rafmacalaba/datause-encoder-data huggingface dataset:
belief, universally held until quite recently, that the atmosphere was an infinite resource. Moreover, the expropriators are mostly dead and gone. Their descendants, even if they could be identified, cannot be held responsible for actions they did not themselves commit.”<sup>6</sup> For example, if only individuals can be responsible, then calculations from the Climate Analysis Indicator Tool (CAIT) suggest that just 8 percent of the 2000 emissions stock can be traced to the flow of emissions from individuals who are still alive and might be held responsible.<sup>7</sup> The rich countries have their own narrative of recalcitrance. They blame the major developingcountry emitters such as China and India for not cooperating adequately and for being unwilling even to consider emissions cuts. Furthermore, claim some in the rich world, if we are responsible for pollution, then the developing world should be responsible for its large population. And if we are to be blamed for the “bads” such as emissions, then we should get credit for the “goods” that we have provided in the form of technology and research (such as those that led to the green revolution.)

# _B. The Adding-Up Problem_

The adding-up problem is the brutal arithmetic that for the planet to survive in some habitable form, the world has to live within a fixed carbon budget of about 750 gigatons of CO2 emissions between now and 2050. More allocations for one country mean less for another. The adding up problem arises because, contrary to popular conception and the assumption underlying much of the climate negotiations, the atmosphere is already saturated with carbon: there is no more “space” to allocate to additional emissions if humans want to preserve a climate that resembles the one in which civilization arose. The cold, hard fact is that a drastic reduction in aggregate emissions is required if we are to achieve a reasonable probability of keeping temperatures at livable levels. But the exercise is even more 6. Joshi (2009, pp. 130-131).

7. Posner and Weisbach, table 5.1 (2011).

11
interest. In this respect, we join a growing literature in recognizing the value of BMA as a tool for systematically identifying robust partial correlates of outcomes when the precise empirical specification is unknown. However this positive message is tempered by two important pieces of bad news. The first is that there is a great deal of instability _across very similar outcomes_ in terms of which variables the BMA procedure identifies as important partial correlates of outcomes. To take the most extreme example, our seven corruption outcome variables have a quite strong average pairwise correlation of 0.62. Yet when we compare across these very similar outcomes, we find that there is virtually no overlap in the subsets of the 303 disaggregated individual indicators in GII that are identified as important determinants of these outcomes by the BMA procedure. The second is that there is a very high degree of instability _across levels of aggregation_ in terms of which individual indicators are identified as important determinants of outcomes by BMA. In particular, we find that the probability that an individual disaggregated indicator is identified as an important determinant of outcomes is not significantly increased by knowledge that the higher‐level aggregate to which it belongs was identified as an important determinant of outcomes. And conversely, knowing that a higher‐level aggregate is identified by BMA as an important determinant of an outcome does not mean that the more disaggregated variables on which it is based are more likely to be identified as important determinants of the same outcome.

These results suggest that it may be very difficult to use the disaggregated indicators of institutional and regulatory quality that we examine here to provide policy advice to guide reforms in these areas. Under the reasonable assumption that policymakers would like to identify high‐impact reforms that that matter for outcomes, it becomes important to identify which those reforms are. Yet we find that quite small changes in the empirical proxies for outcomes that we consider lead to wild fluctuations in the set of variables that are identified as important for those outcomes.

The rest of this paper proceeds as follows. In Section 2 we provide details on the Global Integrity and Doing Business datasets that we use in our empirical analysis, and we justify our selection of outcome variables corresponding to these datasets. In Section 3 we explain the Bayesian Model Averaging methodology. Section 4 contains the results and Section 5 discusses the robustness of the results and caveats. Section 6 offers conclusions.

6
The Impact of Submarine Cables on Internet Access Price, and the Role of Competition and Regulation<sup>∗</sup> Joël Cariolle,<sup>†</sup> Georges Vivien Houngbonon<sup>‡</sup> ,Tarna Silue,<sup>§</sup> Davide Strusani<sup>¶</sup>

**Keywords:** Digital Economy Strategy; Submarine Cable; Price Controls; Competition Economics; Regulatory Impact Assessment.

