rafmacalaba/datause-encoder-data huggingface dataset:
bank*period fixed-effects ��,�. Finally, we add fixed-effects at the industry*quarter level ��,� to control for unobserved time-varying shocks to the industry’s fundamentals such as risk or investment opportunities.<sup>26</sup> Therefore, we explore the variation on the outcomes of loans extended to firms within the same industry in states with and without any senator presiding a committee. In other words, this specification compares, at a given time, the credit conditions of firms from the chairman's states with those in the same sector but headquartered in other states. Therefore, �� captures the preferential lending the bank extended to the same firm when it is politically connected versus when it is not.

Since some committees are industry-specific, we also estimate whether the preferential treatment extended to politically connected firms differs across industries within the same state. The effect is represented by an interaction term of ���������,� and ��������,� in the regression outlined in equation (2)

- ��,�,� ��� ��� ����������,� ��� ����������,� ��������� ���,� ���,� � ��,� �

- ��,� ���,�,�      �2�

where �������� equals one if for a given firm _f_ , a senator from its state chairs the committee that directly supervises its industry. The coefficient of interest �� offers a difference-in-difference estimate of this preferential treatment. It contrasts the credit terms of politically connected firms within the same industry versus firms in other industries. As with the benchmark specification, we saturate our specification with fixed-effects at the _firm*bank_ , _bank*quarter_ and _industry*quarter_ level.

# **_III_ .C. Summary Statistics**

Table 1 presents the summary statistics of our sample. Panel A shows the composition of the loanlevel data according to loan attributes. We create six dummies for each loan according to whether the loan is: (1) made to politically connected firms (firms from the chairman's state); (2) made to politically connected firms within the same industry as the chairman; (3) made to the politically connected firms located in the municipality in which the chairman lives; (4) provided by large banks; (5) provided by domestic banks; (6) made to large firms. The table displays the mean and number of observations for each dummy variable. The variable _Chairman_ indicates whether a loan is in a state-sector-period with a chairman. On average, 45 percent of loans are extended to politically connected firms. The variables _Chairman*SameInd_ and _Chairman*SameMuni_ represent whether loans are made to politically connected firms in the same industry or municipality as their chairman. 5 percent of the loans in our sample are from firms in the same industry as the chairman, while 6 percent of the loans are obtained by firms from the chairman’s municipality.

> 26 Industries are classified at the three digit 2007 NAICS.

12
A Duration Analysis of CONALEP

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World Bank. 1997. “Mexico: Training Assessment Study.” White Cover Draft.

20
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**Trujillo Edgar and Marta Badel** : “Los Costos Económicos de la Criminalidad y la Violencia en Colombia, 1991-1996”, in _Planeación y Desarrollo, Vol XX-VIII_ , Bogotá, Departamento Nacional de Planeación 1997

**Valenzuela Pedro** : “Reflexiones Sobre Interpretaciones Recientes de la Violencia en Colombia” in Reflexión Política, Year 4 No 8, ISSN 0124-0781, UNAB, Colombia 2002

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**Women’s Commission for Refugees and Children** : “Colombia: la Guerra en los Niños y las Niñas,” Watchlist on children and armed conflict, New York, 2004
**Figure 1: Change in welfare indicators between 2007 and 2008**

<!-- Start of picture text --> 1800 0.60<br>1600 0.40<br>0.20<br>1400<br>0.00<br>1200<br>-0.20<br>1000<br>-0.40<br>800<br>-0.60<br>600<br>-0.80<br>400<br>-1.00<br>200 -1.20<br>0 -1.40<br>2007 2008<br>Income Food consumption Consumption<br>Weight-for-age BMI-for-age Height-for-age<br>Meticais Z-score<br><!-- End of picture text -->

**Table 1: Change in household welfare indicators between 2007 and 2008 in Mozambique. Sample means.**

||**2007**|**N**|**2008**|**N**|**Difference**|**p-value**|
|---|---|---|---|---|---|---|
|**Income**|1588,96|896|1001,07|896|-587,89|**0,02**|
|**Consumption**|1278,96|861|1137,67|872|-141,29|**0,05**|
|**Food consumption**|670,90|861|673,24|872|2,34|0,96|
|**Weight-for-age z-score**|-0,08|3314|-0,43|3807|-0,35|**0,00**|
|**Height-for-age z-score**|-1,31|3962|-1,24|4446|0,07|**0,03**|
|**Body Mass Index-for-age z-score**|0,37|3930|0,09|4411|-0,27|**0,00**|
|**Lowest weight-for-age z-score**|-0,46|494|-0,85|546|-0,39|**0,00**|
|**Lowest height-for-age z-score**|-1,96|604|-1,85|662|0,12|0,22|
|**Lowest BMI-for-age z-score**|-0,15|601|-0,51|656|-0,36|**0,00**|

Notes: Income, consumption and food consumption are monthly, per adult equivalent measures, in meticais (100mt = $3.7). The weight-for-age z-score is calculated for children aged 1-10 years. The height-for-age and body mass index-for-age are calculated for children aged 5-19 years. Lowest z- scores are the lowest z-score in each household. One observation per household (resp. child) in lines 1- 3 and 7-19 (resp. 4-6).

