rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 10d. Transparency and Circulation**

_DATA SET: ICRG, Averaged over the period 1984-1997_

||ICRG|Corruption|RuleofLaw|Bureaucracy<br>Quality|Contract<br>Repudiation|Expropriation<br>Risk<br>|
|---|---|---|---|---|---|---|
|SocialistLegal Code|.32|.65**|1.13***|.03<br>|.10<br>|_.50**_<br>|
||(1.24)|(2.1)|(4.44)|(.10)|(.37)|(2.27)<br>|
|FrenchLegalCode|-.55***|-.47**|-.41*|-.94***|-.<br>34**<br>|-.45**<br>|
||_(-3.35)_|(-2.05)|(-1.69)|(4.01)<br>|(-2.14)<br>|(-.34)<br>|
|German LegalCode|.60**|.54|.77*|.90***<br>|.66***<br>|.34**<br>|
||(2.55)|(1.31)|(1.75)|(2.86)|(3.76)<br>|(2.06)<br>|
|Scandinavian LegalCode|.77***|1.39***|1.18***|.89***|.49**|.30*<br>|
||(3.49)|(4.72)|(4.03)|(2.65)|(2.61)<br>|(1.97)<br>|
|Income|.48**|.59**|.74***|.79***|.26<br>|.28**<br>|
||(2.5)|(2.39)|(3.21)|(3.02)|(1.53)|(201)|
|Transparency|-.42***|-.53***|-.38***|-.57***|-.27***|-.30***|
||(-5.94)|(-5.35)|(-2.93)|(-5.68)|(-3.16)|(4.21)<br>|
|Circulation(reverse)|.02|-.10|.02<br>|-.18<br>|.23**<br>|.13<br>|
|._|__________<br>(.16)|(-.61)|(.13)|(-1.22)|(2.05)|(1.48)<br>|
|Constant|1.21|.58|-1.36|-.16<br>|1.80**|2.57***|
||(1.08)|(.41)|(-1.00)|(-.11)|(1.76)|(2.97)<br>|
|#obs|82|82|82|82<br>|82<br>|82<br>|
|Ra|.68|.57|.59|.62|.66|.66|

_*indicate_ **_s_** _ignificance at. Ilevel, ** at.05 level and *** at.01_<sup>_level._</sup>

# **Table 10e. Transparency and FOIA**

_DA TA SET: KAUFMANN, KRAA YAND ZoIDO-LOBA TON_

||Howauthoritie<br>rep|sare selectedand<br>laced|Capacityof<br>implementso|thestate to<br>und policies|Respectofthest<br>citizens forth<br>|ateand the<br>erules<br>|
|---|---|---|---|---|---|---|
||Voice and<br>Accountability|PoliticalInstability<br>and Violence|Govemnment<br>Effectiveness|Regulatory<br>Burden<br>|RuleofLaw<br>|Graft<br>|
|SocialistLegalCode|-.40**|-.07|-.68***<br>|-.71***<br>|-.52***<br>|-.85***<br>|
||(-2.45)|(--41<br>|(-.6<br>|(.4.49)<br>|(-3.58)<br>|(-6.17)<br>|
|FrenchLegalCode|.02|-.11<br>|-.08|-.08<br>|-.27**<br>|-.33***<br>|
||(.14)|(-.80)<br>||(-.92)<br>|(-2.57)<br>|(-2.91)<br>|
|GermanLegalCode|.45|.27|.17|-.35*|.42**|-.02<br>|
||(1.63)|(I.01)|(.51)<br>|(-1.8)<br>|(2.19)<br>|(-.04)<br>|
|ScandinavianLegalCode|.54***|_.50***_<br>|.57***<br>|-.10<br>|.48***<br>|.73***<br>|
||(3.09)<br>|(3.62)<br>|(4.31)<br>|(-.89)<br>|(3.92)<br>|(5.05)<br>|
|Income|.31*.|.46***|.37***<br>|.23***<br>|.52***<br>|.44**<br>|
||(3.7)|(5.91)|(5.29)|(3.88)|(8.28)<br>|(6.34)<br>|
|Transparency|-.09<br>|-.10<br>|-.15**<br>|.18***<br>|-.07<br>|.11**<br>|
||(-1.37)<br>|(-1.47)<br>|(-2.39)<br>|(-3.09)<br>|(-1.38)<br>|(-2.15)<br>|
|FOIA|.52***<br>|.04<br>|.26**<br>|.19**<br>|.08<br>|.23*<br>|
|(reverse)|(3.46)|(.30)|(2.04)|(2.02)<br>|(.74)<br>|(1.73)<br>|
|Constant|-2,31***|3.66***|-2.73***<br>|-1.27**<br>|-3.94***<br>|-3.16***<br>|
||(-3.01)|(4.59)|(-3.99)<br>|(-.7<br>|(-6.41)<br>|(-5.05)<br>|
|#obs|134<br>|123<br>|124<br>|129<br>|129<br>|123<br>|
|RH|.52|.54|.64|.56|.69|.70|

