rafmacalaba/datause-encoder-data huggingface dataset:
- 23 -

We use a version of the t!est jproposed by White (1980) to assess the presence of heteroskedasticity.1<sup>3</sup> The tests were conduced for the OLS- estimated models of tables 2-A and 3-A, and the results are presented in table 4. The results lend strong support to the null hypothesis of no heteroskedasticity: out of the 20 tests reported in table 4, the null hypothesis was only rejected at the 5 percent significance level in two cases. Furthermore, the differences between the White heteroskedasticity-robust standard errors and their conventional counterparts were minor. Therefore, we decided to accept the null hypothesis of no heteroskedasticity.

Based on the assumption of no heteroskedasticity, we tested for the endogeneity of the black market premium and for both the premium and the domestic rate of inflation.<sup>14</sup> We conducted a series of sequential tests using a Hausman (1978) type metric.<sup>15</sup> First we tested for the endogeneity of the premium alone (i.e. assuming exogeneity of inflation). The endogeneity of inflation was tested 13 This test is an extension of White (1980) to the case of panel data, as noted in Chamberlain (1982). The test is given by 17R<sup>2</sup> _ X2 _where R_<sup>_2_</sup> is based on the regression of the squared of the residuals from models (2-A) or (3-A) on the moments of the right side variables of model (1-A) (or 2-A).

14 The teots for endogeneity are substantially simplified in the absence of heteroskedasticity.

is Assuming the following linear relationship (A) y = X1BI + X2B2 + e, where X1 is a single variable which is possibly correlated with e (a homoskedastic disturbance term), while X2 are not. Given the matrix of instruments Z (which should include X2), let 21 _= P_Xl_ = _Z(Z/Z) Z'Xl,_ and consider the following expression: (B) Y = _XIB1_ + _X2 B2_ + _X.B3 +V_ Now a test for Ho: B3 = 0 (a test for the null hypothesis of no covariation between **X1** and e in (A)) is given by the t-statistic corresponding to the OLS estimator of B3.
# References

- Al-Qunaibet, M. H. and R. S. Johnston (1985) "Municipal Demand for Water in Kuwait: Methodological Issues and Empirical Results," _Water Resources Research._ Vol. 21, No. 4.

- Anderson, J. (1989) "Forecasting, Uncertainty, and Public Project Appraisal." World Bank PPR Working Paper 154. International Economics Department. Washington, D.C.: The World Bank.

- Briscoe, J. and D. de Ferranti (1988) _Waterfor Rural Communities._ Washington, D.C.: The World Bank.

- Churchill, A. A. (1987) "Rural Water Supply and Sanitation--Time for a Change." World Bank Discussion Paper No. 18, Washington, D.C.: The World Bank.

- Comes, R. and T. Sandler (1986) _The Theory of Externalities, Public Goods, and Club Goods._ Cambridge, MA: Cambridge University Press.

- Gomez, C. (1987) "Experiencesin Predicting WilingnesstoPay for WaterProjects in LatinAmerica."TechnicalNotes No. 2. Washington, D.C.: Inter-American Development Bank.

- Harrington, W., A. J. Krupnick, and W. 0. Spofford, Jr (1989) "The Economic Losses of a Waterbome Disease Outbreak." _Journal of Urban Economics._ Vol. 25, No. 1.

- Johansson, P. (1987) _TheEconomic Theory andMeasurementofEnvironmentalBenefits._ Cambridge, UK: Cambridge University Press.

- Little, I. M. D. and J. A. Mirrlees (1990) "Project Appraisal and Planning Twenty Years On." Proceedings of the World Bank Annual Conference on Development Economics, 1990. Washington, D.C.: The World Bank.

- Martin, W. E. and J. F. Thomas (1986) "Policy Relevance in Studies of Urban Residential Water Demand." _Water Resources Research._ Vol. 22, No. 13.