23
However, since automation implies deskilling and a need for new skills, many current workers would need opportunities to learn. It is important to give opportunities to workers to invest in relevant skills for the labor market that make them benefit from, and remain immune to, automation.

While there is some emerging evidence on how to improve social skills (see Sanchez Puerta, Valerio and Bernal 2016, for example), for creativity, the psychology literature shows little consensus on how to define or teach it (Beghetto and Kaufman 2014). There are few empirical studies on how to improve it (Grigorenko et al. 2008). However, new methods of measuring creativity in terms of divergent thinking have been developed (Plucker and Makel 2010), opening the way for more empirical work. Nevertheless, countries need to figure out how to invest in relevant skills (see Figure 1). These include problem-solving skills to think critically and analyze; learning to learn skills in order to acquire new knowledge throughout a lifetime; communication skills including reading and writing; personal skills for self-management, making sound judgments and managing risks – extremely relevant in the post-truth age where people need to be able to tell fact from fiction; and social skills for collaboration, teamwork, management, leadership, and conflict resolution (Patrinos 2016).

Figure 1: The Skills that Matter Source: Patrinos 2016 <!-- Start of picture text --> Problem-solving<br>Learning<br>Communication<br>Personal<br>Social<br><!-- End of picture text --> More and better education, and non-cognitive skills will not only prepare students for the future world of work. They will also prepare them to navigate the increasingly complex post-truth society. They will be able to detect fake news – deliberate disinformation spread through news or online media. It will also allow young people to gain trust. The ability to make sense of information from multiple sources is a challenge. It is particularly difficult to distinguish between useful information and mis-truths from online sources. A study found that many young people are not able to evaluate information that flows through social media channels (Wineburg et al 2016). The ability to identify reliable information is a global competence that the OECD has developed a framework for. It requires people to triangulate information by seeking confirmation from multiple sources and be able to evaluate the validity of news and information. PISA 2018 results show that 7
WPS3406

# **SPECIAL ISSUES RELATING TO CORPORATE GOVERNANCE AND FAMILY CONTROL**

**Randall Morck** and Bernard Yeung*****

World Bank Policy Research Working Paper 3406, September 2004 _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 to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* The Global Corporate Governance Forum has commissioned a series of discussion papers that will help improve the understanding of corporate governance reform in developing countries. These papers discuss a number of issues raised by various regional corporate governance research network meetings organized by the Forum to support corporate governance research and policy discussion in developing countries. Comments on these papers are welcome and should be sent to the authors and copied to the Forum <u>(cgsecretariat@worldbank.org). The full series of discussion papers can be found on the</u> Forum’s website at http: _www.gcgf.org_

** Randall Morck is the Stephen A. Jarislowsky Distinguished Professor of Finance at the School of Business, University of Alberta, Edmonton, Alberta, Canada T6G2R6; and a Research Associate with the National Bureau of Economic Research. Phone: (780) 492-5683, E-mail: <u>randall.morck@ualberta.ca</u>

*** Bernard Yeung is the Abraham Krasnoff Professor of Global Business, Professor of Economics, and Professor of Management at the Stern School of Business, New York University. Phone: (212) 998-0425, E-mail: byeugn@stern.nyu.edu
<u>Table 25. Innovation and productivity in Bangladesh</u>

||(5)|(6)|(7)|(8)|
|---|---|---|---|---|
|Log(L)|0.1429***<br>(0.027)|0.1416***<br>(0.028)|0.1357***<br>(0.029)|0.1359***<br>(0.029)|
|Log(K/L)|0.2827***|0.3006***|0.2761***|0.2764***|
||(0.029)|(0.030)|(0.029)|(0.029)|
|Prod &/or process|0.5544*<br>(0.319)|0.6902**<br>(0.341)|||
|Prod&proc*national||0.0094|||
|||(0.162)|||
|Prod&proc*inter national||-0.0103<br>(0.171)|||
|innovation_sales|||-1.9082***<br>(0.465)|-1.8851***<br>(0.466)|
|Sales*national||||-0.1514<br>(0.365)|
|sales_int||||-0.2247<br>(0.509)|
|Constant|8.7193***|8.3993***|9.4665***|9.4639***|
||(0.502)|(0.524)|(0.455)|(0.455)|
|Observations|990<br>2 digit|990<br>2 digit|990|990|
|Sector dummies|ISIC|ISIC|2 digit ISIC|2 digit ISIC|

