were still reluctant to stop rolling over loans because it would force them to start legal action against delinquent debtors, when it was still likely that the govemment would provide additional relief. a As of end 1986, the financial indicators of 100 firms surveyed whose stock is traded on the Santiago stock exchange had improved substantially, a reflection of the corporate sector's improving financial situation. (World Bank 1987)
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<!-- Start of picture text --> 1<br>8 aN<br>ZZ7 NO aN a<br>6 er a\/iaA ffLY<br>a aN<br>aa Ae oS.<br>NS ‘.<br>2000q1 2005q1 2010q1 2015q1<br>—— Allcountries _—-—W— Advanced economies<br>-—-- - EMEs s++++-+++ Developing countries<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>x<br>‘ aan<br>i\ N (eG<br>2 SNOS of vovNLa esad. SESSrerxi?OMY.XID IN,NEAPY yan<br>1995q1 2000q1 2005q1 2010q1 2015q1<br>—— Banks ——— Corporates<br>—- ++ Sovereigns --------- Central bank<br><!-- End of picture text --> <!-- Start of picture text --> 6 h<br>42 \nraNWA..BN7% af),INIAWkVAyo. ATS.‘ LL./\ABRAIS»71\ ‘AeOAK KSwv<br>1995q1 2000q1 2005q1 2010q1 2015q1<br>—— Banks ——— Corporates<br>—- += Sovereigns --------- Central bank<br><!-- End of picture text --> <!-- Start of picture text --> 1 yipene,<br>sf Hae Ai at ene. *<br>ved AP Me OK *<br>6 Fy# iymS EWA 5NSa<br>a . Y<br>4 GM \ af<br>1yondpoo \ MoM<br>sit ! \<br>1995q1 2000q1 200591 201091 201591<br>—— Allcountries —-—-— Advanced economies<br>- --)- EMEs ses+eee++ Developing countries<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>|<br>|<br>6 * |<br>wan I<br>IDE AAI<br>oN OM Mf]<br>2 \\ViVl | - \VVIVA \vie poSANLSi:A /UXRKSipoAOAntps I<br>1995q1 2000q1 2005q1 2010q1 2015q1<br>—— Banks ——= Corporates<br>—--- Sovereigns --------- Central bank<br><!-- End of picture text -->

<!-- Start of picture text --> 6 n A A<br>42 NaTINVAPROMANfh ADW\/\IN\iyo\LAAEy; { 4/i/ Z/ \LfyfiV/ aN \U F.:<br>1995q1 2000q1 2005q1 2010q1 2015q1<br>—— Banks ——— Corporates<br>—- += Sovereigns --------- Central bank<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>8 Oy \ |<br>2 44 yyion<br>6 at7 ityi\ Yoyhy;y i<br>MONA J<br>4M UP ba fate<br>roaRAST AAPcoh ARoe La cii<br>j wf4 Ye<br>1995q1 2000q1 200591 201091 201591<br>—— Allcountries —-—-— Advanced economies<br>- --:- EMEs ses+eee++ Developing countries<br><!-- End of picture text -->

<!-- Start of picture text --> 8 ret<br>i Vas<br>/ a ‘,<br>6 me N /' ‘ .<br>. aN ra<br>OF ON /TMi<br>4 VK SoZ<br>2) = J] ~~ a, a \wee\ WANsAS~-<br>1995q1 2000q1 2005q1 2010q1 2015q1<br>—— Banks ——= Corporates<br>—--- Sovereigns --------- Central bank<br><!-- End of picture text --> <!-- Start of picture text --> i \ ~,<br>6 vyA Nia 1t ‘\ i4 rN Vw ON N. .<br>42 /\Vi"I,vay \monyyaavieeMtAMrawWS \LOAZN>weiiyiOAKi\7pet7% .<br>1995q1 2000q1 2005q1 2010q1 2015q1<br>—— Banks ——= Corporates<br>—---- Sovereigns --------- Central bank<br><!-- End of picture text -->
representative data sets from 13 countries. While single women reporting condom use at last sex increased from 19.3 percent to 28.1 percent, married women reporting condom use at last sex was much lower and hardly increased (3.7 percent to 4.5 percent). Biraro and others (2009) find that unmarried women were 11.4 times and unmarried men were 7.0 times more likely to use a condom at last sex than married women and men, respectively. Numerous other studies demonstrate that condom use with a spouse is very low (Bertrand et al. 1991; Kapiga et al. 1995; Ahmed et al. 2001; Chimbiri 2007; Maharaj and Cleland2004; Hendrikson et al. 2007). Our analysis also illustrates the vast differences between condom use with a spouse and condom use with another type of partner.