**JEL Classification:** E31, L51, L96, O33.

> ∗This study was conducted as part of efforts from the International Finance Corporation, the private sector arm of the World Bank Group, to understand the development impact of digital infrastructure and associated investment opportunities. Earlier study drafts benefited from comments from Jonas Hjort, Denis Medvedev, Marcio Cruz, Maty Konte, Mariana Lopez, Florian Moelders, and Samuel Edet. The authors thank Stephane Straub, the World Bank’s Infrastructure Chief Economist for comments and suggestions. The authors are also grateful to Lozanova Youlia, Senior Analyst of Policy and Regulation at the International Telecommunications Union for kindly providing access to the detailed regulation data.

> †Research Officer, FERDI, and University Clermont Auvergne, CNRS, IRD, CERDI, Clermont-Ferrand, France.joel.cariolle@ferdi.fr

> ‡Economist, IFC:ghoungbonon@ifc.org and Senior Fellow at the GWU Competition and Innovation Lab, corresponding author

> §Economist, IFC:tsilue@ifc.org

> ¶Principal Economist, IFC:dstrusani@ifc.org
votes, around 30 to 35 percent, it can move to a landslide victory in seats by gaining just a few percentage points in popular support. In many electoral districts it is possible for a candidate to win with just about 20 percent of the popular vote (Butler, Lahiri, and Roy 1995).

At the end of the day, it is really the number of seats in the legislature that matters for political control, so interest group politics to attain the critical number of votes from a large number of constituencies may be the key political strategy for all parties. Getting the critical number of votes to win a district may therefore be easier to accomplish with a strategy of targeted public expenditures towards pivotal coalitions of voters, and the cost of undertaking non-optimal levels of spending may be too diffuse across voters to translate into a substantial loss in electoral districts.

The relative importance of intergovernmental transfers and federal political institutions in explaining variation in subnational deficits is ultimately an empirical question. In the following section we lay out the empirical specification suggested by the analytical framework presented here, and test for the significance of political affiliation and central transfers in determining subnational deficits.

# **3. Data, empirical specification, and results**

## **_Data_**

The data set for this study is compiled from diverse sources for 15 major states of India over the period 1972-95. The political data is compiled from Butler, Lahiri, and Roy (1995). The public finance data on revenues, expenditures, and intergovernment transfers, is available from relevant volumes of the _Reserve Bank of India Bulletin_ , a quarterly publication of the central bank of India with annual issues on details of finances of state governments.<sup>5</sup> State demographic and economic characteristics, and a state-level price index to convert all variables into real terms are available from an Indian data set put together at the World Bank. A detailed description of these variables is available in Ozler, Datt, and Ravallion (1996). Table 1 provides summary statistics for each of the variables included in the analysis.

> 5 I am grateful to Tim Besley of the London School of Economics and to Bhaskar Naidu of the World Bank’s South Asia regional division for providing me with some of this data that had already been compiled in their research groups.

13
- 82 -

equilibrium is computed. Policy evaluation is then based on pairwise comparison between the benchmark and new equilibrium.

Four recent studies have employed computable general equilibrium analysis to evaluate the impacts of tax incentives. Clarete (forthcoming) utilizes a static general equilibrium model to examine the effects of tax rebates and duty drawbacks on imported machinery and equipment by priority industries. The author observes a strongly stimulative impact of these incentives on investment. These conclusions, however, must be considered tentative, as the author employed a static model. The use of a dynamic model might well lead to different qualitative and quantitative conclusions.

Trela and Whalley (1991) also utilize an applied general equilibrium model to exaqmine the impact of rebates of direct and indirect taxes on exports, investment tax credits, and tax holidays on Korean growth performance. They conclude that tax policy accounted for less than one-tenth of the growth of the Korean economy during 1962-82. These results are, however, tentative as the model developed for this purpose did not explicitly take into account savings, investment and the accumulation of human capital. The authors nevertheless, expect these results to stand in a more complete analysis of Korean growth performance.