- Powers, T. (1977) "Guide forAppraisingUrban WaterSupply Projects." Papers in ProjectAnalysis No.4. Washington, D.C.: Inter-American Development Bank.

- Powers, T. and Valencia, C. (1980) "SIMOP Urban Water Model. User Manual. A Model for the Appraisal of Water Projects in Urban Areas." Papers in Project Analysis No.5. Washington, D.C.: Inter-American Development Bank.

- Renzetti, S. (1988) "An Econometric Study of Industrial Water Demands in British Columbia, Canada," _Water Resources Research._ Vol. 24, No. 10.

- Whittington, D., J. Briscoe, and X. Mu (1987) "Willingness to Pay for Water in Rural Areas: Methodological Approaches and an Application in Haiti." WASH Project, Field Report No.213. Washington, D.C.: USAID.

- Whittington, D. (1988) "Guidelines for Conducting Willingness to Pay Studies for Improved Water Services in Developing Countries." WASH Technical Report no. 56. Washington, D.C.: USAID.

- Whittington, D., X. Mu, and R. Roche (1989) "The Value of Time Spent on Collecting Water: Some Estimates for Ukunda, Kenya." INU Report no. 40. Washington, D.C.: The World Bank.

- World Bank (1990) "FY90 Sector Review: Water Supply and Sanitation." Infrastructur. and Urban Development Department, PRE. Washington, D.C.: The World Bank.

14
The stock market plays a useful and important role as a signalling device for managers. This finding in turn provides a powerful rationale for financial sector reforms and capital market development in developing countries.

However, in practice, the application of the Q model to developing countries has been limited, given the rather exacting data requirements for the computation of the Q ratio, following the methodology outlined in Salinger and Summers (1983). In particular, it is difficult to estimate the replacement cost of the firm's capital stock precisely and one has to use the book value instead. Given these data limitations, testing of investment theories in developing countries have been confined to accelerator, neoclassical, and cash flow theories of investment.<sup>2 5</sup> Once firm-level data becomes available for developing countries, it would be interesting to replicate the analysis done in this paper to test the robustness of these results.

The findings in the paper with regard to the market timing hypothesis are also interesting. The results suggest that managers do pay attention to the movements in the Q ratio while issuing equity. In particular, firms tend to issue equity when the level of asymmetric information is low, as for instance during bull markets. There is some evidence from India that supports this market timing hypothesis. Reserve Bank of India's (RBI (1995)) survey of public response to capital issues for the 1986-87 to 1990-91 period suggest that there was an increasing tempo in the activities of the capital market during this period, when there was an overall boom in the Indian stock market.<sup>26</sup>

**25** Athey and Laumas (1994) found support for the cash flow theory of investment for India. Likewise, Nabi (1989) and Tybout (1983) found support for the cash flow theory for Pakistan and Colombia respectively. Also, Bilsborrow (1977) found support for accelerator and cash flow theories using panel data for manufacturing firms in Colombia.

' See RBI (1995) for more details.

28
**Table** **<u>3: State Ranking for each Category of Indicators and the YWI</u>**