28
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19
Figure 8: Size Distribution for Food Industry <!-- Start of picture text --> 1 100 1000 10000 100000<br>Size<br>Simulation Data<br>Pareto(-0.7) Logarithmic (0.95)<br>1<br>0.1<br>Pr(size>S)<br>0.001<br>0.0001<br><!-- End of picture text --> In the model, innovation is done by the incumbent producers. When innovation cost is low, these producers get more innovative and there is less room for entry. Hence larger industries have lower turnover rates as observed in the data. The model performs relatively well in capturing the entry rates across industries. However, it underestimates the growth rate distribution.

# 5 What Causes Size Dispersion?

In this section, I elaborate a novel feature of the model. The model incorporates two forces that generate persistent differences in establishments’ performances. In his review of models on establishment evolution, Sutton (1997) lists these forces as: () intrinsic efficiency differences that are determined before entering the economy () differences that are generated through idiosyncratic innovations that accumulate through the life of the establishment. Both views have drawn great attention in the literature<sup>20</sup> . Among the first group of models, Lucas (1978) and Jovanovic (1982) link the differences in efficiencies to the differences in the skills of entrepreneurs. In the second group of models, performance is driven by producer specific learning, R&D, and innovation. Some recent models that follow this view are Ericson and Pakes (1995), Klepper (1996), Klette and Kortum (2004), and Klepper and Thompson (2007).

The model introduced here distinguishes the contributions of exogenous idiosyncratic efficiency differences and accumulated innovations to explain size dispersion. It generates disper-

> 20For a review and comparison of both types of models see Klette and Raknerud (2002).

27
The variables generally take the expected signs, while insignificant parameters result in some cases. The coefficients on distance take the larger values, compared to the estimated in the existing studies at around 0.1 to 0.4. However, the interaction term may adjust it. The average values of the value/weight variable (in log) of the trading partners to Japan are 4.3 for ASEAN5, 4.8 for China, 4.6 for Korea and 4.8 for the United Sates, but only 3.6 for Canada and 2.3 for Australia. Taking the values of the interaction terms in calculation, the elasticity of the distance is almost zero for the neighboring countries to Japan, but 0.1 to 0.4 for Canada and Australia, being in the remote location.

The value/weight variable takes the coefficients larger than one. However, as discussed above, the estimated parameters reflect the variation of the compositions of commodities and modals across the countries. Again, taking the interaction term into consideration, with the average value of the distance variable in log around 7.7, the elasticity of the value/weight would be around one on average, and certainly less than one for the remote countries. A percentage point increase in the shares of air shipments in value increase the total trade cost by 1.4 – 1.7 percent, reflecting higher freight charge by air. A percentage point increase in the share of the specific bulky goods with HS 25-27 in volume decrease the total trade cost by 0.3 – 0.6 percent, reflecting lower charges for the modals to transport these goods, normally tankers and tramps<sup>26</sup> . In sum, controlling the difference in composition of commodities appears to work well, if not perfectly.

The estimated coefficients on the indicators on ports in the Japanese trade cost regression take the expected signs. Their values resemble those obtained from the regression using the U.S. data. However, the estimated coefficients here represent the impacts on total transport costs including those both via vessel and air. If the factors represented by the port indicators affect the air transport costs to lesser degree than ocean transport costs, the estimated coefficients of the port indicators here should be naturally smaller than those on the U.S. The coefficient of our port congestion index takes exactly the same number as the U.S. regression. If we assume no impact of the port congestion on the air transport costs, a 1 percent reduction in our index is estimated

> 26 The ocean shipments costs considerably vary among the modals: the freight charges per ton for Japanese imports are 9,785 yen for linter, 1,872 yen for trampers, and 1,308 yen for tankers ( _Maritime Affairs Report 2004_ by Ministry of Land, Infrastructure and Transport of Japan).

28
The third conclusion, about training, is a useful reminder about possible bottlenecks in the labor market.

The fourth conclusion addresses the structural change induced by environmental policies and so acknowledges the need to take some account of the issue of job displacement.

The fifth conclusion very properly draws attention to possible leakages of green jobs and spending to other countries, to an extent that depends on endowments of skills, existing industry structure, the nature of the technologies newly deployed and the ways in which comparative advantage is exploited – a reminder that general equilibrium effects matter. Yet these are largely ignored in the green jobs literature. They may differ in developing countries from the effects in developed countries, as the next section discusses.