Feltenstein and Shah (forthcoming a, forthcoming b) examine the relative efficacy of tax incentives by using disaggregated dynamic computable general equilibrium models for Mexico and Pakistan. In both applications, they find that while the investment tax credit was more stimulative in its impact on private capital formation, corporate tax rate reductions appeared superior in terms of its impact on aggregate output and consumer welfare.

# **VI.** Concluding Remarks

ITis has been a rather wide-ranging survey of some of the effects of tax incentives on the decision to invest in developing countries. Much of the discussion has been of a conceptual nature. This reflects the fact that the investment decision is inherently a rather complicated one which is not completely understood. There are obviously many factors which affect the decision to invest. Only some of them are conventional price and income effects that economists are used to analyzing. Our analysis has concentrated heavily on these effects.

At the same time, there are many more intangible influences on the decision to invest, many of them specific to a given country. These include the political clinate, the reliability of fiscal commitments, capital markets and the availability of cash, and both economic and political uncertainty. It is very difficult to capture all of these factors in an analytical framework, though some advances have
**Box 1. Educational Levels, Unemployment and Re-Employment.**

We have already pointed out that unemployment is highest among those with low educational levels, and lowest among college graduates. In addition, the number of unemployed workers per vacancy is much larger for those with low education than for workers with secondary or college studies. Such imbalances are the product of a skill mismatch between labor supply and labor demand.

|**TableB **|**1.INumbero **|**funemployed**|**workersperv **|**cancy,byed **|**cation**groups.||
|---|---|---|---|---|---|---|
||**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|
|**Total**|50|28|22|23|18|39|
|None|152|73|77|162|28|80|
|Basic|146|137|120|95|51|131|
|**Apprent.low**|20|10|11|12|11|23|
|**Vocationallow**|87|73|48|23|35|29|
|**Apprent.com.**|75|29|27|19|26|45|
|**Vocationalcom.**|42|24|19|29|17|61|
|**Grammar**|56|363|10|27|19|28|
|**University(+)11**||7|_5_|6|4|10|

**Source:** National Labor Office.

All this implies that workers with low levels of education are hard to re-employ in the present economic context. In other words, their probability of exit to employment is really low, as can be seen in the following graph: <!-- Start of picture text --> Graph B1.l<br>Exit-to-employment probability, by education levels<br>o.s~~~~~'' f=- .. NXX<br>0.9 A.-.<br>0.8<br>0.7 5 - ___<br>0.7 - ___<br>06<br>04<br>Durstion of spell (in ntonths)<br><!-- End of picture text -->

**Source:** Unemployment Registry

These results, together with the evidence we have presented in this section, constitute a strong case in favor of reforms of the education system (i.e. revision of curriculum, etc.), as well as in favor of retraining programs that take into account employers' needs in terms of skills.

29
POLI`Y RFSFARCH WORKING PAPER 1604

# Summary findings

This paper, the first in a series of project reports, briefly describes the background of this research project, which was conducted as a sequel to an earlier study on Nigeria.

Using the fresh results from a sample survey of manufacturing establishments in Indonesia (290 establishments) and Thailand (300), the authors contrast and compare the findings from these new data with data from an earlier study of Nigeria. They compare especially:

* 'The extent and incidence of public infrastructure deficienicies.

* T'he extent of manufacturers' private provision of infrastructure in responise to such deficiencies.

* The capital shares of various private infrastructure investments, inciliding electric power, water, telecommunications, transport, and waste disposal.

The extent of public infrastructure deficiencies and private provision of infrastructure services varies across countries and by firm size. For example, 92 percent of Nigerian firms had their own generators to supplement the inadequate public supply; only 66 percent of firms did in Indonesia, and only 6 percent in Thailand. But the quality of electric power in Thailand was not much different from that in Indonesia.

