rafmacalaba/datause-encoder-data huggingface dataset:
# OUTINE

|1. Introduction<br>......<br>.......................<br>**..**.........|1|
|---|---|
|2. The thin line between monitoring and control:||
|from Information gathering to decision-taking<br>.............................|2|
|2.1. Debt clms<br>as satisficing instrunents<br>....<br>........................|2|
|2.2. Information fromclients'accowunts and balance sheet data<br>..............|3|
|23.<br>Dired and indirectconsultingactivities**...........................**|4|
|2.4.<br>Banks' involvement in the Boards of clientfirms:a US - German||
|comparison .......................................................|6|
|3. German banks and stock markets - features of a universal||
|banking systen.................................................|9|
|3.1.Equityholdings of German banks<br>**........**<br>**. .**<br>............................<br>.|9|
|<br>3.1.1. Extent and structure of ownership<br>......<br>.. .. ..<br>.....<br>...<br>..<br>.|9|
|3.1.2. Critique andpolicyresponse<br>...............<br>............|11|
|3.2. Acting voting agents forequityinvestors .................<br>.....|13|
|3.2.1. Voting proxies in shareholder meetings of clientfirms...........|13|
|3.2.2. Voting proxies on their own shareholder meetings<br>.............|15|
|3.3.<br>The role of bankers on client firms' Boards of Directors ...............|16|
|3.4. Confficts of interest . .......................................|_19_|
|3.4.1. Firns' value maximizing behavkir and the conflicts of||
|interest among differenit groupsofstakeholders..............|20|
|3.4.2. The conflictofiitterest withinaun:versal bank<br>...<br>.....<br>.......|22|
|3.5. From collateral to creditworthiness lending..................<br>. .....|24|
|3.6. The housebai-k9sanequity underwriter ant; the question of under-||
|<br>capitalizationof Germanindustry ............................|25|
|4. Summary and Final Analysis**...................**<br>**.....**<br>**. .**|26|
relationships, to have many relationships, or to diversify relationships across bank ownership types.

Our Indian data set provides an excellent opportunity to test the hypotheses, with information on firms, banks, and their relationships, as well as data on local market conditions. The data set includes information for 3,423 nonfinancial firms for the year 2001 from the Centre for Monitoring Indian Economy (CMIE). The firms are either listed on a national exchange or have sales or assets exceeding the equivalent of about US $4 million. In addition to firm characteristics, the data include the identities of the banks with which these firms have relationships. Firms with multiple relationships assign priorities to them, and we designate the bank with the highest priority as the firm’s main relationship bank. For firms with only one relationship, we treat their only bank as their main bank.

These data are matched to information from the central bank on the size, financial condition, and ownership type of the commercial banks in the country. We identify four bank ownership types – foreign banks, two types of state-owned banks, and private domestic banks. The state-owned banks include the State Bank of India (SBI) and its associates, established in the 1950s with a substantial rural footprint. These are treated separately from the other type of state-owned bank: more recently nationalized large banks that are managed more as corporate entities and retained some prior management, staff, and relationships with large firms. For convenience, we refer to the four ownership types as foreign, SBI, nationalized, and private, respectively.<sup>1</sup> We estimate three models of bank ownership types and firm-bank relationships. These are reduced forms that embody the preferences and needs of the firms, the abilities of the different types of banks to meeting these needs/preferences, and local market characteristics. Our first empirical model examines the determinants of the ownership type of the firm’s main bank. The results are generally consistent with the hypothesis that foreign banks tend to serve as the main bank for transparent firms. However, the findings often do not support the hypothesis that state-owned banks provide the main relationship for types of firms they are mandated to serve.

Our second empirical model analyzes the effects of main bank ownership type and other variables on whether the firm has multiple banking relationships and on the number of relationships. The results

> 1 Our sample excludes regional rural development banks (RRBs) – state-owned development finance vehicles that make microloans to the rural poor – that do not have relationships with the firms in our sample.

