rafmacalaba/datause-encoder-data huggingface dataset:
# **Box 1.  Questions on Relatedness**

(from Barth, Caprio, and Levine database of Bank Regulation and Supervision 2000, 2003)

1. What is the maximum percentage of bank capital that can be owned by a single owner [0-100]?

2. What is the maximum percentage of a bank’s capital that can be owned by a related party [0-100]?

3. What is the level of regulatory restrictiveness for non-financial firms’ ownership of a bank [0 = Prohibited; 33.3 = Restricted; 66.7 =Permitted, with prior authorization or approval; 100=Unrestricted]?

4. What is the level of regulatory restrictiveness for bank ownership of nonfinancial firms [0 = Prohibited; 33.3 = Restricted to some figure below 100 percent; 66.7 = Permitted to own 100 percent of equity, but ownership is limited based on the bank’s equity capital; 100 = Unrestricted, a bank may own 100 percent of the equity of any non-financial firm]?

We rescale the qualitative responses to questions 3 and 4 on ownership inter-linkages with non-financial firms (prohibited, restricted, permitted subject to certain stipulations, and unrestricted) so that they conform to the responses from the first two questions (scaled from 0 to 100). We assign ―prohibited‖ a value of 0, ―restricted‖ a value of 33.3, ―permitted‖ a value of 66.7, and ―unrestricted‖ a value of 100. Our index of related lending, _Related Lending_ , is the simple average of the responses to the four questions.<sup>4</sup> As Box 2 indicates, some of the responses to the questions are significantly correlated, especially questions 1 and 2 (the percentage ownership limits of banks for individual entities and related parties). To a lesser extent, responses to those two questions are also correlated with the responses to question 3 (limits on non-financial firms’ ownership of banks). By contrast, the responses to question 4 (limits on bank ownership of non-financial firms) are not significantly correlated with the responses to any of the other questions. Despite some significant correlations, the overall pattern suggests that each question provides a separate source of information. In robustness checks below, we de-compose our index into individual components that isolate the effects of each type of restriction on private credit growth.

> 4 The BCL questions in Box 1 were asked in 1998-99 and again in 2002, and thus we have two observations for our index of related lending for each country. We compute the index for each country using the 1998-99 responses and assign that value to all observations prior to 2002.  For observations after 2002, we use the responses to the 2002 survey.  We then take the average of those two observations for each country (weighted by the number of pre- and post-2002 observations), which we use in the regressions.  As a practical matter, there was not much variance in responses from the 1998-99 survey to the 2002 survey.  Thus, our results are not sensitive to the weighting scheme.

8
51-A PERU: REAL INFOR :MAL EXCHANGE RATE <!-- Start of picture text --> (INDEX 1987:1-100)<br>250-<br>240<br>230<br>220<br>210<br>200<br>190<br>180<br>170 -<br>160<br>150-<br>140-<br>130<br>120-<br>110<br>100<br>90<br>80<br>70-<br>1987 1988 1989 1990<br><!-- End of picture text -->
The correlation coefficient between asset share of foreign banks and the growth rate is 0.06. This indicates a low relationship between economic growth and foreign bank entry without controlling for other determinants of growth.

Since we use country-level variables while estimating the regression equations, the banking sector variables at the country level are calculated as averages of the corresponding variables at the bank level.

Our data set covers the years from 1994 to 2003. Since BANKSCOPE reports banking sector variables only for the most recent years, it is not possible to conduct longer-term growth analysis. Thus, the focus, in the following sections, is on the shortterm economic growth.

# **3.2 Regression Specification**

We use two different sets of regression specifications to study direct and indirect links between the economic growth rate and the share of foreign bank assets. The basic regression equation for the direct link is as follows: where _i_ is the country index and _t_ is the year index. _Tt_ is the time dummies and _Fi_ is the country dummies, and ε _i_ , _t_ is the error term. The economic growth is measured as the log − difference of GDP per capita in real terms, _yi_ , _t yi_ , _t_ − 1 . The initial level of GDP per capita is introduced as the first explanatory variable to control for initial conditions. This variable makes the equation dynamic. _FBSi,t_ is the over-time asset share of foreign banks in each country. _Xi,t_ stands for control variables for other determinants of growth. These variables are chosen according to their significance in determining growth and their 13
POLICY RESEARCH WORKING PAPER **_3059_**

# **Abstract**

This paper explores empirically the role of risk and return in the observed evolution of net foreign asset positions of industrial and developing economies. The paper adopts a dynamic approach in which investors' portfolios adjust gradually to their long-run equilibrium, defined **by** a standard Tobin-Markowitz framework. The parameters characterizing the long-run equilibrium are estimated using data on foreign assets and liabilities of a large number of industrial and developing countries spanning the period from **_1965_** to **1997.** The paper employs a dynamic panel estimation procedure allowing for unrestricted short-run heterogeneity across countries, using the pooled mean group estimator recently developed **by** Pesaran, Shin, and Smith **(1999).** The empirical results lend considerable support to the model when applied to countries with low capital controls and/or high and upper-middle income. The results for countries with either high capital controls or low per capita income are less supportive of the stock equilibrium model for net foreign asset positions.