|State|Region*|YWI<br>Rank|Ranki|ng of S<br>the|ub-Cate<br>YWI|gory of|Socioeconomic<br>Rank<br>|
|---|---|---|---|---|---|---|---|
||||Health|Behavior|School<br>performance|Institutional<br>connectedness|(not used to<br>generate the<br>YWI)|
|DistritoFederal|CW|1|17|3|4|1|1|
|Santa Catarina|S|2|22|20|1|2|5|
|Goiás|CW|3|3|1|11|13|10|
|SãoPaulo|CE|4|21|24|2|3|3|
|Minas Gerais|CE|5|1|17|5|14|11|
|Rio Grande do Sul|S|6|27|21|3|11|4|
|Tocatins|N|7|10|2|15|19|22|
|Roraima|N|8|2|14|14|8|13|
|Rondônio|N|9|11|7|10|24|12|
|Mato Grosso|CW|10|4|12|12|21|9|
|Mato Grosso do Sul|CW|11|9|18|9|15|7|
|Paraná|S|12|18|19|7|7|6|
|Pará|N|13|6|4|17|20|19|
|Rio de Janeiro|CW|14|14|27|6|6|2|
|Ceará|NE|15|15|6|16|5|23|
|Sergipe|NE|16|13|10|22|12|20|
|Rio Grande do Norte|NE|17|12|13|19|4|14|
|Espírito Santo|SE|18|8|26|8|18|8|
|Acre|N|19|24|16|18|16|16|
|Bahia|NE|20|7|5|24|17|24|
|Paraíba|NE|21|20|9|25|9|21|
|Piauí|NE|22|16|15|21|10|26|
|Amazonas|N|23|5|11|23|27|17|
|Maranhão|NE|24|19|8|20|26|27|
|Amapá|N|25|23|23|13|22|15|
|Alagoas|NE|26|26|22|27|25|25|
|Permanbuco|NE|27|25|25|26|23|18|
|correlationwith YWI|||0.30|0.22|0.81|0.55|0.68|

***** CW=Center West, CE = Center East, NE = Northeast, N = North, S = South

20
on average the largest debtholder held almost one quarter of the firm's outstanding debt and over 5% of the firms outstanding equity, while the largest 5 debtholders of the firm held over half of the firm's debt and almost 20% of its equity. In contrast, Clyde (1989) finds in a sample of U.S. Fortune 500 companies that in only a few cases do any of the largest 5 shareholders hold any of the firm's debt and that in these few cases the debtholdings of the largest shareholders are minuscule.

Table 4 Gross funding of non-financial corporations, 1970-85 As a percentage of total gross financing

|Retentions<br>......<br>................|52|76|
|---|---|---|
|External finance .48||24|
|of which:|||
|Intermediated debt|41|21|
|Securities<br>.7||3|

Note: Total gross financing excludes trade credit and some overseas financing. Intermediated debt refers to loans from financial institutions. Securities includes public equity and short and long-term bills and bonds.

Sources: OECD Financial Statistics, Part Ill and national data.

# _Marketfor corporate control_

Another dramatic difference between the U.S. and Germany or Japan is the frequency of mergers and acquisitions. The market for corporate control is much less active in Japan and Germany. Data on the volume of completed domestic merger and acquisition transactions for the second half of the 1980's is displayed in table 5, which reveals large differences between countries. Part of the reason for the greater merger activity in the U.S. is of course the larger number of companies listed on the U.S. stock market. However, even normalising the dollar value of mergers and acquisitions by stock market capitalisation fails to alter the impression that the merger market is much more active in the U.S.--15 to 20 times more so according to table 5.8 The differences across countries in the frequency of hostile take-overs are even more striking.<sup>9</sup> Since WWII, for example, there have only been 4 successful hostile take-overs in Germany.<sup>10</sup> They appear almost as rare in Japan. Kester (1991) claims that the use of take-overs as a device for replacing inefficient management in large Japanese firms is very infrequent. Conversely, in the U.S. almost 10% of the Fortune 500 in 1980 has since been acquired in a transaction that was hostile or started off as hostile.

8 Other empirical evidence supports the claim that mergers and acquisitions in Japan are far lower than in the U.S.. Kaplan (1993a) finds that just over 2% of his sample of large Japanese firms are taken over or merged between 1980-89 in contrast to over 22% of his sample of large U.S. firms.

9 Hostile take-overs are thought to be motivated by corporate control considerations to a greater extent than friendly mergers.

I10 See Franks and Mayer (1993).