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 35
# 2.1 Agricultural services

Agricultural services offer a context particularly relevant for policy. Our experiment provides evidence on the potential for simple user feedback tools to address two main pitfalls of agricultural services. First, sustaining farmers’ interest in the service may be difficult beyond the first year. Evidence from successful input subsidy schemes in rural Kenya (Duflo et al 2011) shows that, despite large returns to fertilizers in the first year, fertilizer adoption collapses in the second year. Second, private extension services typically fail to secure farmers’ interests in training in the long run, as they focus more on helping farmers cope with idiosyncratic shocks through just-in-time advice (Davis et al., 2014). Third, the incentives of a non-governmental service provider imply that trainings may be tailored to relatively more sophisticated farmers, excluding the more marginalized farmers (Alwang and Siegel 1994; Anderson and Feder, 2003; Axinn, 1988; Saito 1994).

The literature on agricultural extension systems emphasizes demand-side constraints to private (or fee-based)<sup>2</sup> extension provision.<sup>3</sup> Feder et al. (2001) and Anderson and Feder (2004) provide an exhaustive survey of extension systems. The reviews highlight the tendency for general participation to drop under fee-based extension services, at the risk of excluding the poorest farmers, and that accountability mechanisms are needed to overcome potential conflicts of interests in the supply of information. For instance, fee-based extension may exclusively advertise the merits of inorganic fertilizers to boost input sales. Yet, feebased extension services may also be more client-oriented, per their private sector logic. These concerns are particularly salient for policy as markets for agricultural services are

> 2 In the rest of the text, we refer interchangeably to non-governmental and private fee-based extension services, since in our context the extension services are delivered against a fee by an NGO.

> 3 There are many institutional and political reasons governments are eager to retain centrally administered agricultural extension systems; see Axinn (1988) for a detailed description of the tradeoffs associated with different extension delivery systems.

5
Finally, we conclude the analysis by attempting to isolate the impact of the _Canon_ from the direct effect of the mining activity. For that purpose, we run the following specification: where, Canonpd represents the government transfers related to mining revenue received by each district during 2002-2006. Because the Canon’s attribution rule does factor in socioeconomic indicators for the provincial and departmental allocations, we instrument the amount of Canon received by each district with three variables: the log of (one plus) the value of the mineral extracted in each district, province and department between 2002 and 2006.

# **IV. Results**

We begin the analysis by considering the possibility of preexisting differences between producing and non-producing districts. In particular, we test whether socioeconomic outcomes in 1993 differ between districts where a mine was active during 2002-2006 and districts with no mining activity. The socioeconomic outcomes under consideration are the illiteracy rate and the index of basic necessities uncovered, the two variables for which we have information for 1993.

Results are shown in Table 3. The results corresponding to each outcome variable are organized by rows, with columns assigned to different regression specifications and comparison groups. The comparison groups are defined according to whether or not we include in the regressions department and province dummies. In regression (1), the comparison group consists of all non-producing districts in non-producing provinces. In regression (2), the comparison group is restricted to non-producing districts in non-producing provinces within the same producing department. Finally, in regression (3), we compare producing and non-producing districts in the same province. In all cases, we control for timeinvariant district characteristics (provincial capital, log of area, and log of altitude), and district characteristics obtained from the 1993 Census (log of total population, share of rural population, and share of households without electricity).<sup>7</sup> Results are encouraging. While there are differences in the

> 7 Observe that, when we look at 1993 outcomes, the analysis may suffer from reverse causality biases. We shall however not devote efforts to attempt to correct for these biases since the core of our analysis deals with

12
in the long run.

Tables (7)-(9) report the estimated coefficients of the error correction terms and the chisquared statistics for the test of Granger causality. We find evidence of long-run causality from GDP and energy price to energy intensity for all income groups. For lower-middleincome countries, we also find evidence of long-run causality from energy intensity and energy price to GDP growth. This implies that in these countries, GDP in the long run responds to shocks to energy intensity and energy prices.

In the short run, we find bidirectional causality between aggregate energy intensity and economic growth in high-income countries. We find no Granger causality between energy intensity and economic growth for middle-income countries.

Tables 7-9 show the results of Granger causality tests at the sector level. For all income groups, there is long-run causality from GDP and energy price to industrial energy intensity. We also find Granger causality from industrial energy intensity and energy price to GDP for upper-middle-income countries in the long run, and for high- and lower-middle-income countries in the short run. Finally, there is evidence that GDP and energy price Grangercause agricultural energy intensity for upper-middle-income countries in the long run.