In contrast, much less has been said in the current literature about condom use during extramarital intercourse. There seems to be agreement among researchers that men report significantly higher instances of extramarital sex than women. A study in Tanzania shows that 40 percent of married men but only 3 percent of married women reported having non-marital partners in the last year (Nnko et al. 2004). Based on a nationally representative sample for Uganda, Kirungi and others (2006) report that 12 percent of males versus 3 percent of females report extramarital sex in the previous 12 months. In Zimbabwe, 30 percent of married males compared with 10 percent of married females reported partners outside their marriage in the last year (Mumtaz et al. 2005). 23 percent of married males and only 1 percent of married females reported extramarital sex during the past six months in a sample in Democratic Republic of Congo (Bertrand et al. 1991). Pullum and others (2005) report Demographic Health Survey (DHS) data on men’s extramarital partners from five African countries; the highest is Tanzania with 36 6
**Appendix 1 Table 9: Student test scores regressed on classroom observation variables (level 1), variable-by-variable**

||All te|achers|Teachers with|test score <=0|Teachers with|test score >0|
|---|---|---|---|---|---|---|
|**SDI**|||||||
|Share of time teaching|0.408|-0.092|1.610 ***|1.126 **|0.044|-0.126|
||(0.295)|(0.263)|(0.538)|(0.449)|(0.322)|(0.278)|
|Share of time lost|-0.777 *|-0.258|-0.819|0.025|-0.849|-0.994 *|
||(0.417)|(0.431)|(0.513)|(0.598)|(0.805)|(0.575)|
|Good Teacher Demeanor|-0.219|-0.451 *|0.496|0.723|-0.664 **|-1.031 ***|
||(0.258)|(0.237)|(0.446)|(0.463)|(0.307)|(0.262)|
|Good Pedagogical Practice|-0.404|-0.692 *|-0.181|-0.493|-0.471|-0.825 **|
||(0.403)|(0.335)|(0.646)|(0.553)|(0.529)|(0.339)|
|Classroom Environment|-0.052|-0.120|0.102|-0.042|-0.331|-0.316|
||(0.187)|(0.163)|(0.238)|(0.180)|(0.249)|(0.198)|
|Availability of materials|1.245 *|1.361 **|0.881|0.430|1.567 *|2.538 ***|
||(0.698)|(0.623)|(1.250)|(1.054)|(0.795)|(0.617)|
|Use of materials|-0.309|-0.061|0.005|0.354|-0.486|0.064|
||(0.272)|(0.243)|(0.491)|(0.447)|(0.345)|(0.301)|
|**Stallings**|||||||
|Share of time teaching|-0.158|-0.388|0.207|0.079|-0.428|-0.317|
||(0.326)|(0.273)|(0.601)|(0.607)|(0.509)|(0.336)|
|Share of time lost|-0.759|-0.601|-1.283|-1.613|-0.469|-0.604|
||(0.515)|(0.444)|(0.860)|(0.478)|(0.654)|(0.591)|
|Availability of materials|0.470|-0.045|-0.350|-1.223|1.602|0.829|
||(0.975)|(0.683)|(1.320)|(0.849)|(1.214)|(0.861)|
|Classroom environment|-0.128|0.034|-0.152|-0.047|-0.184|-0.128|
||(0.225)|(0.203)|(0.333)|(0.276)|(0.290)|(0.219)|
|**CLASS**|||||||
|Emotional Support|0.120|0.158 ***|-0.028|0.201 *|0.304 ***|0.025|
||(0.099)|(0.059)|(0.139)|(0.102)|(0.111)|(0.093)|
|Classroom Organisation|0.155|0.190|0.214|0.246|0.142|0.183|
||(0.131)|(0.115)|(0.242)|(0.186)|(0.142)|(0.123)|
|Instructional Support|0.147|0.159 ***|0.029|0.217 **|0.281 **|0.085|
||(0.095)|(0.060)|(0.137)|(0.098)|(0.112)|(0.093)|
|Student Engagement|0.093|0.121 **|0.001|0.205 **|0.205 **|-0.002|
||(0.073)|(0.049)|(0.118)|(0.097)|(0.077)|(0.080)|
|Controls<br>continued...|NO|YES|NO|YES|NO|YES|

