rafmacalaba/datause-encoder-data huggingface dataset:
paper, we examine whether internal monitoring through independent boards and stronger shareholder protections can substitute for external monitoring through the use of short-term debt.

Past research suggests that leverage and debt maturity structure choices could be effective in mitigating agency conflicts (Grossman and Hart, 1982; Jensen, 1986; Stulz, 1990; Hart and Moore, 1995). Good corporate governance and independent boards are also expected to be effective in mitigating agency problems (Bhagat, Carey, and Elson, 1999; Yermack, 1996; Gompers, Ishii, and Metrick, 2003; Klein, 2002). In addition, the law and finance literature suggests that the extent to which debt contracts can be used to mitigate agency problems depends on the institutional environment, and in particular the laws surrounding creditor and investor rights and the quality of enforcement of these laws (La Porta et al., 1998 and 2000; Diamond, 2004; Burkart et al., 2003).

Given the trade-off between monitoring benefits and liquidity risk associated with shortterm debt, we conjecture that firms in countries with better legal protection of shareholders would reduce the need for costly external monitoring using short-term debt. If strong investor protection reduces discretion of insiders and allows for more efficient internal monitoring, then we would expect governance and short-term debt to act as substitutes. An independent board trying to maximize shareholder value, for instance, would choose lower levels of short-term debt if the benefit from external monitoring is low.

To empirically test these conjectures, we gather a large firm-level data set consisting of about 8,453 firms from 23 different countries. Our data set includes a wide range of governance attributes for these firms over the 2003 – 2007 period. These governance attributes, which are collected and maintained at the International Shareholder Services (ISS) database include a wide coverage of key governance features, such as board characteristics, managerial and director compensation, executive stock ownership, auditing practices and shareholder rights. Using these governance attributes, we construct a number of governance related variables that are likely to reflect board and governance qualities of firms. Our set of variables includes the overall governance index that provides a holistic view of firm-level governance quality (Aggarwal et al., 2009; 2011), board quality, board independence, board size and CEO-chairman duality. In the extended analysis, we supplement this list with variables related to the independence levels of compensation, nomination and audit committees.

3
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# **4.1 Are post-conflict projects more successful than others?**

The first column of Table 3 includes all our control variables and is run on the whole sample of World Bank projects, excluding projects in war-torn countries. We use an ordered probit model using the IEG success rating from 0 to 5. The use of an ordered probit enables to not lose the information contained in the discrete variable. However, interpretation of the results is a lot less intuitive than for a probit, which is therefore preferred. Table 10 reports the marginal effects from regression (1) for each outcome value. Coefficients change signs between the project being _moderately successful_ and _successful_ thus confirming the appropriateness of our binary variable definition. Column (2) shows the results of the probit model. The signs and the significance of the coefficients are not affected by the change of model. The sample includes projects started in a post-conflict environment and during which the war broke out again. Introducing a dummy variable for a potential return to war shows that these projects are not significantly different from the rest of the projects in the sample (Table 9, column (1)). Decade, region, as well as sector dummy variables are introduced in all the regressions of Table 3, their coefficients are not reported. The standard errors are also clustered by country.

Column (2) includes the ICRG index as a control. The coefficient is negative, as expected, but not significant. Nevertheless, the introduction of the ICRG in the regression leads to a significant loss of observations. Indeed, the ICRG is only available since 1984. For this reason, we decide to drop it from the regression. Column (3) reports the results of the basis analysis.

The results suggest that the longer the project, the smaller the chance of success. On the contrary, better preparation and supervision significantly increase the probability of success of World Bank projects. Yet the impact of the supervision quality is greater than the one of the preparation.<sup>5</sup> Investments are nine percent more likely to be successful while IDA projects have an eleven percent higher probability to be successful than IBRD projects.

Regarding country-level characteristics, income growth and GDP per capita scores are significantly different from zero. Both increase the chances that World Bank projects will succeed, suggesting that higher level of development, combined with economic growth are favorable to aid projects.<sup>6</sup> In the fourth column of Table 3, we introduced interaction terms between INIT ~~P~~ C and the project characteristics. This enables a first step of comparison between post-conflict and peace environments.

> 5We cannot reject the hypothesis that the coefficients of preparation and supervision are different (p-value = 0.016).