3
These four influences on resource dependence are clearly strongly related to the basic determinants of structural change identified in the classic Chenery, Robinson and Syrquin (1986) study of industrialization and structural change. One other influence on the structure of output, and of exports, identified by Chenery, Robinson and Syrquin is non-homotheticity of consumer demand, although this is difficult to use this for policy purposes. Low income elasticities of demand may, in fact, cause a country undergoing unbiased growth to become more reliant on exports of commodities.

A wide range of policies designed to promote the development of favored sectors have been discussed under the rubric of industrial policy (see Pack 2000 and Stiglitz 1996). Industrial policies have included many specific policies, such as provision of infrastructure, support for education; export promotion activities; technology promotion programs; duty exemption and drawback arrangements for exporters; and preferential allocation of credit to exporting industries. All of these policies can be seen ultimately as affecting the level and structure of output through one of the four channels considered in this paper.

The process of developing growth models that go beyond balanced growth is only now getting under way (see, for example, Kongsamut, Rebelo and Danyang Xie 2001). Specifying model features in a way that will allow them to be useful in analyzing the profound structural changes associated with reducing resource dependence seems likely to require more sources of structural change than are included in most current growth models.

As noted in World Bank (2003), and in Martin (2003), there have been dramatic changes in the participation of developing countries in world trade. The share of manufactures in total merchandise exports has increased dramatically, at the expense of the traditional stalwarts—agricultural products and minerals. This change has been associated with dramatic shifts in policy toward trade openness, and with increases in factor endowments, that raise the available capital and skills per worker.

3
equals the elasticity of migration with respect to fuel revenues in a destination; that is, the relationship between fuel revenues and migrant demand can be identified as an estimate of _ψ_ in an estimating equation We thus feed a regression estimate of _ψ_ into the structural model, and estimate intercepts _φ_<sup>_d_</sup> _e_<sup>foreachmigrantgroupbylettingthemodelmatchtherespectiveobservedemigration</sup> rate. The vector ( _ψ, φ_<sup>_M_</sup> 1<sup>_, ..., φUAE_</sup> 4 ) then parameterizes _λ_<sup>_d_</sup> _et_<sup>,theprobabilityoflocatinga</sup> foreign job. Estimating a life-cycle model requires an assumption on agents’ expectations about the paths for fuel revenues in destination countries over time. We fit quadratic trends to fuel revenues in the main destination countries over the 2000-2017 period, and assume that agents’ expectations are based on these trends.

Taken together, We estimate the 52-element structural parameter vector **_θ_** _≡_ ( _η_ 1<sup>_M, η_</sup> 2<sup>_M, ...,_</sup> _η_ 1<sup>_O, ..., η_</sup> 4<sup>_UAE_</sup> _, φ_<sup>_M_</sup> 1<sup>_, ..., φUAE_</sup> 4 _, A_ 1 _,_ 0 _, ..., A_ 4 _,_ 0 _, C_ 1<sup>_I, ..., C_</sup> 4<sup>_I, p_1</sup><sup>_, ..., p_4)</sup><sup>_′_ofthemodelinSection5byus-</sup> ing method of simulated moments, which minimizes the distance between moments **_m_** _m_ simulated from the model, and the corresponding data moments **_m_** _d_ from the HIES and BRMS samples.<sup>29</sup> Specifically, we minimize the estimation criterion _crit_ ( **_θ_** ) = ( **_m_** _d −_ **_m_** _m_ ( **_θ_** ))<sup>_′_</sup> **_W_** ( **_m_** _d −_ **_m_** _m_ ( **_θ_** )) _,_ where **_W_** is a diagonal weighting matrix with the inverse variances of the data moments on the diagonal.

The moments targeted are listed in Appendix Table A6, and Figure 12 shows the model’s good overall fit. The figure plots moments simulated by the model against their empirical counterparts. For better visibility, we use log scales, and indicate the different groups of moments in the graph. The precise magnitudes for all moments are shown in Appendix tables A7-A9.

> 29The simulation draws individuals from the sample-specific, age-education distribution, and mirrors the empirical sampling scheme of the BRMS in selecting former (simulated) migrants who have returned since 2010.