7
In this paper, we concentrate on three critical elements underlying the 2007-2009 financial turmoil: capital regulations and holdings, market discipline and banking supervision (Caprio et al., 2010). We define capital regulations as quantitative capital requirements and the degree of stringency in the definition of regulatory capital. Market discipline captures the capacity and incentives of market participants to monitor and influence bank health. We show that on average the level of regulatory capital held by banks was higher by the end of 2016 than at the end of 2010. The levels of the highest loss-absorbency types of capital had also increased over time, indicative of a positive effect brought about by the new Basel III capital framework and regulatory capital reforms enacted at the country level. Many countries also introduced a binding leverage ratio to limit the adverse effects of financial leverage on bank stability. We take these developments as good signs since previous analysis using BRSS data from 2010 (round 4) showed that bank capital ratios were lower in countries that experienced a crisis in 2007-2009 than in others (Cihak et al. 2013). Those results also showed that incentives for the private sector to monitor banks’ risk were weaker in countries that experienced a crisis.

However, and contrary to the positive trends for capital ratios, the definition of capital became less stringent since a higher percentage of countries allow for the inclusion of hybrid debt capital instruments, asset revaluation gains and subordinated debt in calculating tier 1 capital.<sup>2</sup> We also document that 115 countries have now introduced a formal deposit insurance system (compared with 86 in 2010) and that in some instances the schemes have become more generous than before the crisis. This may have dampened the monitoring incentives of depositors. Furthermore, we present some descriptive evidence that points to an increase in the complexity of bank supervision that has not been met by a comparable increase in the number of bank supervisors, in their training nor experience.

> 2 According to the definition of the Basel Committee on Banking Supervision, tier 1 capital comprises Common Equity Tier 1 (CET1)— the highest quality component of capital, essentially common shares and retained earnings—or additional Tier 1 instruments (AT1)—other regulatory capital instruments that meet the criteria for inclusion in the Tier 1 capital (Basel Committee on Banking Supervision, 2011).

2
eligibility threshold of this program<sup>34</sup> for the whole sample or when broken down by gender and location. Second, and related to the continuity checks discussed above, there is no evidence of discrete changes in the distribution of other observable dimensions beyond the probability of participation in the program that could indicate some degree of manipulation of the scoring and ranking system.

Another issue is the possibility of nonrandom migration and crossover. In theory, families in control areas could try to migrate to treatment areas to become eligible for the program. This may affect the internal validity of the analysis if, for instance, families that are poor – and perhaps systematically different from non-migrants in other dimensions – are more likely to engage in this type of migration. Similarly, parents that live in control areas may prefer to send their children to schools that serve program beneficiaries if they are perceived to be of higher quality and are within their geographical reach. There are, however, a number of reasons to think that migration and crossover effects of this sort are unlikely to invalidate the findings of this paper. First, migration from control to treatment municipalities is close to zero among the baseline households examined in the PSM approach and that were resurveyed in the two subsequent rounds of longitudinal data collected in 2003 and 2005. Second, as noted above, part of the control group used for the RD analysis is comprised of children and adolescents that are above the cutoff of eligibility but reside in the same municipalities than participating children. Third, the economic incentives induced by the amount of the transfer (around $7-14 per eligible child) are probably not enough to compensate for the direct and indirect costs of migration. Fourth, _Acción Social,_ the national agency that administers the program opens calls for inscription and enrollment into the _FA_ program only on specific dates for each municipality, making it impossible for people to join the program at other times.

Problems of misspecification of the underlying regression models may introduce bias in treatment effects, particularly in the RD design. In order to avoid such problems, we checked the sensitivity of program impacts using various alternative specifications. Overall, the existence or lack of program effects on school completion and test scores,

> 34 Camacho and Conover (2011) find evidence of manipulation for the _Sisben_ proxy-means test constructed with the same census of the poor. However, this problem occurs only for people around the level 2 threshold which determines eligibility for other social programs such the Subsidized Health Insurance ( _Régimen Subsidiado_ ).