A1-11
**Table 9. Additional regression results, including EU and time controls**

|(FE with<br>robust SE)|(9)<br>|(10)<br>Country FE|(11)<br>|(12)<br>Cou|(13)<br>ntry FE an<br>dummies|(14)<br>d EU<br>|(15)<br>Count|(16)<br>ry and Ti|(17)<br>me FE|
|---|---|---|---|---|---|---|---|---|---|
|GDP Growth<br>|14.72*|11.76|15.72*|16.25**|14.19*|16.43*|-3.917|-5.997|-8.865|
|rate||||||||||
||(8.161)|(7.550)|(8.841)|(7.041)|(7.090)|(7.776)|(14.24)|(14.83)|(17.91)|
|Δ Trade<br>O|3.874|4.358*|4.156|3.996|4.316*|4.462|5.191|6.185|5.177|
|penness||||||||||
||(2.399)|(2.476)|(2.585)|(2.335)|(2.428)|(2.561)|(4.623)|(5.037)|(5.170)|
|Δ Enrolled in<br>tertiary ed.|0.0431|0.0520|0.0315|0.0210|0.0273|0.0254|0.0919|0.0996|0.0258|
||(0.0785)|(0.0777)|(0.0770)|(0.0878)|(0.0897)|(0.106)|(0.0808)|(0.0826)|(0.103)|
|Δ Fixed&mobile<br>|<br>0.125**|<br>0.124**|<br>0.0994*|<br>0.133**|<br>0.131**|<br>0.106**|<br>0.103**|<br>0.104**|<br>0.0607|
|phone lines||||||||||
||(0.0470)|(0.0460)|(0.0492)|(0.0475)|(0.0470)|(0.0476)|(0.0465)|(0.0461)|(0.0391)|
|Law and order|<br>2.321**|<br>2.539***|<br>2.409**|<br>2.374**|<br>2.499**|<br>2.504**|<br>2.469***|<br>2.527***|<br>2.026**|
||(0.812)|(0.856)|(0.894)|(1.020)|(1.040)|(1.080)|(0.804)|(0.834)|(0.838)|
|Exchange rate<br>risk||0.212*|0.233*||0.142|0.221||0.186|0.196|
|||(0.108)|(0.128)||(0.124)|(0.136)||(0.169)|(0.187)|
|Large scale<br>privatization|||3.022**|||2.046|||5.021*|
||||(1.348)|||(1.500)|||(2.701)|
|EU accession<br>candidate||||2.326**|2.124**|1.799***||||
|||||(0.826)|(0.921)|(0.587)||||
|EU member||||0.410<br>(1.292)|0.336<br>(1.291)|0.382<br>(1.650)||||
|Constant|-6.733*|-9.466**|-18.8**|-8.912*|-10.46*|-17.39**|-7.055|-8.561*|-21.2**|
||(3.677)|(4.381)|(7.327)|(5.064)|(5.425)|(7.199)|(4.284)|(4.702)|(8.812)|
|Observations|214|214|183|214|214|183|214|214|183|
|R-squared|0.247|0.254|0.253|0.266|0.269|0.260|0.318|0.322|0.336|
|Number of<br>countries|16|16|15|16|16|15|16|16|15|

Source: author’s calculation. *** , **, *, denote significance at 1%, 5%, and 10% levels.