39
42 / V.E. Lee and A.S. Bryk. "Effects of Single-Sex Secondary Schools on Student Achievement and Attitudes." Journal of Educational Psychology, 78(5) (1986): 381-395; and C. Riordan. "Public and Catholic Schooling: The Effects of Gender Context Policy." American Journal of Education 5 (1985):518-540.

2/ E. Jimenez and M.E. Lockheed. "The Relative Effectiveness of Single-Sex and Coeducational Schools in Thailand." Fducational Evaluation and Policy Analysis 11 (summer 1989).

i/The focus of research related to primary and secondary education for females differs sharply between developed and developing countries. In developed countries, where primary education is generally universal, and secondary education participation is quantitatively equal for both male and female students, research has typically sought to explain gender differences in achievement and gender differences in selection of courses of s-cudy. (For recent reviews, see M.E. Lockheed, M. Thorpe, J. Brooks-Gunn, P. Casserly and A. McAloon. Sex and Ethnic Differences in Middle-School Mathematics. Science. and Computer Science: What Do We Know? (Princeton, NJ: Educational Testing Service, 1985) and S.S. Klein (Ed.) Handbook for Achieving Sex Eguitv Through Education. (Baltimore: Johns Hopkins University Press, 1985). In developing countries, by comparison, research has sought to explain gender differences in school participation: enrollment, retention, and completion. Research from developing countries on factors contributing to gender differences in achievement is almost non-existent. For example, a recent annotated bibliography of 52 studies dealing with female primary education identified only 14 studies that were principally concerned with female students, only two of which dealt with learning outcomes. See N. Stromquist. School-Related Determinants of Female Primary School Participation and Achievement in Developing Countries: An Annotated Bibliography. EDT Discussion Paper No. 83. (Washington, DC: The World Bank,. Education and Training Department, 1987).

R.L. Strang. Women, a World Survey. (Washington, DC: World Priorities, 1985); R. Horn and A.M. Arriagada. The Educational Attainment of the World's Population: Three Decades of Progress. EDT Discussion Paper No.37. (Washington, D.C.: The World Bank, Education and Training Department, 1986).

_5._ A.G. Komenan. World Education Indicators: An Annex to "Improving the Efficiency of Education in Developing Countries. EDT Discussion Paper No. 88. (Washington, DC: The World Bank, Education and Training Department, 1987).

t/ C.A. Anderson and M.J. Bowman (Eds.) Education and Economic Development. (Chicago: Aldine, 1965); and M.J. Bowman and C.A. Anderson. "The Participation of Women in Education in the Third World." In G. P. Kelly and C. M. Elliott (Eds.) Women's Education in the Third World: Compar..tive Perspectives (Albany, N Y: State University of New York Press, 1982), pp. 11-39.
**Table 5: Employment by urban and rural sectors (Figures in percent).**

|||**1987**|||**1988**|||**1991**||
|---|---|---|---|---|---|---|---|---|---|
|**Sector**|**Rural**|**Urban**|**Total**|**Rural**|**Urban**|**Total**|**Rural**|**Urban**|**Total**|
|Government|9.67|23.87|14.06|10.21|23.77|14.09|8.70|23.49|12.97|
|private-formal|2.93|14.15|6.92|4.70|14.04|7.89|2.45|9.10|4.51|
|Private-infornal|2.43|5.22|3.37|3.03|5.58|3.87|2.71|6.36|3.91|
|Self-employed<br>agriculture|68.71|14.06|49.24|59.88|11.84|43.57|64.34|11.98|47.0|
|Self-employed<br>business|15.92|39.17|21.54|21.93|42.39|28.80|20.41|43.24|27.46|
|Non-working|0.34<br>100.0|3.54<br>100.00|2.14<br>100.0|0.25<br>100.0|2.37<br>100.0|1.74<br>100.0|1.39<br>100.0|5.82<br>100.0|4.13<br>100.0|

**Source: Authors calculations using GLSS 1,2,3**

In terms of education and employment we find that most of the wage workers had middle school education (Table 6). Over 70% of the private sector wage workers had middle school education. Of those with post-secondary school education, in 1992, 52% were employed in the public sector and 16% in agriculture, implying that education pays in the public sector. Among the self employed in agriculture 52% only had primary school education. Thus public sector seems to be attracting workers with higher education, while those with little or no education are self employed. However, the share of formal employment has decreased during this period, while the share of private informal has increased especially in the urban areas. Females are predominantly engaged in self-employed business/trading activity, a sector which has been growing in Ghana in the last decade.