The total share of capital investment in private infrastructure was 16 percent of all capital in Indonesian firms, about the same as in Nigerian firms (14 percent), which was twice that in Thai firms.

The private costs of infrastructure deficiences are substantial and the burdens are much greater on small firms than on large firms.

* The firms' costs for producing their own electricity and water.

- - This paper a product of the Infrastructire and Energy Division, Operations Evaluation Department was prepared as part of a World Bank research project on "Infrastructure Bottlenecks, Private Provisions, and Industrial Productivity: A Study of Indonesian and Thai Cities" (RPO 676-71). It was jointly funded by USAID, Jakarta. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington DC 20433-0001. Please contact Stacy Ward, room G6-132, telephone 202-473-1707, fax 202-522-3 125, Internet address sward(aworldbank.org. May 1996. (35 pages plus 75 pages of tables)

_The Policy Research Working Paper Series_ **_disseminates the findings_** _of_ **_work in progress to encourage the exchange_** _of_ **_ideas about_** _development issues. An objective of the series is lo get_ **_the_** _findings out quickly, even ifihe presentations are less than_ **_fully_** _polished. The papers carry the names of the authors and should be used and cited accordingly. The findings, interpretations, and conclusions are the authors' oun and should not be attributed to the World_ **_Bank,_** _its Executive Board of Directors, or any of_ **_its_** _member counfrnes._ Produced by the Policy Research Dissemination Center
20 positions in the private sector. On the other hand, study at STAN is tuition-free in exchange for a commitment to join either the Ministry of Finance or to assume an accounting role at one of the other ministries, should a position be offered to the students. Students who are offered a position and turn it down are required to repay their tuition. All STAN students intend to join the public sector; nearly all do so.

**Table 4:  Sample differences between public and private workers**

|**Variable**|**University**<br>**of**<br>**Indonesia**|**State College**<br>**of**<br>**Accountancy**|**Sig.**|
|---|---|---|---|
|Observations|650|1073||
|Risk Gambles|3.26|3.39|*|
|Effectiveness of the Charity|5.18|5.50|***|
|Age|19.75|20.35|***|
|Female (%)|53%|23%|***|
|Religious Attendance|4.12|4.24|**|
|Personal Finances|2.48|2.60|***|
|Family Income|2.76|2.87|**|
|Fairness of Others|4.56|5.10|***|
|Trust in Others|3.73|4.08|***|
|Prepared to take Risks|2.55|2.75|***|
|Accounting Major (%)|32%|36%|**|
|Want to Join the Public Sector (%)|56%|97%|***|
|Procedures:||||
|Privacy Preserved (%)?|97%|97%||
|Clarity of Instructions|4.20|4.20||
|Clarity of Activity|4.18|4.19||
|Clarity of Survey|4.04|4.13|**|
|Charitywas Paid|4.09|4.29|***|

Note: * p<0.1, ** p<0.05, *** p<0.01. 2-sided t-tests (proportions tests for percentages).

The essentially iron-clad commitment to join the public sector is the main distinguishing feature between STAN and the University of Indonesia. For example, entry into both is highly competitive and both are prestigious, as students in both places affirmed.<sup>16</sup> Because neither group of students has actually worked in either the private or public sectors, we can attribute any

> 16 For example, a student at UI, who had also been accepted at STAN, indicated that he chose UI over STAN because he did not want to join the public sector.
in means. Our estimated models include all the covariates used to estimate the notional wage distribution, as described in section 4.1, and two additional sets of dummy variables. Since we now pool all years in the estimation, we include time dummies to capture any macroeconomic effects associated with wage rigidities. Additionally, we include four indicator variables to capture the quartile distribution of worker wage levels on in the base year. Note that DNWR is, in principle, related to the expected wage growth of individuals, but not their wage levels. In contrast, to the extent that DRWR is related to increases in the minimum wage, as seems to be the case in the first years of the sample in Brazil, its effects are expected to be more visible for low-earning workers. However, wage indexation associated with the minimum wage often extends beyond those workers strictly earning the minimum -the so-called numeraire effect (Cunningham, 2007). Moreover, both DNWR and DRWR associated with price changes are more likely to be binding for high-wage workers if insider-outsider mechanisms are at play.