This paper-a product of Macroeconomics and Growth, Development Research Group-is part of a larger effort in the group to understand international capital flows. Copies of the paper are available free from the World Bank, **1818** H Street NW, Washington, **DC** 20433. Please contact Tourya Tourougui, room **MC3-301,** telephone 202-458-7431, fax **202-522-** **_3518,_** email address ttourougui@worldbank.org. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at nloayza@worldbank.org or Iserven@worldbank.org. May **2003.** _(45_ pages) _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._ Produced **by** Partnerships, Capacity Building, and Outreach
Ever since the appearance of Kuznets' seminal works (1955, 1963) on the relationship between economic development and income inequality, there has been much interest and speculation about the sources of income inequality in the developing world. Within the past fifteen years the development of new methodologies for decomposing the sources of income inequality has infused this subject area with new data and insights. Using various techniques, a number of empirical studies in individual developIng countries have pinpointed the contiibution of different sources of income to total income inequality.' These studies have decomposed income inequality by economic sector (e.g., urban vs. rural), income source (e.g., income from labor vs. capital vs. land) and family characteristics (i..cluding educational and occupational attributes of workers).

Such empirical studies are of considerable potential use to developing country policymakers because they help identify both the structure of income inequality and how the character of that inequality changes over time. Equipped with such information, government officials can devise specific policy measures to help improve the distribution of urban and rural incomes.

This paper seeks to add to our knowledge about the sources of income inequality in the developing world by examining the sources of such inequality in rural Pakistan. The paper seeks to make two contributions. First, it uses panel data gathered in a rural area to identify the contribution of different sources of income --
# **References**

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38
# **Contents**

|**Exe**|**cutive Summary**|**2**|
|---|---|---|
|**1**<br>**Intr**|**oduction**|**5**|
|**2**<br>**Lite**|**rature Review**|**9**|
|**3**<br>**Mod**|**el**|**12**|
|3.1|Time periods and discount factor . . . . . . . . . . . . . . . . . . . . . . . . . . . .|14|
|3.2|Investments, prices, costs and demand for each period . . . . . . . . . . . . . . .|14|
|3.3|Present value of the project<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|15|
|3.4|Partners from diferent countries . . . . . . . . . . . . . . . . . . . . . . . . . . . .|15|
|3.5|Firms’ preference<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|16|
|3.6|The competition of diferent consortia for the project . . . . . . . . . . . . . . . . .|17|
|3.7|Government’s preference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|17|
|**4**<br>**The **|**general guarantee mechanism**|**18**|
|4.1|The defnition of the concession period . . . . . . . . . . . . . . . . . . . . . . . . .|19|
|4.2|Transfer to the government in case of excess value. . . . . . . . . . . . . . . . . .|21|
|4.3|Foreign partners and their values . . . . . . . . . . . . . . . . . . . . . . . . . . . .|22|
|4.4|Impact of the mechanism on the bidding process. . . . . . . . . . . . . . . . . . .|24|
|4.5|Impact of the mechanism on the government’s objective<br>. . . . . . . . . . . . . .|25|
|**5**<br>**Spe**|**cifc guarantee mechanisms: target functions**|**27**|
|5.1|Guaranteed exchange rate level . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|27|
|5.2|Guaranteed return<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|28|
|5.3|Guaranteed equal returns among partners<br>. . . . . . . . . . . . . . . . . . . . . .|29|
|**6**<br>**Spe**|**cifc guarantee mechanisms: test functions**|**30**|
|6.1|Notional costs and investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|31|
|6.2|Notional demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|32|
|6.3|Notional prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|32|
|6.4|Notional and real values<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|32|
|6.5|Information requirements under diferent target functions and alternative test<br>functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|33|
|**7**<br>**Sim**<br>|**ulating alternative guarantee mechanisms in a real life highway concession**<br>|**34**|
|7.1|Guaranteed return<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|36|
|7.2|Guaranteed equal returns among partners<br>. . . . . . . . . . . . . . . . . . . . . .|38|
|**8**<br>**Con**|**clusion**|**39**|
|**A Pro**|**ofs**|**40**|
|A.1|Proof of of Theorem 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|40|
|A.2|Other proofs<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|42|
|**B Sim**|**ulations’ data**|**46**|

4
role in mainstreaming climate adaptation into development assistance and take advantage of the numerous opportunities to initiate action at governmental as well as donor level.