29
# 1 Introduction

One of the intriguing results of the Cop 15 (Copenhagen climate change conference 2009) is the apparent contradiction between the absence of a firm commitment on country or regional targets and the confirmed 2 � C objective, i.e. the stabilization of global warming below a 2 � C rise in global temperature. Obviously, this target is surrounded by ambiguity about whether it is a strict ceiling or a long term objective with some degree of overshooting allowed in between. The reference to that threshold originates in a visual reflex, a 550 ppm target fitting just in the middle of the IPCC SAR figure displaying various scenarios between 350 ppm and 1000ppm. This target was also supported by the intuition that it would not lead to extreme mitigation costs, given published results from economic models of the time.

This 550 ppm target was supposed to be tight enough to keep temperature increase below two degrees above pre-industrial temperatures. But, some years later, the assessments of climate sensitivity became more pessimistic and it seems unlikely that a 550 ppm target will secure a 2 � C objective and the delay in mitigation actions after the semi failure of international climate negotiations since the Kyoto Protocol makes it far more difficult to reach significantly tighter targets like 380 ppm.

But, the gap is obvious between, on the one hand, this political claim and, on the other hand, the published economic literature that relies on a cost-benefit analysis of climate policies (Tol, Mendelsohn, Nordhaus, etc.) or even the Stern report that pleads in favor of early action but does not appear to support firmly the 2 � C objective.

The overwhelming majority of tenants of the 2 � C target can be found in scientific and intellectual circles that reject the very idea of CBA in these matters. Some start from an ethical rejection of a full monetization of climate change damage (value of life, intrinsic value of irreplaceable assets), but many simply question the credibility of such estimates given the cascade of uncertainty between GHGs emissions and ultimate damage. In both cases, they prefer to refer to the search for “safe corridors” normatively fixed from qualitative insights provided by integrated models.

In a companion paper (Hallegatte et al., 2009), we analyze the many reasons why, beyond a 2 � C temeprature increase, the uncertainty may become so high that it would generate very high adaptation costs and significant residual damage. With a temperature increase higher than 2 � C, the climate system enters in a domain where the validation of climate models becomes less reliable, making their projections more uncertain. Also, the ecosystem reaction is not well understood for large warming. Finally, the difficulty in mobilizing the full capacity of adaptation potentials in case of very fast envi2
**Table S4.9.** ITT effects on number of adopted ISFM technologies and integrated adoption of the full ISFM package, FD- and non-FD samples separately.

|||OL<br>|Numb<br>S<br>|er of ISFM te<br>|chnologies a<br>|dopted<br>Poi<br>|sson<br>||Int<br>|egrated adop<br>|tion of full pa<br>|ckage<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Non-FD|sample|FD sa|mple|Non-FD|sample|FD s|ample|Non-F|D sample|FD s|ample|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|T1 (extension)|0.340*|0.301***|1.133***|1.206***|0.348*|0.308***|1.127***|1.208***|0.045|0.041*|0.227***|0.237***|
||(0.192)|(0.077)|(0.187)|(0.137)|(0.193)|(0.085)|(0.179)|(0.141)|(0.046)|(0.025)|(0.059)|(0.049)|
|p-value|0.079|0.000|0.000|0.000|0.072|0.000|0.000|0.000|0.331|0.093|0.000|0.000|
|T2 (extension + video)|0.573***|0.521***|0.954***|0.943***|0.563***|0.510***|0.973***|0.950***|0.074|0.074***|0.249***|0.247***|
||(0.174)|(0.085)|(0.201)|(0.128)|(0.166)|(0.091)|(0.198)|(0.134)|(0.045)|(0.026)|(0.070)|(0.044)|
|p-value|0.001|0.000|0.000|0.000|0.001|0.000|0.000|0.000|0.100|0.004|0.000|0.000|
|Test T1 >= T2 (p-value)|0.135|0.006|0.789|0.954|0.135|0.010|0.790|0.961|0.289|0.130|0.388|0.427|
|Endline control mean|2.4|65|2.7|89|2.4|65|2.7|89|0|.167|0.|227|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.034|0.503|0.172|0.545|0.006|0.094|0.023|0.075|0.006|0.282|0.053|0.310|
|Observations|1,616|1,616|388|388|1,616|1,616|388|388|1,616|1,616|388|388|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson and probit models (columns (5) to (12)) show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 5. Integrated adoption of full ISFM package is a dummy variable. FD stands for field day. FD- and non-FD samples consist of actual field day participants respectively non-participants in treatment mws, and their matched controls. Additional control variables identical to those listed in notes of Table 2. One-sided equality tests of T1 and T2 are F-tests or Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