22
much of their money will be diverted to service old debt, thereby reducing the value of their claims even if projects financed by the new moneys have a sufficiently high rate of return to service the new debt in the absence of old claims outstanding. This is the classic debt overhang (DO) problem, triggering a larger need for adjustment than just the current fall in income, as debt repayments coming due cannot be refinanced either. In this way debt overhang problems raise the costs of adjustment substantially and may also explain why volatility of government expenditure in Nigeria has exceeded the volatility of oil prices.

It is important to note that debt overhang problems can arise in countries with relatively little debt; what matters is short term cash flow needs. So a modest debt but all coming due in the near future is more damaging than a much higher debt with amortization smoothly stretched. And the worse the debt service record, the more likely it is there will be a debt overhang problem. The point that they did not service their existing debt actually reinforces the argument: debt overhang exists when debt has strong equity characteristics (i.e. is only serviced in good times) and accordingly trades at a large discount.

# **A. Fiscal policy as a source of volatility**

Fiscal policy in Nigeria has in fact contributed to volatility, rather than smooth the impact of volatile oil prices on the economy. Despite the government’s attempts to save past oil windfalls, public expenditure has remained highly correlated with oil fiscal revenue and fluctuated widely as a result of weak fiscal discipline and high oil price volatility (Figure 9a.).

**Figure 9 a. Revenues and Expenditures, 1971-2005 b. Expenditure volatility and Growth**

<!-- Start of picture text --> Expenditure and Revenue (% of Non-Oil GDP) Nigeria: Volatility and Growth<br>90 3.5 8<br>80<br>3.0 6<br>70<br>2.5 4<br>60<br>50 2.0 2<br>40<br>1.5 0<br>30<br>20 1.0 -2<br>10 0.5 -4<br>0<br>0.0 -6<br>1970- 1976- 1981- 1986- 1991- 1996- 2001-<br>1975 1980 1985 1990 1995 2000 2005<br>Total Expenditure (% of non-oil GDP) Relative Volatility of Govt. Expenditure to Oil Exports<br>Lagged Total Revenue (% of non-oil GDP) Real Non-Oil GDP Growth (%, annual avg, right scale)<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005<br><!-- End of picture text --> 15
# **Figure 5: RDD plot of employment**

A. The probability of having farm work before 2009 B. The probability of having farm work since 2009 <!-- Start of picture text --> 20 30 40 50 60 70 80 20 25 30 35 40 45 50 55 60 65 70 75 80<br>The 2006 poverty rate of districts (%) The 2006 poverty rate of districts (%)<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>C. The probability of having nonfarm work before 2009 D. The probability of having nonfarm work since 2009<br>20 30 40 50 60 70 80 20 25 30 35 40 45 50 55 60 65 70 75 80<br>The 2006 poverty rate of districts (%) The 2006 poverty rate of districts (%)<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>E. The probability of having a wage job before 2009 F. The probability of having a wage job since 2009<br>20 30 40 50 60 70 80 20 25 30 35 40 45 50 55 60 65 70 75 80<br>The 2006 poverty rate of districts (%) The 2006 poverty rate of districts (%)<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>1 1<br>.8<br>.8<br>.6<br>.6<br>.4<br>.4<br>.2<br>The probability of having farm work The probability of having farm work<br>.2 0<br>.3 .3<br>.2 .2<br>.1 .1<br>The probability of having nonfarm work The probability of having nonfarm work<br>0 0<br>.3 .3<br>.2 .2<br>.1 .1<br>The probability of having a wage job The probability of having a wage job<br>0 0<br><!-- End of picture text --> Note: This graph shows the regression discontinuity plot of employment outcomes of individuals across the 2006 poverty rate of districts. The sample is limited to individuals living districts with the 2006 poverty rate above 40%.