As noted, an aggregate analysis at the country level may not reflect variation in sectoral composition. As a robustness check to control for the effect of structural change on energy intensity, we include in the system of equations (5)-(7) an additional variable measuring the ratio of industrial and services value added to GDP. The second panel to the right in Tables 7-9 report corresponding long- and short-run Granger causality test results. Our main conclusions on the direction of causality between GDP and energy intensity remain the same under this alternative specification. This is consistent with findings in the literature on the decomposition of energy intensity indicating that most of the gains in energy productivity over the past few decades can be attributed to genuine efficiency improvements while structural change in the mix of economic activities has had less influence (Zhang, 2013).

# **4 Conclusion**

Energy efficiency is recognized as playing an important part in climate change mitigation. However, the long-term relationship between energy efficiency and growth has not been fully understood. Using panel data for 56 countries from 1991 to 2012, this paper presents a first effort to shed light on this question. We employ panel unit root tests, panel cointegration analysis, and a multivariate panel vector autoregression framework to investigate the long- and short-run causal relationship between energy intensity (used as a proxy for energy efficiency) and GDP for a mix of high and middle-income countries. Because changes in energy intensity can be driven by both changes in sectoral composition and improvements in efficiency, we combine macro-level analysis and analysis of the industrial and agricultural sectors to differentiate the effects of these two processes on energy intensity.

10
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# **Table A5 Individual and Household Outcomes (Individual Items)**

|||||||P-value of<br>|<br>P-value of<br>|
|---|---|---|---|---|---|---|---|
||N. Obs|Mean|BT|LW|BT and LW|t-test<br>(2)=(4)|t-test<br>(3)=(4)|
|||(1)|(2)|(3)|(4)|(5)|(6)|
|**Household Expenditures and Assets**||||||||
|Log of Monthly Expenditures|3494|8.28|0.056***<br>(0.021)|0.018<br>(0.025)|0.045*<br>(0.026)|0.64|0.36|
|Log of Savings|3494|9.42|0.140<br>(0.153)|0.184<br>(0.190)|0.327*<br>(0.188)|0.31|0.50|
|Housing index|3494|0.06|0.194***|0.065|0.158**|0.53|0.19|
||||(0.048)|(0.059)|(0.067)|||
|Log of Livestock value|3494|7.15|-0.165<br>(0.194)|0.137<br>(0.207)|-0.612**<br>(0.252)|0.04|0.00|
|_Aggregate of Income and Assets_|3494|0.05|0.079***<br>(0.024)|0.043<br>(0.030)|0.061*<br>(0.032)|0.55|0.61|
|**CO Cohesion**||||||||
|Can rely more on group members (1=Yes)|3494|0.17|0.027**<br>(0.013)|0.022<br>(0.019)|0.012<br>(0.019)|0.45|0.65|
|More collective action in group (1=Yes)|3494|0.10|0.029**<br>(0.012)|0.004<br>(0.015)|0.052***<br>(0.018)|0.18|0.01|
|Lends to CO members (1=Yes)|3494|0.02|0.019**<br>(0.008)|-0.001<br>(0.011)|0.008<br>(0.012)|0.27|0.47|
|Borrows from CO members (1=Yes)|3494|0.02|0.010<br>(0.007)|0.018*<br>(0.010)|0.007<br>(0.010)|0.75|0.38|
|_Aggregate of CO Cohesion_|3494|-0.06|0.090***<br>(0.027)|0.048<br>(0.033)|0.082**<br>(0.038)|0.82|0.44|
|**General Outlook on Life**||||||||
|Trust index|3494|0.00|0.060<br>(0.051)|0.071<br>(0.070)|0.101<br>(0.069)|0.55|0.70|
|Stress index|3494|0.09|0.173**<br>(0.067)|0.124<br>(0.084)|0.197**<br>(0.090)|0.77|0.45|
|Satisfaction with life|3494|5.47|0.191***<br>(0.065)|0.189**<br>(0.091)|0.289***<br>(0.088)|0.22|0.32|
|_Aggregate of Outlook on Life_|3494|0.02|0.100***|0.095***|0.141***|0.26|0.28|
||||(0.030)|(0.036)|(0.041)|||

Note:<sup><u>1</u></sup> Variable collected only during follow-up. Column 1 reports the mean of CO members not offered business training nor assigned to be lottery winners. Aggreggate variables for each family of outcomes are averages of standardized z-scores of each variable in the family. See Appendix B for a definition of the aggregates. Each row in the table is from a regression of the form in Equation (1) in text. All regressions are estimated using OLS methods and include as covariates the stratification variables (eligibility for loan lottery, business ownership at baseline, gender and branch dummies). Standard errors reported in parentheses are clustered at the CO level. The following symbols *, * * and ** * denote significance at the 10 , 5, and 1 percent level, respectively.