rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Key Conditions for Hybrid Offer Regimes**

||**United**<br>**States**|**EU**|**Brazil**|**Chile**|**India**|**Israel**|**Malaysia**|**Thailand**|
|---|---|---|---|---|---|---|---|---|
|**Key**<br>**conditions**<br>**for initial**<br>**offering**|Initial<br>purchaser<br>who acts in<br>the capacity<br>of an<br>underwriter,<br>typically an<br>accredited<br>investor,<br>with the<br>intention to<br>sell to QIBs<sup>a</sup>|5 possible<br>conditions,<br>including<br>qualified<br>investors|Max. 50<br>QIBs|Qualified<br>investors|Max. 200<br>investors,<br>excluding<br>QIBs. For<br>financial<br>institution<br>issuers –<br>max. 49<br>investors<br>in total|Qualified<br>investors|AIs,<br>HNWEs<br>and<br>HNWIs|AIs, which<br>include<br>HNW and<br>institutional<br>investors|

_Note:_ AI = accredited investor; HNW = high net worth; HNWE = high-net-worth entity; HNWI = high net worth individual; QIB = qualified institutional buyer.

a. In the United States, the HBOR is constituted by the combination of the pure private placement regime (Regulation D or Section 4(a)(2) of the 1933 Act) and the resale of private securities (Rule 144A). Thus, the key conditions for the HBOR are derived based on offers that are ultimately targeting the 144A treatment, or are intended to be sold to QIBs. The private placement regime on its own stipulates that securities can be sold to an unlimited number of accredited and up to 35 sophisticated investors. Thus, offers targeting the 144A treatment are initially purchased by an underwriter, typically an accredited investor, with the intention of distributing the securities to QIBs. The QIB definition is stricter than that of accredited investors.

## **Box 2. Accredited Investor definition in the US**<sup>*****</sup>

1. A bank, insurance company, registered investment company, business development company, or small business investment company;

2. An employee benefit plan, if a bank, insurance company, or registered investment adviser makes the investment decisions, or if the plan has total assets in excess of $5 million; 3. A charitable organization, corporation, or partnership with assets exceeding $5 million; 4. A director, executive officer, or general partner of the company selling the securities; 5. A business in which all the equity owners are accredited investors; 6. A natural person who has individual or joint net worth together with a spouse that exceeds $1 million excluding primary residence; 7. A natural person with income exceeding $200,000 or joint income with a spouse exceeding $300,000 in each of the two most recent years; or 8. A trust with assets in excess of $5 million, not formed to acquire the securities offered whose purchases a sophisticated person makes.

# ______________

*This is an abridged definition available on the US SEC website and further modified by the author. A full definition is available on the Electronic Code for Federal Regulations (e-CFR) website.

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26 internationally accepted and standardized accountancy practices for IIFSs reduces the ability to make comparisons across markets, and may reduce consistency in ratings.<sup>66</sup> Overcoming these financial information challenges should promote a _competitive environment_ for IIFSs, and thereby enhance the contribution that competition can bring to sound IIFS CG.<sup>67</sup> There are three alternative approaches to empowering and protecting UIA holders. Rights that normally belong to equity-holders could be extended to UIA holders. Or, moving in the opposite direction, UIA holders could be granted full debt-holding status and the protection it carries. Alternatively, the _sui generis_ status of UIA holders could be maintained, provided that specific governance structures for the protection of UIA holders’ interests are in place.

The first option would be the extension of shareholders’ rights and duties to UIA holders. Given their equity-like investment, it may be argued that UIA holders should be on an equal footing with shareholders and thus be granted the right to have a voice by electing board representatives. This measure would increase their ability to air their demands and concerns with management. It would also satisfy these depositors’ demand for greater involvement in the strategic management of the IIFS.<sup>68</sup> If extending shareholders’ rights to UIA holders is deemed impractical, depositors’ protection may be an alternative option. However, the moral hazard argument against deposit insurance schemes would need to be addressed. Furthermore, the PLS nature of investment accounts prevents the application of deposit insurance in its present form, and a _Shariah_ -compliant version would need to be developed.

> 66 For example, as we can see onn table IV, AAOIFI’s standards are not broadly endorsed by regulatory agencies.

> 67 Grosfeld and Tressel (2001) provide evidence that competition has an important complementary effect where good CG mechanisms are already in place.

> 68 In a survey of IIFS consumers’ preferences, Chapra and Ahmed (2002) record an interest by depositors to be involved in the strategic management of the bank.
Sahara (a disputed rather than dependent territory) and have summed up Jersey and Guernsey (forming Channel Islands). Stock data for 33 dependent territories are provided in Table 3.

- As the second step of our analysis consists in comparing the numbers of emigrants and residents by educational attainment, we have to consider homogeneous groups. Working on the working-aged population (aged 25 and over) maximizes the comparability of the immigration population with data on educational attainment in source countries. It also excludes a large number of students who temporarily emigrate to complete their education. We cannot control for graduate students aged 25 and over completing their schooling<sup>15</sup> . As it will appear in Table 1 the age group is slightly different in a limited number of countries.

Building an aggregate measure of emigration per educational attainment requires a rule for sharing the unknown values. At the OECD level, the number of migrants whose educational attainment is not described amounts to 1.287 million, i.e. 2.2 percent of the total stock. Two reasonable rules could be considered: either unknown values can be distributed in the same way as the known values or they can be assimilated as unskilled. We combine both rules depending on the information available in the receiving country. For receiving countries where information about immigrants' education is available, we assimilate the unknowns to unskilled workers<sup>16</sup> .