# **4. Modeling labor markets in developing countries**

It has long been recognized that the standard neoclassical or Keynesian models may not adequately represent labor markets in developing countries. Thus the implicit assumption in many green jobs studies that there is Keynesian unemployment and no crowding out of jobs elsewhere in the economy by green fiscal stimuli may not be valid. But equally, the objection that employment is determined in competitive markets, implying that green jobs are likely to displace at least as many jobs elsewhere in the economy, may lack force because the assumptions on which it is based do not hold.

This issue is important for evaluating green policy initiatives’ labor market implications and is hardly a new one. For example, half a century ago Lewis drew attention to the possible existence of surplus labor in developing countries, particularly in agriculture (Lewis, 1954). In a surplus labor economy, there is much less risk of crowding out employment when green projects are undertaken. Thus the estimates of direct employment creation in the green jobs literature might be less misleading for developing countries than for industrial economies close to full employment. But the situation is more complicated in ‘dual’ economies with modern and traditional sectors or three-sector economies with a traditional rural sector and both formal and informal urban sectors, as has also been long understood (Harris and Todaro, 1970; Fields, 1975; Mazumdar, 1976).

20
are also major producers of expert assessments. Some of the most notable include the Country Policy and Institutional Assessments produced by the World Bank, by the African Development Bank, and also by the Asian Development Bank. Each one of these assessments is based on the responses of their country economists to a detailed questionnaire, which are then reviewed for consistency and comparability across countries. Other examples include the Public Expenditure and Financial Accountability (PEFA) indicators mentioned above.

We also identify several large cross-country surveys of firms and individuals that contain questions relating to governance. These include the Investment Climate Assessment and the Business Environment and Enterprise Performance Surveys of the World Bank, the Executive Opinion Survey of the World Economic Forum, the World Competitiveness Yearbook, Voice of the People, and the Gallup World Poll.

# _Expert Assessments_

Expert assessments have several major advantages which account for their preponderance among various types of governance indicators. One is simply cost: it is for example much less expensive to ask a selection of country economists at the World Bank to provide responses to a questionnaire on governance as part of the CPIA process than it is to carry out representative surveys of firms or households in a hundred or more countries. A second straightforward advantage is that expert assessments can more readily be tailored towards cross-country comparability: many of the organizations listed in Table 1 have fairly elaborate benchmarking systems to ensure that scores are comparable across countries. And finally, for certain aspects of governance, experts simply are the natural respondent for the type of information being sought. Consider for example the Open Budget Index's detailed questionnaire regarding national budget processes, the particulars of which are not the sort of common knowledge that survey data can easily collect.

Expert assessments nevertheless have several important limitations. A basic one is that, just as is the case among survey respondents, different experts may well have different views about similar aspects of governance. While this is perhaps not very surprising, it suggests that users of governance indicators should be cautious about 16
Table 4: Transitions from employment states to studying by gender and income, in percent

|**Non Conditional**|**Argent**|**ina**|**Braz**|**il**|**Mexic**|**o**|
|---|---|---|---|---|---|---|
|**All**|15-18|19-24|15-18|19-24|15-18|19-24|
|Self employment|21.2|8.6|15.6|3.7|17.1|5.6|
|Formal|14.2|3.6|5.2|1.5|7.7|2.9|
|Informal|19.7|9.5|12.7|4.8|16.0|7.6|
|**Conditional**|**Argent**|**ina**|**Braz**|**il**|**Mexic**|**o**|
|**All**|15-18|19-24|15-18|19-24|15-18|19-24|
|Self employment|28.3|13.6|30.7|10.8|25.3|11.3|
|Formal|28.2|15.4|33.9|16.1|23.9|14.6|
|Informal|35.3|19.3|37.9|13.4|32.8|15.2|
|**Males**|15-18|19-24|15-18|19-24|15-18|19-24|
|Self employment|26.2|12.1|29.2|10.0|23.3|11.0|
|Formal|22.6|11.8|31.9|14.9|23.3|13.6|
|Informal|31.3|15.9|37.4|12.4|34.8|14.7|
|**Females**|15-18|19-24|15-18|19-24|15-18|19-24|
|Self employment|35.8|17.5|34.4|12.4|33.2|12.1|
|Formal|40.0|21.5|36.8|17.6|24.7|15.8|
|Informal|42.8|24.4|38.8|14.8|29.0|16.2|
|**Poor**|15-18|19-24|15-18|19-24|15-18|19-24|
|Self employment|20.6|11.8|24.0|9.0|20.4|8.7|
|Formal|18.5|8.1|23.9|10.9|21.0|10.1|
|Informal|28.6|15.4|33.8|9.9|31.9|13.7|
|**Nonpoor**|15-18|19-24|15-18|19-24|15-18|19-24|
|Self employment|43.8|21.6|41.8|15.9|40.2|15.8|
|Formal|42.1|22.6|43.3|20.9|37.0|20.4|
|Informal|58.6|25.6|45.9|21.4|48.0|20.1|