> 6We also introduced the ratio of aid over GDP as well as the dependency on fuel exports. Both had no impact on the probability of success of the project. We thus decided to drop them from the regression as they led to a loss of observations, see Table (9) column (2). Note that aid/GDP is significant but only at 10 percent

11
30

# Figure L1 Investment and Tax Rates for 111 Countries: 1970-80

<!-- Start of picture text --> .405 0<br>0<br>0<br>o 0 0<br>0<br>0. 0 0 0 0 0 0<br>0 00 0 oooo o0o o 8 0 000<br>n o90 0 0<br>vO o ° 0<br>0 ~000<br>0 0 : o 0<br>.049 0<br>.053 Average Tax Rate 1970-80 .431<br><!-- End of picture text --> Note: Investment is defined to be the ratio of gross investment to GDP, averaged over 1970-80. Real data used from Summers and Heston (1984).
## **Table 7.  Decomposition' of Inequality: Share of between and within group inequality in Theil entropy index**

# **A. By location (for inequality measured by consumption per capita)**

||**Lat**|**via**|**Hun**|**gary**|**Pol**|**and**|**Rom**|**ania**|**Ru**|**ssia**|**Mol**|**dova**|**Tajik**|**istan**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1998|2002|1993|2002|1998|2002|1998|2002|1997|2002|1998|2002|1999|2002|
|**Theil entropy measure**|**0.198**|**0.254**|**0.149**<br>**Decom**|**0.126**<br>**position**|**0.206**<br>**of Thei**|**0.217**<br>**l inequal**|**0.167**<br>**ity meas**|**0.178**<br>**ure :**|**0.205**|**0.186**|**0.240**|**0.209**|**0.142**|**0.190**|
||**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|
|Of which between locations|6%|16%|5%|3%|5%|7%|3%|11%|3%|4%|9%|9%|3%|4%|
|of which within Capital city|37%|50%|30%|19%|5%|14%|||8%|7%|22%|27%|9%|15%|
|of which within other urban areas|34%|19%|36%|49%|58%|51%|56%|58%|57%|70%|18%|14%|18%|20%|
|ofwhichwithin ruralareas|23%|16%|28%|29%|31%|28%|42%|31%|32%|19%|50%|51%|69%|62%|
||||**Theilen**|**tropy in**|**dex for **|**per capit**|**aconsu**|**mption**|||||||
|Capital city|0.189|0.241|0.194|0.127|0.187|0.266|||0.182|0.178|0.212|0.214|0.151|0.217|
|Other urban areas|0.189|0.177|0.124|0.124|0.198|0.195|0.157|0.160|0.177|0.181|0.230|0.176|0.158|0.213|
|Ruralareas|0.184|0.189|0.125|0.116|0.189|0.190|0.170|0.154|0.258|0.168|0.218|0.184|0.131|0.169|
|||||**P**|**opulatio**|**n shares**|||||||||
|Capital city|33%|38%|19%|17%|4%|8%|||6%|6%|17%|18%|6%|9%|
|Other urban areas|37%|32%|43%|48%|55%|52%|55%|54%|68%|68%|19%|19%|16%|18%|
|Ruralareas|31%|30%|38%|35%|41%|40%|45%|46%|27%|26%|63%|63%|78%|73%|
||**R**|**eal mea**|**ns, relati**|**ve to nat**|**ional me**|**an per c**|**apita co**|**nsumptio**|**n=1.00**||||||
|Capital city|1.193|1.357|1.251|1.149|1.420|1.386|||1.504|1.148|1.467|1.427|1.385|1.370|
|Other urban areas|0.976|0.852|0.993|1.024|1.093|1.088|1.085|1.181|0.980|1.065|0.998|0.882|1.032|0.999|
|Ruralareas|0.824|0.698|0.885|0.897|0.834|0.805|0.898|0.783|0.944|0.801|0.873|0.911|0.962|0.952|

25
are positively correlated, loan officers have an incentive to switch their focus to wealthier borrowers, unless that incentive is checked by others that compel them to seek out information on whether applicants are poor (such as random audits on the wealth of borrowers selected by agents). Based on hypothetical comparisons by loan officers between loan applicants with different personal characteristics, empirical evidence from Burundi confirms that the poor are only slightly more likely to receive a micro loan, and that the lending preferences of loan officers from non-profit and for-profit MFIs are remarkably similar in terms of the characteristics of applicants that tend to be approved (Sagamba et al., 2013).

Evidence from field experiments also indicates that incentive-based compensation schemes affect the lending patterns of credit agents and could curtail outreach to poorer clients. From an experimental study with experienced commercial bank loan officers in India, Cole et al., (2015) find that incentive schemes that penalize officers for extending loans that become delinquent induce them to invest more effort in screening and make more profitable lending decisions, but by sanctioning significantly fewer loans.<sup>5</sup> Similarly, in the Chinese context, Cao, et al. (2011) make use of an experimental design in which loan officers from rural Credit Cooperatives in Shandong were recruited to make lending decisions on randomly assigned loan files that had been previously evaluated. As in the Cole et al. experiment in India, the performance and repayment histories on the approved loans were already known. In addition to treatments that penalized loan officers for approving loans that had repayment problems (Type I errors), there were also treatments that penalized them for rejecting loans that were not delinquent (Type II errors). The findings confirm that loan rejection rates were unduly high when loan officers had only disincentives to commit Type I errors. Both experimental studies focused primarily on the incentive schemes imposed on loan officers rather than on their personal characteristics in making loan decisions, though they did use psychometric questions to assess the

> 5 Loan officers were recruited from leading private and public sector commercial banks in India to participate in an experiment to ‘approve’ or ‘reject’ loans that were drawn from a sample of small business loan applications from a large commercial lender to self-employed individuals who were first-time applicants for a formal loan. The approval decisions and repayment histories for these loans were already known, since they had occurred in the past. The experiment therefore compensated participating loan officers for correctly approving loans for which there were no repayment problems.

