rafmacalaba/datause-encoder-data huggingface dataset:
# _F. Shareholder rights, supervision, and regulation_

This paper examines the impact of the legal protection of shareholders and official bank supervisory and regulatory practices on bank valuations.

RIGHTS is the LLSV (1998) index of the legal protection of shareholders across countries. This index ranges from zero to six, where larger values indicate greater legal protection of shareholder rights. Table I gives a detailed definition and Table II provides summary statistics.

To study the supervisory and regulatory environment, we use an assortment of indicators from the Barth, Caprio, and Levine (2001, 2004) database. A growing number of papers use the information contained in this dataset to examine the impact of bank supervision and regulatory policies on bank performance, stability, and corporate finance (e.g., Beck, Demirgüç-Kunt, and Levine, 2003a,b; Demirgüç-Kunt, Laeven, and Levine, 2003, and citations therein). Table II gives summary statistics and the values for each country are reported in Table III.

OFFICIAL is an index of the power of the commercial bank supervisory agency. As specified in Table I, OFFICIAL includes information on the rights of the supervisory agency to meet with, demand information from, and take legal action against auditors; to force a bank to change its internal organizational structure, management, directors, etc.; to oblige the bank to provision against potential losses and suspend dividends, bonuses, and management fees; and to supersede the rights of shareholders and intervene in a bank and/or declare a bank insolvent. We include this variable since greater bank supervisory/regulatory power may reduce insiders from exploiting minority investors in the bank.<sup>11</sup>

> 11 We also conduct the analyses using components of OFFICIAL that focus only on the disciplinary powers of the supervisory agency. That is, we include information on the power of the supervisory agency to force a bank to change its internal organizational structure, management, directors, etc.; to oblige the bank to provision against potential losses, and suspend dividends, bonuses, and management fees; and to supersede the rights of shareholders and intervene in a bank and/or declare a bank insolvent. We confirm all of our findings with this alternative indicator.

14
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both emergency assistance and food consumption scores. We find no significant impact of this variable, neither on assistance nor on food consumption score. This is an important result since it proves that households seem to succeed in coping with this shock. Besides, food assistance appears to have no effect on household food consumption score in non-directly affected areas. Female headed families have lower food consumption score, while agricultural producers are less food insecure.

# **6. Cash and Food-for-Work Programs**

Before the earthquake, the labor market in Haiti was already a major issue. According to CNSA (2008), in rural Haiti, half of the population was inactive (with a higher concentration in the North) and the dependency rate (i.e. the number of inactive persons over active ones) appears to be particularly high with about 103 inactive persons for 100 employed persons.

In response to these very restrictive conditions on the labor market, and in addition to food assistance programs, cash-for-work (CfW) programs provide immediate cash support to the unemployed. In general, CfW are short-term interventions (normally 2-3 months) which provide temporary employment in public projects (such as repairing roads, cleansing irrigation channels or re-building infrastructure) to the most vulnerable segments of the population or in areas directly affected by natural disasters. After the earthquake, CNSA (2010a) reported that many people interviewed in focus groups expressed a preference for activities where their skills and capabilities could be used, such as the cash and food-for-work (C/FfW) programs which allow people living in directly affected areas to keep their dignity and self-esteem.

# _(a) Main results_

Tables 14a and 14b present descriptive statistics by pre-earthquake wealth. First, Tables present variables describing household vulnerability such as employment status, death of workers, housing damage and asset losses after the earthquake. Second, variables such as the household’s main income source, participation in cash and food-for-work programs as well as the benefice derived from other transfers from relatives or friends are presented before and after the earthquake. Benefits from various assistance programs are presented in June.

Main income sources have been hit by the disaster: although the percentage of households for whom agriculture production is the main source of income has remained stable over the period, incomes from trade, unskilled and skilled works have dropped. Conversely, 22
Table A.6: Hamrin area 2

|**Dates Removed**|**Temperature(K)**|
|---|---|
|March 7,2015|1273|
|March 8,2015|1810|
|March 9,2015|972|

Last, in an another site in the Hamrin vicinity, Daesh again set fires before it ceded control March 10th 2015. Landsat Images are available March 1, 2015, March 17, 2015, and April 2, 2015. Smoke plumes from portions of the oil field are visible in the March 17, 2015 image and smoke plumes are visible in other portions April 2, 2015. There is no definitive evidence of smoke on March 1.