One important area of action lies in improving organizational capacity and the enabling environment. Continued budget support for the environment, with the aim of strengthening MICOA as an institution, would increase its leverage and convening power to coordinate other ministries that are instrumental in mainstreaming adaptation such as agriculture, energy or health. Another area of assistance is support of the Sector Working Group on environment. Joint meetings with other relevant working groups whose performance and deliverables are affected by climate impacts including agriculture, roads and health could encourage the cross-sectoral dialogue and ensure the consideration of climate mainstreaming in the relevant budgetary planning and implementation processes.

Another key area for mainstreaming climate adaptation is in education and training. Programme Aid Partners can play a key role in stimulating explicit consideration of climate change adaptation as a cross cutting issue. Climate adaptation specific indicators derived from the existing adaptation intervention could to be used in the joint reviews of PAP, and help to operationalize mainstreaming climate adaptation. Improved climate data collection and management as well as the national early warning and forecasting systems are further areas where donors can actively assist the Government of Mozambique in improving the foundations of climate informed development.

Finally, at the partnership level, bilateral and multilateral donors are in a unique position to support climate mainstreaming in development planning and implementation. A systematic approach to screen public investments for climate impacts at sub-national level and identify the existing capital stock to receive priority in the face of climate change are fundamental next steps to define targeted adaptation interventions. Such action provides valuable insights to be adopted by the Development Partner Group and associated sectoral working groups. Further actions include the broad engagement of stakeholders such as NGOs and the civil society in supporting adaptation projects in the country and promoting comprehensive capacity building programs.

23
<!-- Start of picture text --> Kaplan-Meier survival estimates<br>0 10 20 30<br>Duration<br>IFI not involved IFI involved<br>Kaplan-Meier survival estimates<br>0 10 20 30<br>Duration<br>Priv. share<50 50<Priv. share<80<br>80<Priv. share<90 90<Priv. share<br>1.00<br>0.75<br>0.50<br>Survival rate<br>0.25<br>0.00<br>1.00<br>0.75<br>0.50<br>Survival rate<br>0.25<br>0.00<br><!-- End of picture text --> 39
15

**Figure 8:** Percent of Firms by Age, 2000 and 2010

- a. 2000                                                                  b. 2010

<!-- Start of picture text -->
>50 years,  >50 years,<br>0.7 0.7<br>≥16 to ≤50 years,<br>≥16 to ≤50 years,  ≤5 years,  31.9<br>33.8 21.6 ≤5 years,<br>29.1<br>≥6 to ≤15 years,  ≥6 to ≤15 years,<br>43.9 38.4<br>≤5 years ≥6 to ≤15 years ≥16 to ≤50 years >50 years ≤5 years ≥6 to ≤15 years ≥16 to ≤50 years >50 years<br><!-- End of picture text -->

Source: Department of Statistics Malaysia (DOSM).

Note: Firms that reported an inconsistent age over the three census waves are excluded from the age analysis. About 9.5% of firms are removed from this analysis.

Likewise, the truncation does not imply that the age distribution of firms shifts towards older firms (figure 8, panels a and b). In the truncated sample, the percentage of firms younger than 5 years increased from 21.6% to 29.1%, and that for firms between 6 and 15 years of age decreased from 43.9% to 38.4%. This pattern is quite similar in the truncated sample, as can be observed in figure 8.

**Figure 9** : Share of Firms that Are Locally, Foreign, and Jointly Owned, 2000 and 2010 _(percent)_

a. 2000 b. 2010 <!-- Start of picture text --> 0.3 0.3<br>13.4 9.6<br>86.3 90.1<br>Local Foreign Joint ownership Local Foreign Joint ownership<br><!-- End of picture text --> Source: Department of Statistics Malaysia (DOSM).

A large proportion of firms (86–90%) in all the three censuses are locally owned (figure 9). The truncation of data does not change the ownership profile of firms, where locally owned firms still represent the majority.

# **B. Treatment of Data**

# (i) Construction of the Capital Stock

We analyze the patterns of investment across the three census periods to derive a reasonable measure for the growth rate of investment, g. Our analysis focuses on investment made by
the large public bank, Commercial Bank of Syria. For loans to MSEs, public banks were also the leading providers – Real Estate Bank, Popular Credit Bank, and Commercial Bank of Syria.

**State banks hold a significant proportion of financial service outlets in several MENA countries** , including Syria, Algeria, Tunisia, Egypt and Iraq.  The Principal Bank for Development and Agricultural Credit (PDBAC) is the state agricultural bank in Egypt, and has more than 1200 branches and 23,000 employees throughout the country outside of Cairo, providing deposit services; payments services; foreign currency services and passbooks, machinery and equipment loans and lending for agriculture and other food production enterprises.  In Iraq over two thirds (69 percent) of the bank branch network is owned by the two large state banks, Rafidain and Rasheed.