37
_<u>Table 21: Poverty impact of trade, by educational levels</u>_

|Hh. average<br>schooling|Population<br>%<br>2001|P0<br>initial lev|% contr. to<br>P0<br>els|Population<br>%<br>|BaU 2001-<br>15 P0<br>change<br>201|Doha % of<br>BaU P0<br>change<br>5|Full % of<br>BaU P0<br>change|
|---|---|---|---|---|---|---|---|
|<= 3|16.2|52.3|35.9|16.0|-10.7|103.5|111.5|
|<=5|16.9|36.0|25.7|16.6|-9.1|102.9|109.9|
|<=8|20.9|21.1|18.7|20.7|-5.6|105.0|111.5|
|<=10|12.9|11.3|6.2|12.9|-2.9|104.6|111.3|
|>=11|33.1|9.6|13.5|33.7|-2.0|101.7|104.6|

Source: Authors’ calculations.

As Table 21 indicates, the educational level of the households is an important determinant of poverty. The headcount among households with 3 or less average years of schooling of the employed household members is well above 50 percent. This group accounts for 16.2 percent of the population but for more than a third of the poor. The poverty incidence among households with 4 or 5 years of schooling is 15 percentage points lower. For both groups with ‘ten’ or ‘more than ten’ years of average schooling, the headcount is about 10 percent. The Doha round does not appear to be particularly helpful for those with little educational endowment. Yet, the full liberalization scenario again leads to a substantial additional reduction in poverty.

Finally, we analyze the poverty impact of trade reform by occupational groups. In Table 22 we differentiate between wage-employed, self-employed and households with members engaged in both types of employment in agricultural and non-agricultural activities, respectively.<sup>39</sup> In addition, there are households with no employed household member. One out of five poor people in Brazil comes from a self-employed agricultural household, and the agricultural wage-employed households are almost equally poor. In non-agricultural households, the difference between self-employed and wage-employed households in terms of poverty is not too pronounced. Due to their high share in the population, non-agricultural wage-employed households account for more than a third of the Brazilian poor. Poverty rates are significantly lower for households who derive their income from both wage-and self-employment. Under the Doha scenario, all nonagricultural household groups gain, whereas there are only minor gains for agricultural households. As noted above, full liberalization however helps both agricultural and nonagricultural households. Interestingly, poverty in non-agricultural activities declines more

> 39 If the number of self-employed household members is greater than the number of wage-employed members, the household is considered self-employed, and vice versa. Are the numbers equal, the households falls under the “both” category.

50
-106-

- G.P. Pfefferrann and R. Webb (1979), The Distribution of Income in Brazil, World Bank, Washington D.C..

- S.J. Prais (1981), Productivity and Industrial Structure, Cambridge, 1981.

- Z. Roman (1982), Productivity and Economic Growth, Akademiai Kiado, Budapest.

- L. Rostas (1948), Comparative Productivity in British and Americoin Industry, Cambridqe.

- A.D. Roy (1982), "Labour Productivity in 1980: An International Comparison:, National Institute Economic Review.

- D.J. Roy (1987), "International Comparisons of Real Value Added, Productivity and Energy Intensity in 1980", Economic Trends, June.

- G.E. Sadler and E. Grossman (1982), "Comparative Productivity Dynamics, Japan and the United States", American Productivity Center, Houston (mimeographed).

- G.E. Sadler (1986), "International Productivity Comparisons", American Productivity Center, Houston (mimeographed).

- G.E. Schroeder and I. Edwards (1981), Consumption in the USSR: An International Comparison, CIA study published by Joint Economic Committee, US Congress, Washington DC.

- M. Selowsky (1967), "Education and Economic Growth, Some International Comparisons", Ph.D. thesis, University of Chicago.