Table A.7: Al Hussein North

|**Dates Removed**<br>**Temperature(K)**|
|---|
|October 26,2015 to November 23,2015<br>Average = 1725|

**Al Hussein North Area:** A final site located near the border of Ar-Raqqah and Homs Syrian governorates was removed due to irregular activity in November 2015. The site displayed infrequent low-intensity production and during and before the period of Daesh control. We observed a sudden spike in uncharacteristically high production in November 2015 (Figure A.9). We interpreted the irregular spike immediately prior to indefinite inactivity as evidence of an air strike or warfare that destroyed infrastructure at the site.

In support of this interpretation, we observed that the radiant heat signals during this period were generally lower than expected for oil production. Most temperatures observed in this period were below 1600K; however, for several days there were no detections in the M10 band and we assumed the temperature was 1810K. Thus the high average temperature generally reflects our generous assumption of activity when the signal was poor. Qualitative evidence of the activity at this location was not available. Landsat images were available November 8, 2015 36
“men are increasing in number in the factories and so are women, so there is a high likelihood of it taking place”.

Most factories operate at least five days a week, with work on Saturdays as orders demand, and Sundays off. The typical workday is from 7am until 5pm, with the gates closing promptly at 7am. Workers do not earn paid leave except in the case of illness proven with a doctor’s note, and hence they hesitate to take any time off.<sup>19</sup> On average, textile respondents are not satisfied with their job (3.3 on 1‐5 scale, with 1 being “very satisfied”, 3 being “indifferent”, and 5 being “very unsatisfied”) and are not likely to recommend their job to a family member or close friend back home (3.3 on a 1‐4 scale, with 1 being “very likely” and 4 being “not likely at all”).

Despite little job satisfaction, there is evidence that some factories have improved conditions with the help of various programs, including Better Work.<sup>20</sup> Better Work was a partnership program of the ILO and the IFC that provided assessment, advisory, and training services to the industry in Lesotho from 2010 to 2016. The initiative further addressed common workplace issues – harassment by supervisors, violations of occupational safety and health requirements, and compensation – through various training products, including Supervisory Skills, HR Management, and Occupational Safety and Health. A 2013 assessment found increased compliance efforts with regards to discrimination, union operations, minimum wages, contracting procedures, discipline and disputes, and OSH management systems.<sup>21</sup> While not entirely satisfied with their efforts,<sup>22</sup> textile workers acknowledged the Ministry of Labor and other associations/unions at their disposal, as well as a committee to help with workplace conflict: “To solve conflicts amongst ourselves as employees, there is a committee chosen by us which deals with such issues, they sit us down and talk to us”. Textile workers are highly unionized (54 percent of survey workers). They are represented by five sectoral unions: the Lesotho Clothing and Allied Workers’ Union (LECAWU), the Factory Workers’ Union (FAWU), the National Union of Textile (NUTEX), the United Textile Employees (UNITE), and the Voice of Workers (Lentsoe La Sechaba). They report that the factories tend

> 19 Lesotho’s labor legislation is mainly documented in the Lesotho Labour Code 1992, with additional amendments made in subsequent years, including those specific to the treatment and prevention of HIV. The laws include the ILO’s core conventions, including no child or forced labor, non‐discrimination, freedom of association, regulated working hours of 45 normal and 11 hours of overtime a week, and minimum leave. Additional codes of conduct include no forced labor, payment of minimum wages, and health and safety, and sexual harassment. 20 World Bank (2015).

> 21 <mark>International Labor Organization (2013).</mark>

> 22 Despite union membership among textile workers being around 50%, their ability to challenge constraints to labor law effectiveness remains limited (They’Daemane 2014).

34
variance.<sup>10</sup> This latter percentage rises to about 7-8% if a “naïve” model that does not control for locational effects is estimated.<sup>11</sup> In addition, with the naïve model the importance of locational effects at the higher level become more pronounced (Table 4c).

# Table <u>3:  Breakdown of localities in Minas Gerais</u>

|Locality-type|No. of Localities|Numbe<br>Minimum|r of Households Per<br>Average|Locality<br>Maximum|
|---|---|---|---|---|
|Meso-region|12|10519|48637|158153|
|Micro-region|66|1601|88430|114589|
|Municipality|853|46|684|61852|
|District|1.568|3|372|39410|
|Enumerationarea|22,211|1|26|153|

Source: Census and authors’ calculation.

# Table 4a:  Inter-cluster variances: Preferred Model

|Locality-type|M1|M2|M3|M4|M5|M6|M7|
|---|---|---|---|---|---|---|---|
|Meso-Region|0.00001|0.00003|-|-|-|-|-|
|Micro-Region|0.00007|0.00006|-|-|-|-|-|
|Municipality|0.00296|0.00178|0.00408|-|-|-|0.00187|
|District|-|0.00229|-|0.00630|-|0.00415|0.00223|
|E.A.|0.01042|0.00948|-|-|0.01190|0.00948|0.00948|
|Household|0.42292|0.42315|0.43252|0.43173|0.42315|0.42316|0.42316|

Source: Census and authors’ calculation. Models M1 to M7 represent models with different nested error structures.