17
Figure 3: Maintenance Expenditure<sup>15</sup> <!-- Start of picture text --> 280<br>260<br>240<br>220<br>200<br>180<br>160<br>140<br>120<br>100<br>1994 1996 1998 2000 2002 2004<br>Mean Median<br><!-- End of picture text --> _Source: Benfratello et al. (2006). The base year for the two series is 1994=100._ In 2007, Autostrade released a report in which several quality and safety indicators were analyzed, in order to quantify the private concession’s effect on the service improvement. With the sole exception of the mortality rate, which refers to the whole highway system, the other variables are specific for the Autostrade concessionaire. We can generally affirm that the quality of infrastructure has increased, because all variables included show a remarkable variation between 1999 and 2005. However, because there is no independent report measuring the same variables or including quality-oriented indexes, final judgment on the matter must be suspended.

Table 3: Quality and Safety Indicators for Autostrade Group<sup>16</sup>

|**Variable**|**Unit**|**1999**|**2005**|**2006**|
|---|---|---|---|---|
|**Mortality rate**|Death/100 Mln km|1.1|0.60|0.60|
|**Draining paving**|Mln m<sup>2</sup>|2.6|9.4|13.5|
|**Network cover with draining asphalt**|% total km|16%|55%|69%|
|**Median strip (annual substitution)**|km|0|76|300|
|**Network cover with qualified median strip**|% network|55%|66%|76%|
|**Electronic information panel at entrance**|Number of total panels|102|323|401|
|**Electronic information panel on**|Number of total panels|151|326|355|
|**Call-center mobility dedicated line**|Number of lines|12|120|120|

_Source: Autostrade, 2007, page 21._

> 15 Source: Benfratello and al, (2006), the base year for the two series is 1994=100.

> 16 Autostrade, 2007, page 21.

8
# CONTENTS

||Page|
|---|---|
|I.|INTRODUCTION .<br>. . . . . . . . . . . . . . . . . . . . . . . . . .<br>1|
|II.|THE ANALYTICAL APPROACH AND DATA .<br>. . . . . . . . . . . . . . . .<br>2|
||Selection into Training .<br>. . . . . . . . . . . . . . . . . . . .<br>5|
||The Employment Effects of Training .<br>. . . . . . . . . . . . . . .<br>6|
||The Earnings Effects of Training ... . . . . . . . . . . . . .<br>.<br>7|
||The Data .<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>9|
|III.|OVERALL PATTERNS OF TRAINING . . . . . . . . . . . . . . . . . . .<br>'.1|
|IV.|SELECTION INTO TRAINING . . . . . . . . . . . . . . . . . . . . . .<br>19|
|V.|THE EMPLOYMENT AND EARNINGS EFFECTS OF TRAINING . . . . . . . . . .<br>26|
||'The Employment Effects of Training . . . . . . . . . . . . . . . .<br>26|
||The Earnings Effect of Training<br>. . . . . . . . . . . . . . . . .<br>31|
|VI.|CONCLUSION<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>39|
|REFER|ENCES<br>. . . . . . . . . . . . . . . . . .<br>. . . .<br>.<br>. . . . . . . .<br>41|
|ANNEX|ES. . . . . . .<br>. . . . . .<br>. .<br>. .<br>.<br>. . . . . . . . . . . . . . . .<br>44|