*Non-conditional transition estimates are drawn from the transition matrix; Conditional transition estimates are drawn from the propensity matrix

Table 5: Transitions to self-employment by age and income, conditional on moving, in <u>percent.</u>

|**Arg**|**entina**|**OLF**|**Studying**|**Unemployed**|**Formal**|**Informal**|
|---|---|---|---|---|---|---|
|15-18|Poor|5.6|5.6|9.0|3.7|16.3|
|<br>|Nonpoor|3.8|5.4|5.3|5.3|10.3|
|19-24|Poor|8.8|10.3|13.0|7.4|21.8|
|<br>|Nonpoor|8.0|6.9|3.8|7.2|11.5|
|**B**|**razil**|**OLF**|**Studying**|**Unemployed**|**Formal**|**Informal**|
|15-18|Poor|14.2|15.2|7.9|2.5|12.3|
|<br>|Nonpoor|10.4|11.5|6.8|2.3|8.5|
|19-24|Poor|18.3|18.4|12.8|13.2|19.3|
|<br>|Nonpoor|15.6|15.1|10.3|8.4|16.7|
|**M**|**exico**|**OLF**|**Studying**|**Unemployed**|**Formal**|**Informal**|
|15-18|Poor|3.7|4.1|2.3|3.7|7.3|
|<br>|Nonpoor|2.8|3.5|2.5|2.1|4.8|
|19-24|Poor|9.2|7.8|4.1|8.5|13.9|
|<br>|Nonpoor|10.2|8.4|5.3|9.8|13.4|

22
Offices, while 70 thousand<sup>werederegistered,of whichonlylessthan40thousand</sup> reportedly found jobs<sup>(Table 5).Sincethe excess of inflows into unemploymentover</sup> outflows from unemployment<sup>haspersistedovera numberof years,the resulthas</sup> been a gradual build<sup>up of unemployment. Whileregistered unemploymentwas240</sup> thousand in 1995, in early<sup>2002it alreadyexceeded410 thousand.</sup> There is a discrepancy<sup>between unemploymentdatacomingfromthe</sup> unemployment register<sup>andfromthe Labor ForceSurvey(see Box1 for a description</sup> of the differences between<sup>bothsources).This isa common regularity,and Croatiais</sup> no exception, however<sup>thediscrepancyisquite dramatic.Over 40percentofall</sup> persons who are registered<sup>at EmploymentOfficesare</sup><sup>_not_unemployedaccordingto</sup> the ILO definition of<sup>unemployment! 4They either have a job (in theinformal sector),</sup> or are not actively looking<sup>forwork, or arenot availablefor work.</sup> The genuinely unemployed<sup>asa ruleregister at EmploymentOffices;only14</sup> percent of the job seekers<sup>do notregister,and thisgroupis likely toincludemainly</sup> persons whose unemployment<sup>is short-termor transient.The net effect is thus that the</sup> unemployment register<sup>datasignificantly-byalmost50percent -</sup> overestimate the level of unemployment.

4 Based on the Labor Force<sup>Survey data from the second half of 2001.</sup>

-7-
standard deviation of 100, these are not very large numbers. Also, when we examined the differences in means between the two years, it became apparent that the regression sample was more urban and public school-oriented in both years but particularly in 2003. However, we found that the overall mean test score of the regression sample was very similar to the whole sample mean. Therefore, we concluded that the regression sample was not biased.

# **Table 2: PISA 2003-2006, Mean Math Scores** **<u>by Selected Characteristics</u>**

||Pisa|2003|Pisa|2006|
|---|---|---|---|---|
||_Yes_|_No_|_Yes_|_No_|
|_Institutions_|||||
|School determines pedagogy|360|371|393|360|
|Adequate supply of teachers|357|363|396|371|
|_School_|||||
|Public|374|344|404|372|
|Rural|335|371|364|401|
|_Student_|||||
|8th grade|313||342||
|9th grade|348||366||
|10th grade|395||424||
|11thgrade|413||423||
|Female|358|362|382|399|
|_Family_|||||
|Mother - no education|347||369||
|Mother – primary|350||377||
|Mother - lower secondary|360||389||
|Mother - upper secondary|398||417||
|Mother – university|359||417||
|0 - 10 books|358||383||
|11 - 100 books|363||393||
|101- 500 books|391||415||
|Home computer > 1|387|355|453|382|
|Home language same as test|362|361|402|385|

_Source_ : PISA, 2003 and 2006 9