5
# **References**

Acharya, V., I. Drechsler, and P. Schnabl, 2013, A Pyrrhic victory? Bank bailouts and sovereign credit risk, forthcoming in _Journal of Finance_ .

Aggarwal, R., I. Erel, R. Stulz, and R. Williamson, 2009, Differences in governance practices between U.S. and foreign firms: Measurement, causes, and consequences, _Review of Financial Studies_ 22, 3131–3169.

Anginer, D., A. Demirguc-Kunt, H. Huizinga and K. Ma, 2013, How does corporate governance affect bank capitalization strategies, Policy Research WPS 6636, World Bank.

Barth, J.., G. Caprio Jr., and R. Levine, 2004, Bank regulation and supervision: what works best?, _Journal of Financial Intermediation_ 13, 205-248.

Becht, M., P. Bolton, and A. Röell, 2011, Why bank governance is different, _Oxford Review of Economic Policy_ 27, 437–463.

Berger, A., B. Imbierowicz, and C. Rauch, 2012, The roles of corporate governance in bank failures during the recent financial crisis, Bertay, A., A. Demirguc-Kunt, and H. Huizinga, 2013, Do we need big banks? Evidence on performance, strategy, and market discipline, _Journal of Financial Intermediation_ 22, 532-558.

Bushman, R., and C. Williams, 2012, Accounting discretion, loan loss provisioning, and discipline of banks’ risk-taking, _Journal of Accounting and Economics_ 54, 1–18.

Calomiris, C., and M. Carlson, 2014, Corporate governance and risk management at unprotected banks: National banks in the 1890s, NBER WP 19806.

Campbell, J., J. Hilscher, and J. Szilagyi, 2008, In search of distress risk, _Journal of Finance_ 63, 2899–2939.

Chen, C., T. Steiner, and A. Whyte, 2006, Does stock option-based executive compensation induce risk-taking? An analysis of the banking industry, _Journal of Banking and Finance_ 30, 915-945.

Demirguc-Kunt, A, and H. Huizinga, 2013, International evidence from equity prices and CDS spreads, _Journal of Banking and Finance_ 37, 875-894.

DeYoung, R., E. Peng, and M. Yan, 2013, Executive compensation and business policy choices at U.S. commercial banks, _Journal of Financial and Quantitative Analysis_ 48, 165-196.

Ellul, A., and V. Yerramilli, 2013, Stronger risk controls, lower risk: Evidence from U.S. bank holding companies, _Journal of Finance_ 68, 1757–1803.

24
deviation outcomes also account for time and ODS drawdown schedules. Untabulated results that include year fixed effects are quite similar and event study specifications in Figures 5–8 largely support our preferred specifications.

In Model (2), _β_ 1 captures the changes in country outcomes after the signature of an RTA without ODS provisions while _β_ 2 captures the differential effect from RTAs with relevant provisions. Thus, signature of RTAs that include ODS provisions will lead to an estimated _β_ 1 + _β_ 2 increase in the outcome variable.

We also consider an additional differencing dimension (i.e. a quadruple difference model) on whether the provisions are enforceable via dispute settlement mechanisms: where _DS ig_ = 1 if agreement _g_ for country _i_ features ODS provisions that are enforceable via dispute settlement.

We focus on signature, rather than entry into force of RTAs, because countries are already faced with targets under the Montreal Protocol. By signing an RTA linked to the Montreal Protocol, countries are exposed to potential RTA disputes related to Montreal Protocol noncompliance whenever the RTA enters into force. Abman and Lundberg (2020) present evidence of considerable uncertainty in the RTA negotiation process. The negotiation phase of RTAs is typically lengthy and requires multilateral assent to the negotiated treaty content—countries are not able to unilaterally conclude the negotiation phase.<sup>11</sup> We argue that these institutional features of RTAs create plausibly exogenous timing in the signature and, hence exposure to finalized RTA content. Note that our strategy here differs from Abman and Lundberg (2020) and Abman _et al._ (2021) which consider entry into force rather than signature of RTAs. Those studies focus on commitments exclusively enumerated in trade agreements rather than linkage to international environmental agreements as we consider here. For robustness, we also include in the Appendix triple-difference estimates around

> `11` More specifically, countries are not able to _successfully_ conclude the negotiation phase unilaterally. Countries can unilaterally withdraw from negotiations which can, in many circumstances, terminate the negotiation phase and future prospects for the RTA.