In summary, we find that formal sector share of employment has decreased over time. Most people are still self employed in agriculture, even though its share is declining. On the other hand, the self employed business category has increased, which indicates that trade and commerce is absorbing more labor. Most of the educated workers are in the formal sector, with a majority of them having middle or secondary schooling. If informal sector has more potential of growth and labor absorption it may indicate that the present education system does not seem relevant for the informal sector. Over half of the post-secondary educated are employed in the public sector- an indication that education pays in the public sector. The public sector is dominated by males, but the share of females in the public sector has also increased over time.

_15_
# **ZDBLIOGRAPEY**

- Commission of the Eurooean Communities, (1992), "Industrial Competitivenees and Protection of the Znvironment", Communication of the Commiesion to th Council and to **the** European Parliament, Brussels.

- Blazerczak. Juroan, (1993), "Environmental Policies and **the** Decision of Enterprises to migrate", **mimeo.**

- Fukasaku, U. (Mai 1992), "Environment Policy, Research **and** Innovation in Japan, An Overview", in CSI - L'Environement au Jacon.

- Intornational Finance Corooration, (1992), Investina in the Environment, Washington.

- Low, P. (1992), "Trade Measures and Environmental Quality: The Implications for Mexico's Exports", in P. LCw (ed.): International Trade and the Environment, World Bank Discussion Paper 159, pp. 105-120.

- Low, **P.** /Yeats. A. (1992), Do **"Dirty"** Industries Migrate?", in: P. Low **(ed.) International TSade and the** Environment, World Bank **Discussion Paper** 159, pp. 89-104.

- OECD (1983), **Positive** Adiustment **Policies,** Managina Structural Chanoe, Paria.

- QIC4, (1990), Pollution Control and Abatement Exoenditure in OECD Countries: A Statistical ComRendiam, No. 38, Paris.

- QECD (1993), Pollution Abatement and Control Exoenditure in OECD Countries, No. 75, Paris.

- OECD, (1991a), Environmental Indicatorg. A Preliminary Set, Paris.

- 3ECD, (1991b), The State of the Environment, Paris.

- OC, (1992), The OECD Environment Industry: Situation, Prosoects and Government Policies, Paris.

- Porter, M.F., (1990), The Competitive Advantaae of Nation, New York Free Press.

- Statiotisches Dundesamt, (1991), Ausgewahlte Ergebnisse zur Umweltokinomischen Gesamtrechnung 1975 bis 1990, Heft 18 der Schriftenreihe Auagewahlte Arbeitsunterlagen zur Bundeestatistik, Wiesbaden.

- The World Bank, World Development Report, 1992, Oxford University Press.

- Uaelow. J.L., (1982), A Survey of Recent Studies on Costs of Pollution Control and the Effects on Trade, in S. Rubin (ad.) Environment and Trade, New Jersey.

- United Nations (1992), World Investment Report. Tranenational Corporation as Engines of Growth, New York.

- OECD (1993), Pollution Abatement and Control Expenditure in OECD Countries, No. 75, Paris.
the empirical strategy. Section 4 discusses the main results, while Section 5 presents results from a number of robustness tests. Section 6 concludes and suggests possible directions for future research.

# **2. Concept definition and data**

This paper focuses on attitudes toward violent extremism. We do not study the process of becoming radicalized but the traits of people who justify terrorist attacks which target civilians. These extremists may not commit terrorist acts themselves but may be at high risk for being recruited by terrorist organizations or may sympathize and assist these organizations. The comprehensive survey data from the Gallup World Poll for the period 2006-12 include detailed information on individual wellbeing, attitudes, and a host of objective individual and household characteristics, along with views that indicate an individual’s position on the radicalization spectrum. This spectrum includes at one extreme individuals who are completely against attacks targeting civilians and at the other extreme features people who completely justify such attacks. Specifically, the survey includes the question Q1: “ _I would like you to indicate to which extent it can be morally justified: attacks in which civilians are the target_ ”.<sup>6</sup> The answers to this question reveal the individuals’ attitudes toward attacks targeting civilians, with responses ordered from 1 for those who believe that such attacks “ _cannot be justified at all_ ” to 5 for those who consider such attacks “ _completely justifiable_ ”.