# **Microfinance banks, or specialized microfinance institutions, are an alternative model**

**where regulations allow.** Instead of relying on banks for deposits and commercial borrowing, microfinance banks can intermediate their own funds.  AGFUND and the Aga Khan Agency for Microfinance are committed to establishing microfinance „banks‟ as specialized financial institutions that provide a broad range of financial services to low income people and microenterprises, on a sustainable basis.  AGFUND has set-up MFIs in Jordan, Egypt, Bahrain, Syria and Yemen. For example, Al Amal microfinance bank was established under the Yemen microfinance law, and offers deposit and lending services to low income clients, including micro and small enterprises. Al Amal started operations in 2008, and now has nine branches, 12,000 active clients (half of whom are savers). A money exchange company in Yemen has also reportedly converted to a microfinance bank, Al Kurami. The Aga Khan Foundation has set-up a deposit-taking microfinance institution in Syria, which has some characteristics of a microfinance bank microfinance bank, as a Social Financial Banking Institution.  Under Syria‟s General Microfinance Decree such an institution is allowed to provide microlending, deposittaking, and micro insurance.

# _Microcredit in MENA_

**Microcredit**<sup>**3**</sup> **is still limited in scale** in much of the region.  The market coverage ratio for specialized microfinance providers is only half (1.78 percent) the proportion of the adult population as is the case for South Asia or Latin America.<sup>**4**</sup> As indicated in Table 2, the number of active borrowers compared to working age adults is much smaller than all regions except subSaharan Africa, while microcredit loan portfolios represent a significantly smaller proportion of total credit<sup>5</sup> than any other region.

> 3 Defined by the World Bank and CGAP as having an average outstanding loan size less than three times GDP per capita.

> 4 Data for Microfinance Institutions that report to the Microfinance International Exchange. 5 Domestic private credit to the real sector by banks.

8
- _<mark>Notes:   a   Mapping from 10 digit codes to sectors is based on Goskomstat classification b   Standard deviation in parentheses</mark>_

# **Table B5.    Number of lines with estimated collected rates above 20 and 30% and maximum estimated collected rates by year and industry**

|**_Industry_**|||||**_Yea_**|**_r_**|||||
|---|---|---|---|---|---|---|---|---|---|---|
||**_200_**|**_1_**|**_200_**|**_2_**|**_200_**|**_3_**|**_2004_**||**_200_**|**_5_**|
||_>20_|_>30_|_>20_|_>30_|_>20_|_>30_|_>20_|_>30_|_>20_|_>30_|
|_Chemicals and_<br>_thil_|**_6_**|**_2_**|**_14_**|**_9_**|**_18_**|**_12_**|**_15_**|**_10_**|**_13_**|**_5_**|
|_perocemcas_||125||154||186||181||198|
|_Mechanical_<br>_engineering_|**_49_**|**_17_**|**_57_**|**_38_**|**_59_**|**_43_**|**_59_**|**_46_**|**_60_**|**_43_**|
|_and metal-working_||198||338||939||293||278|
|_Timber, wood, pulp and_<br>_paper_|**_17_**|**_5_**|**_24_**|**_7_**|**_27_**|**_15_**|**_29_**|**_17_**|**_28_**|**_14_**|
|||51||83||73||74||54|
|_Construction materials_|**_2_**|**_1_**|**_6_**|**_2_**|**_7_**|**_2_**|**_4_**|**_0_**|**_5_**|**_0_**|
|||48||81||60|29||29||
|_Light industry_|**_130_**|**_51_**|**_232_**|**_97_**|**_251_**|**_129_**|**_210_**|**_71_**|**_165_**|**_50_**|
|||518||284||319||164||84|
|_Food industry_|**_230_**|**_76_**|**_238_**|**_83_**|**_313_**|**_130_**|**_317_**|**_140_**|**_297_**|**_144_**|
|||127||388||323||256||384|
|_Other industries_|**_2_**|**_1_**|**_3_**|**_3_**|**_3_**|**_3_**|**_4_**|**_3_**|**_4_**|**_3_**|
|||38||63||54||57||53|
|_Agriculture and forestry_|**_2_**|**_2_**|**_20_**|**_9_**|**_24_**|**_15_**|**_21_**|**_14_**|**_17_**|**_8_**|
|||58||56||70||57||62|
|_Other goods-producing_<br>|**_1_**|**_1_**|**_1_**|**_1_**|**_1_**|**_1_**|**_1_**|**_1_**|**_1_**|**_1_**|
|_sectors_||124||99||109||76||53|

_Notes: a Number of lines b The highest rate in % in the subcategory_ 46