- M. Shinohara (1966), Japan's Industrial Level in International Perspective, Ministry of Foreign Affairs, Tokyo, March.

- I.H. Siegel (1951), "Concepts and Definitions of Production and Productivity", Ph. D. Thesis, Columbia University.

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- SPP (1979a), X Censo Industrial 1976. Datos de 1975, Resumen General, Tomo 1, Mexico.

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- R. Stone (1956), Quantity and Price Indices in National Accounts, OEEC, Paris.
**Table 5.** Effects of the lottery incentive intervention on STI prevalence

|Sample|||A|ll|||HIV+ b|aseline|
|---|---|---|---|---|---|---|---|---|
||OLS|OLS|Probit|OLS|OLS|Probit|OLS|OLS|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Any lottery|-0.032<sup>***</sup>|-0.032<sup>***</sup>|-0.033<sup>***</sup>||||-0.108<sup>***</sup>||
||(0.009)|(0.009)|(0.008)||||(0.039)||
|High lottery||||-0.034<sup>***</sup>|-0.034<sup>***</sup>|-0.039<sup>***</sup>||-0.112<sup>***</sup>|
|||||(0.010)|(0.010)|(0.013)||(0.039)|
|Low lottery||||-0.031<sup>***</sup>|-0.031<sup>***</sup>|-0.029<sup>***</sup>||-0.102<sup>**</sup>|
|||||(0.010)|(0.009)|(0.009)||(0.038)|
|Mean control group|0.038|0.038|0.038|0.038|0.038|0.038|0.112|0.112|
|Control STI status baseline|No|Yes|No|No|Yes|No|No|No|
|P-value (TH=TL)||||0.44|0.41|0.49||0.13|
|Observations|2884|2884|2884|2884|2884|2884|466|466|

Note: Sample of individuals aged 18-32 at baseline. The dependent variable (STI prevalence) is equal to one if the individual tested positive for any of the two STIs (syphilis and trichomoniasis) after 24 months and zero otherwise. Any lottery is high and low lottery combined. Probit estimates are marginal effects calculated at the mean. P-value (TH=TL) is the p-value for the test that the treatment effects are equal in the high and low lottery arm. All regressions include village fixed effects. Robust standard errors in parentheses, clustered at the village level. *** 1% , ** 5% , * 10% significance.
Table A2. 8 Administrative burden on sole proprietor firms

||**_Weight on_**<br>**_mlin t_**<br>**Administrativ**|**e burdens**|**for Sole Pr**|**oprietor F**|**Scale 0-6**<br>**irms**||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**_opace ype_**||||||||||||||||
||**_(c k )_**|**0**|**1**|**2**|**3**|**4**|**5**|**6**|**Croatia**|**Albania**|**OECD**|**EU-15**|**Poland**|**Slovakia**|**Republic**|**Hungary**|
|Number of mandatory procedures<br>required to register a sole prioprietor<br>firm (pre-registration+registration)|1/4|<=3|<=5|<=8|<=12|<=16|<=20|>20|16|10|8.6|7.3|7|12|7|9|
|Number of public and private bodies to<br>contact to register a sole proprietor<br>firm(pre-registration+registration)|1/4|0|1|2|3|4|5|6|2.5|2|3.4|3.5|5|7|3|6|
|Number of working days required to<br>complete all mandatory procedures for<br>registering a sole proprietor firm (pre-<br>registration+registration)|1/4|<=16.4|<=32.8|<=49.2|<=65.6|<=82|<=98.4|>98.4|8|9.5|12.3|8.7|7|24|30|19|
|Total cost (euros) of registering a sole<br>proprietor (pre-registration+registration)|1/4|<=500|<=1000|<=1500|<=2500|<=5000|<=7500|>7500|90.8|0.82|227.8|283.4|248|1572|32.967033|473.35411|
|**Country scores(0-6)**|kckanswerk||||||||2.25|1.75|1.9|1.8|3.25|2.25|2|3|

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