14
_HOTTER PLANET, HOTTER FACTORIES_ _5_ outdoor workplace setting of data collection is different from most production activities in non-agriculture sectors. Our study builds on and contributes to this literature.

This paper makes two main contributions to the literature. First, it employs data from a global, standardized, and comparable survey of firms rather than an individual country. This presents opportunities to understand the potentially heterogeneous impacts of temperature change on firm productivity and allows the estimation of impacts across regions, climate zones, industries, and country income groups. To account for the potentially heterogeneous impacts of changes in temperature in colder and hotter climate zones, we estimate a nonlinear model using a binning approach in which we group together firms located in the same categories of temperatures. This may be the only study that estimates the impact of temperature change on productivity using a representative sample of firms in more than 150 countries, providing the most comprehensive study in the literature. The large sample allows for additional heterogeneity analysis by firm characteristics, including firm size and industry classification, country income group, and region of the world. This analysis can be used in the determination and distribution of costs and investments associated with actions to mitigate and adapt to climate change in global negotiations.

Second, this study uses high-resolution climate data capturing localized climate hazard impacts, which are the most relevant because the nature and extent of exposure and damage vary within a few thousand meters.<sup>5</sup> The study combines gridded historical climate data with WBES firm-level data, allowing better identification of impact within a relatively highly geographically specified location. Thus, we can estimate important heterogeneity, controlling for both withincountry and cross-country variations, going beyond cross-country to the level of subnational variations. In addition, the study contributes to the relatively scarce literature on the impacts of climate change on the non-agriculture sectors, especially at the firm level. Despite the relatively rich literature on the impacts of climate change on agriculture, studies on the impacts on the non-agriculture sectors are relatively scarce.

We document that the effects of rising temperatures are nonlinear and uneven across climate zones, where firms in hotter zones experience steeper losses in productivity with increases in temperature, compared to firms in relatively colder zones, which tend to register productivity gains. Specifically, a one unit (1<sup>0</sup> _C_ ) increase in WBT deviation in the hottest WBT quantile 25.7<sup>0</sup> _C_ and above results

> 5The matched data on climate change and enterprises is another key contribution, since it can also be used by other researchers.
dismissing employees (World Bank 2020b). In 2020, Senegal scored a 0 in terms of the Ease of Hiring Index and 60.0 for the Ease of Redundancy Index.<sup>6</sup> This is further corroborated by Golub et al. (2015),<sup>7</sup> which rank Senegal as the third most highly regulated labor market in the world, and the World Economic Forum’s labor market competitive index, where Senegal ranks among the 30 least competitive countries in terms of its labor market.<sup>8</sup> A closer look at the data shows that countries with high levels of informality are associated with lower levels of competitive labor markets (Figure 2, Panel A). This is particularly the case for SSA, which has the highest levels of informality and lowest levels of labor market competitiveness in the world. The opposite is true for countries in Europe and Central Asia, which have a low labor informality rate and are associated with more effective labor markets.

Quality jobs and formality yield higher levels of productivity. This rapport is well documented in the literature (World Bank 2020c; Brummund, et al. 2016; La Porta and Shleifer 2016; Busso et al. 2012; Benjamin et al. 2012; Hsieh and Klenow 2009, Steel and Snodgrass 2008; Gelb et al. 2009; La Porta and Shleifer 2008). Countries with high informality tend to have the lowest levels of worker productivity in the world, as measured by their output per worker (Figure 2, Panel B). This sheds light on the substantial productivity gap between formal and informal firms (World Bank 2019). In Senegal, La Porta and Shleifer (2014) find that the value added per employee of informal firms is 14 percent that of formal firms. Therefore, expanding the share of workforce in formal employment is a critical policy objective for developing countries in order to increase productivity.

> 6 The lower the score, the least flexible regulation, with 0 being the lowest score possible. The Ease of Hiring Index estimates the availability and maximum length of a fixed-term contract for a task related to the permanent activities of a firm, the probationary period, and the ratio of the minimum wage to value added per worker. The Ease of Redundancy Index estimates characteristics of regulation governing notification and approval requirements, retraining obligations, and priority rules for dismissal and reemployment.

> 7 Based on the Doing Business database, the authors create an index of labor market regulations for 2014. 8 The World Economic Forum’s Global Competitiveness Index aims to measure the drivers of total factor productivity (TFP). These drivers are organized into 12 pillars: Institutions; Infrastructure; ICT adoption; Macroeconomic stability; Health; Skills; Product market; Labor market; Financial system; Market size; Business dynamism; and Innovation capability. The labor market pillar aims to assess the flexibility, incentives and merits of the labor market.

6