# Table 4b:  Percentage <u>contribution to total variance: Preferred Model</u>

|Locality-type|M1|M2|M3|M4|M5|M6|M7|
|---|---|---|---|---|---|---|---|
|Meso-Region|0.0|0.0|-|-|-|-|-|
|Micro-Region|0.0|0.0|-|-|-|-|-|
|Municipality|0.7|0.4|0.9|-|-|-|0.4|
|District|-|0.5|-|1.4|-|0.9|0.5|
|E.A.|2.4|2.2|-|-|2.7|2.2|2.2|
|Household|96.9|96.9|99.1|98.6|97.3|96.9|96.9|

Source: Census and author’s calculation. Models M1 to M7 represent models with different nested error structures.

# Table 4c:  Percentage <u>contribution to total variance: Naive Model</u>

|Locality-type|M1|M2|M3|M4|M5|M6|M7|
|---|---|---|---|---|---|---|---|
|Meso-Region|0.98|1.08|-|-|-|-||
|Micro-Region|1.02|1.04|-|-|-|-||
|Municipality|1.48|0.91|3.63||-|-|2.78|
|District|-|0.99|-|4.77|-|4.10|1.14|
|E.A.|3.48|3.25|-|-|5.96|3.23|3.24|
|Household|93.04|92.73|96.37|95.23|94.04|92.68|92.83|

Source: Census and author’s calculation. Models M1 to M7 represent models with different nested error structures.

> 10 Note while it is not typically possible to examine the contribution of multiple higher-level location effects absent the availability of the kind of data we use here, conventional surveys do sometimes employ a multi-clustered sampling design.  In such cases one can carry out a similar investigation of the separate contribution of the EA-level effect relative to a single higher, “district” or “municipality” level effect.  Experience with poverty mapping applications in other settings suggest that the pattern observed here, of an overwhelming share deriving from the EA level, is quite general.

> 11 See also further below.

18
percent in European assets, 1 percent in Japanese assets, **1.3** in Asian assets, and **0.9** percent in Latin American assets. Although the percentage dedicated to emerging markets is small, the large size of the **U.S.** mutual fund industry implies that the dollar amount held in assets from emerging countries is significant.

Turning now to world funds, the number of funds in this category grew from **52** to **273** in the **1991-1998** period. The total net asset value increased from **$16** billion to **$125** billion over the same period. Most assets are allocated in stocks (between **83** and **87** percent). Bond holdings are small (between 2 and 4 percent). At the end of **1998,** world funds' assets were 34 percent from the **U.S.** and Canada, **37** percent from Europe, 4 percent from Japan, **3** percent from Latin America, and **6** percent from Asia.

The most rapidly growing fund categories since **1991** are the emerging market funds, Asia funds, and Latin American funds (though these fimds did start from a low level). Emerging market funds increased from **3** funds in **1991** to **165** in **1998.** The total net asset value increased from $142 million to **_$13.5_** billion over the same period, with a peak of **$17** billion in late **1997.** These funds hold between **70** and **90** percent in stocks and the rest in short-term interest-bearing securities ("cash"). In late **1998,** 84 percent was allocated to stocks. Regarding the geographic allocation, **30** percent was allocated in Asia, **30** percent in Latin America, and **11** percent in Europe.

Asia funds, excluding specialized Japanese funds, grew from **11** funds in **1991** to 154 in **1998.** Their net assets grew from **$1** billion to **$6.5** billion over the same period. Nevertheless, due to the Asian crisis, the total assets of Asian funds deteriorated during

> 14 Morningstar classifies the assets as being invested in one of six Countries/Regions: **U.S.** and Canada, Japan, Asia (ex Japan), Europe, Latin America, or other. Holdings are classified in one of four asset classes: cash, stocks, bonds, or other.