> 6 There is a similar question related to individuals’ preferences for targeting civilians by the military, but the answers to it capture a different story from the one in this study.

8
skipped topics include the effects of large-scale privatization, the effects of reforms in access to finance and corporate governance.<sup>6</sup> Almost all investment climate surveys are cross-sectional in nature, with the notable exception of BEEPS in Eastern Europe and in a few countries such as India. However, since institutions and investment climates change slowly, the attempt to identify effects of investment climate relying on within-country across-time variations in a short time span is unlikely to go far (Griliches and Hausman 1986). Not surprisingly, I am unaware of solid firm-level empirical studies using panel investment climate surveys so far.

The most common mode of identification with cross-sectional firm-level data is to construct proxies of the local or national business environment by using city- or country-level measure of access to finance, tax burden, corruption, labor flexibility, and so on, and then relate them to firm performance. In the case of country-level measures of business environments, multiple-country data have to be used. A variety of robustness checks are often used to ensure the robustness of the key findings. Often, omitted city-level or country-level proxies (such as level of development and other aggregate level variables) are added to check the robustness of the results (Cai et al. 2011). Alternatively, instruments for key business environment measures are used. An example is to use the distance to surrounding enforcement offices to instrument the enforcement of regulations (Almeida and Carneiro 2009). Another common method of identification in this literature is the difference-indifference approach using cross-country cross-industry data, in which the more disaggregated outcomes are regressed toward country dummies, industry dummies, and an interaction term of country-level treatment with industry-level sensitivity variable.<sup>7</sup> The idea is that “more sensitive” industries should be more affected by the treatment. By controlling for country and industry dummies, all country- and industry-specific factors are controlled for, which makes this method more convincing and less subject to the omitted variable bias than most other cross-sectional estimations. If the interaction term proves to be significant, then more sensitive industries are indeed responding as predicted to the treatment, supporting the notion of causality from the treatment to the outcome especially for the sensitive type. Often, sensitivity checks are applied—in which more interaction terms of the treatment with other industry-level variables are used—to make sure that it is indeed

> 6 See La Porta et al. (2000), Levine (1997), World Bank (2001 and 2008a), Djkankov and Murrell (2002), and Megginson and Netter (2001). We also do not discuss the lessons from survey methodology. If interested, see Recanatini et al. (2002) and Iarossi (2006).

> 7 For the use of difference-in-difference approach in the business environment literature, see Ciccone and Papaioannou (2007); Aghion et al. (2008); and Klapper et al. (2006).

4
# Fostering community-driven development: what role for the state?

## Monica Das Gupta

_Development Research Group, World Bank, 1818 H Street NW, Washington DC 20433 Mdasgupta(a,worldbank orgz_

## Helene Grandvoinnet

_Development Research Group, World Bank, 1818 H Street NW, Washington DC 20433 hjzrandvoinnet(a-).worldbank. ore_

## Mattia Romani

_Development Research Group, World Bank, 1818 H Street NW, Washington DC 20433_ _mromani(d&worldbank org_ This analysis was done as we prepared for writing the chapter on state institutions of the WDR 2000/1 on Poverty. We are very grateful for comments and suggestions received on earlier drafts, from Abhijit Banerjee, Pranab Bardhan, Tony Bebbington, Hans Binswanger, Stephens Commins, Samantha da Silva, Mrinal Datta Chaudhun, Jean-Jacques Dethier, Shantayanan Devarajan, Jean Dreze, Gershon Feder, Jonathon Fox, Ron Herring, Ravi Kanbur, Peter Lanjouw, Nora Lustig, William Mayville, Dilip Mookherjee, Jean-Philippe Platteau, Alice Sindzingre, Judith Tendler, Robert Wade, anonymous reviewers, and participants at the seminars at the World Bank Research Group and Social Development Group.