# 5.3 Results

Tables 6 and 7 show OLS regressions of the probability of being subject to each type of rigidity as a function of worker and firm characteristics in Uruguay and Brazil, respectively. In both countries, wage rigidity rises with age and tenure at a decreasing rate, over the study period, as evidenced by the positive signs of the main effects and negative signs of the quadratic terms. Comparing a 50-year-old worker with a worker aged 25, the estimated probability of being subject to DNWR (DRWR) increases by 0.5pp (2pp) in Brazil, and by 0.7pp (2.7pp) in Uruguay. The estimated results for tenure are similar across both types of rigidities in the two countries, suggesting a difference between a worker with 15 years of tenure in a firm and one with 5 years of tenure in the range from 0.9 to 1.6pp. Both of these features are in line with insider-outsider considerations. This evidence appears in contrast to previous evidence for the US, where in a survey of firm managers, Campbell and Kamlani (1997) find strong support for explanations of DNWR based on efficiency wage considerations and adverse selection, but little evidence in favor of the insider-outsider theory. Interestingly, gender, education, and type of contract seem to have little impact on the probability of being subject to either type of 22
**Table A6: Counterfactual change in men’s LFPR: Varying elasticity** Labor market σσ

||Labor<br>on|market<br>ly|Human<br>on|capital<br>ly|Preferen|ces only|A|ll|
|---|---|---|---|---|---|---|---|---|
||1995|2005|1995|2005|1995|2005|1995|2005|
||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|(7)|(8)|
|**Panel A: Labor for**<br>|**ce participatio**|**n rate, diff**|**erence over**|**2015: men**|**aged 25-34**|**(pp)**(σ= 2)|||
|Costa Rica|-3.3|-0.5|3.1|1.9|-0.1|<br>-1.5|2.0|0.7|
|El Salvador|0.4|0.1|0.3|0.0|7.1|<br>6.0|7.2|6.0|
|Panama<br>|-3.3<br>|-2.2<br>|3.2<br>|2.8<br>|1.6<br>|1.2<br>|3.1|2.7|
|**Panel B: Labor for**<br>|**ce participatio**<br>|**n rate, diff**<br>|**erence over**<br>|**2015: men**<br>|**aged 25-34**<br>|**(pp)**(σ= 4)<br>|||
|Costa Rica|-2.4|-0.4|2.0|1.3|-1.3|<br>-2.4|0.4|-0.6|
|El Salvador|0.2|0.1|0.2|0.0|7.4|<br>6.5|7.5|6.5|
|Panama|-2.0|-1.4|2.5|2.1|0.7|0.9|2.4|2.1|

# **References**

**CEDLAS, and the World Bank.** “Socio-economic database for Latin America and the Caribbean _.” Centro de Estudios Distributivos, Laborales y Sociales,_ Facultad de Ciencias Economicas, Universidad Nacional de La Plata, La Plata, Argentina; _the World Bank,_ Washington DC (2019).

**Ganzeboom, Harry BG, and Donald J. Treiman.** “Internationally comparable measures of occupational status for the 1988 International Standard Classification of Occupations.” _Social Science Research_ 25, no. 3 (1996): 201-239.

**Hsieh, Chang-Tai, Erik Hurst, Charles I. Jones, and Peter J. Klenow.** “The allocation of talent and US economic growth.” _Econometrica_ 87, no. 5 (2019): 1439-1474.

**International Labor Organization.** “International standard classification of occupations: Structure, group definitions and correspondence tables.” _International Labor Office, Geneva_ (2012).

**National Institute of Statistics and Censuses.** “Encuesta de Hogares de Propósitos Múltiples.” San Jose, Costa Rica (2009).

**National Institute of Statistics and Censuses.** “Encuesta Nacional de Hogares.” San Jose, Costa Rica (2020).

48