12
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42
**<u>Table 1: Evolution of number and area under agric. and non-agric. estate leases</u>**

|||**1909-64**|**1965-86**|**1987-94**|**1995-2016**|**By sub-**|**period**|
|---|---|---|---|---|---|---|---|
|||||||**1995-2006**|**2007-16**|
|**Panel A: Cumulat**<br>_Total_|**ive figures**|||||||
|Area transferred|1000 ha|17.95|259.12|779.05|960.06|864.62|960.06|
|No. of leases|No.|648|5,281|27,282|39,695|33,252|39,695|
|_Agric._||||||||
|Area transferred|1000 ha|16.73|254.05|772.85|944.18|853.34|944.18|
|No. of leases|No.|155|2,432|23,439|27,321|26,202|27,321|
|_Non-agric._||||||||
|Area transferred|1000 ha|1.23|5.08|6.21|15.89|11.29|15.89|
|No. of leases|No.|493|2,849|3,843|12,374|7,050|12,374|
|**Panel B: Period in**|**crements**|||||||
|_Total_||||||||
|Area transferred|1000 ha|17.95|241.17|519.93|181.01|85.57|95.44|
|No. of leases|No.|648|4,633|22,001|12,413|5,970|6,443|
|Mean lease size|ha|29.24|52.80|23.79|14.87|14.84|14.89|
|_Agric._||||||||
|Area transferred|1000 ha|16.73|237.32|518.82|171.33|80.49|90.84|
|No. of leases|No.|155|2,277|21,007|3,882|2,763|1,119|
|Mean lease size|ha|123.90|105.15|24.73|44.13|29.43|81.47|
|_Non-agric._||||||||
|Area transferred|1000 ha|1.23|3.85|1.13|9.68|5.08|4.60|
|No. of leases|No.|493|2,356|994|8,531|3,207|5,324|
|Mean lease size|ha|2.56|1.67|1.30|1.16|1.67|0.87|
|**Panel C: Annual i**<br>_Total_|**ncrements**|||||||
|Area/year|1000 ha|0.32|10.96|64.99|8.23|7.13|9.54|
|Leases/year<br>_Agric._|No.|12|211|2,750|564|498|644|
|Area/year|1000 ha|0.30|10.79|64.85|7.79|6.71|9.08|
|Leases/year<br>_Non-agric._|No.|3|104|2,626|176|230|112|
|Area/year|1000 ha|0.02|0.18|0.14|0.44|0.42|0.46|
|Leases/year|No.|9|107|124|388|267|532|

_Source:_ Own computation from the National Geographical Estates Database.

16
**<u>Table 5: Estimates of program effects on labor supply</u>**

||**Self-e**|**mployment**|**Cas**|**ual work**|**Salaried**|
|---|---|---|---|---|---|
||**Farm**|**Non-farm**|**Farm**|**Non-farm**||
|||**P**<br>|**anel A: All worke**<br>|**rs**<br>||
|MNREGS * post|0.050***|-0.009|0.009|0.034***|-0.003|
||(0.013)|(0.006)|(0.011)|(0.008)|(0.005)|
|Obs.|38,714|38,714|38,714|38,714|38,714|
|R-squared|0.504|0.333|0.504|0.390|0.286|
|||**Pan**|**el B: Male vs. fe**|**male**||
|MNREGS * post * male|0.071***|-0.012|0.042***|0.047***|-0.015**|
||(0.013)|(0.009)|(0.013)|(0.012)|(0.007)|
|MNREGS * post * female|0.031**|-0.004|-0.021*|0.025***|0.008*|
||(0.015)|(0.006)|(0.011)|(0.007)|(0.005)|
|F-statistics|8.908|0.893|34.83|3.376|11.35|
|P-value|0.00285|0.345|3.81e-09|0.0662|0.000758|
|Observations|38,714|38,714|38,714|38,714|38,714|
|R-squared|0.505|0.334|0.506|0.396|0.287|
|||**Panel**|**C: Size of landh**|**olding**||
|MNREGS * post * landless|-0.002|-0.007|0.057**|0.037**|-0.006|
||(0.014)|(0.012)|(0.023)|(0.017)|(0.008)|
|MNREGS * post * small|0.051**|0.005|-0.021|0.043***|-0.009|
||(0.023)|(0.010)|(0.020)|(0.015)|(0.008)|
|MNREGS * post * medium|0.039*|-0.014|-0.017|0.026**|-0.009|
||(0.022)|(0.010)|(0.018)|(0.012)|(0.008)|
|MNREGS * post * large|0.008|-0.019**|-0.013|0.015|0.019**|
||(0.025)|(0.010)|(0.014)|(0.011)|(0.008)|
|Observations|38,714|38,714|38,714|38,714|38,714|
|R-squared|0.541|0.308|0.475|0.361|0.260|

_Notes:_ Household and year fixed effects as well as controls included but not reported. Standard errors, clustered at household level, in parentheses; *** p<0.01, ** p<0.05, * p<0.1 19
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