For example, Australian immigrants who did not mention their educational attainment are considered unskilled. In receiving countries where no information about skill is available, we transpose the skill distribution observed in the rest of the OECD area or in the neighboring region. For example, if we have no information about the skill structure of immigrants to Iceland, Algerian emigrants to Iceland are assumed to be distributed in the same way as Algerian emigrants to the rest of the Scandinavian countries. The assumptions will be discussed below.

Formally, the stocks of emigrants of skill _s_ from country _j_ at time _t_ ( _M t_<sup>•</sup> , _sj_ ) are obtained as follows:

> 15 Carrington and Detragiache (1998) used data from the Institute of International Education to estimate the number of graduate students completing their schooling in the United States. We consider that some of these students aged 25 and over receive grants and can be considered as workers (researchers).

> 16 Country specific data by occupation reveal that the occupational structure of those with unknown education is very similar to the structure of low-skilled workers (and strongly different from that of high-skilled workers). See Debuisson et al. (2004) on Belgian data.

10
**Table 6. Production Function Estimates for 1993-95 and 1991-95 Dependent variable: Log(Production Value minus Materials Cost)**

|**Variable**|_Levels_<br>Coefficient|**1993-19**<br> <br>z-statistic|**95**<br>_First diffe_<br> Coefficient|_rences_<br>z-<br>statistic|_Leve_<br> Coefficient|**1991-**<br>_ls_<br> z-statistic|**1995**<br>_First diffe_<br> Coefficient|_rences_<br> z-statistic|
|---|---|---|---|---|---|---|---|---|
|Log(Capital assets)|0.234 *|15.3|0.094*|3.3|0.202*|17.3|0.084*|5.0|
|Log (Employment)|0.833 *|33.0|0.455*|8.8|0.833*|37.8|0.473*|10.3|
|CIMO Variable|-0.607 *|-10.9|-0.004|-0.2|-0.574*|-11.0|0.019|0.8|
|Constant|8.721 *|41.9|0.066|1.1|8.651*|49.0|0.162*|3.1|
|1995 dummy|-0.166 *|-10.0|-0.274*|-9.8|-0.045*|-2.4|-0.314*|-12.4|
|No. of observations|3266||2043||4781||3392||

_Note:_ Industry dummy variables included but not reported. * Denotes significance at 5 % level.

# **V.  Summary and Conclusions**

To summarize, the empirical evidence provides qualified support for the proposition that Mexico’s CIMO program of integrated training and technical assistance has generally been effective in improving the performance of micro, small and medium size companies. Compared to the control group, CIMO firms increased investments in worker training, had higher rates of capacity utilization, and were more likely to adopt quality control practices. The evidence suggests that these improved intermediate outcomes were associated with increased productivity growth among SMEs that participated in the CIMO program, impacts that were especially strong in the 1991-1993 period. The effects of the program were not apparent in the 1993-95 period, though this result may simply reflect the poor choice of a control group.

This re-examination of the data demonstrates that estimates of program impacts using the same data sets can vary dramatically depending upon the empirical methodology used. The productivity effects of CIMO participation were invariably negative when production functions were estimated in levels, and positive or mixed when estimated in first-differences to address the issue of self-selection bias. These results highlight several lessons. First, it is critical to select an _appropriate control group_ for the program beneficiaries, and to collect panel data for both groups in order to allow comparisons of pre- and post-program participation outcomes. Such a quasi-experimental design is required if the net impacts of the program are to be estimated. Second, there is need to account for and _address sample selectivity issues_ that might arise with the choices firms make about program participation. To the extent that more or less productive 14
significance are very similar to the results in panel A, so that we still obtain the same results even holding occupation fixed.

The quantity numbers we have are for new contracts issued. Typically new hires are 38% of the total contracts issued each year, with rehires constituting the remainder. Micro data on rehires was not available from the POEA, preventing us from examining the wages for this group. Nevertheless, annual destination country level data on total quantities of migrants are available from 1998 to 2009 in the POEA’s Compendium of Overseas Foreign Worker Statistics (Philippine Overseas Employment Administration, 2005-2009). We subtract the total new hires from our micro data from these totals to obtain data on rehire numbers by year.

We use this rehire and total contract worker data to examine the robustness of our migrant quantity results to the measure of migrant flows used. Table 3 presents the results. First, column 1 re-estimates equation 1 on our micro new hire data over the shortened time period 1998-2009. The point estimate suggests an even higher elasticity of quantities to GDP over this shorter period, but we cannot reject equality with our point estimate over the full sample. Columns 2 and 3 then show the same elasticity for rehires and for total migrant contract workers respectively. The point estimates are positive and significant in both cases, and we cannot reject equality of the total and rehire responses.

The stock of migrant workers at origin depends on both the flows of migrant workers, and how long these workers stay. The elasticity of the stock of contract workers with respect to GDP may therefore deviate from that of the flow if the duration of contracts varies with economic conditions at origin. Our database contains the duration of each new contract issued, and in column 4 of Table 3 we test whether the contract length (in months) varies significantly with GDP. The effect is statistically insignificant, and the point estimate is small, suggesting a 1 percent increase in GDP only increases contract duration by 0.16 months. As a result, we conclude that the stock of contract workers is likely to behave similarly to the flow in terms of its responsiveness to GDP.

We also examined the robustness of our results to issues of timing, to account for the possibility that labor demand either anticipates changes in GDP in the next year, or reacts slowly to changes that have occurred. We do this by adding leads and lags of log GDP to equation (1).

14
Tables 5 and 6 give the structure of the adult population (25+) by country group and region of origin.

The world adult population increased from 2.559 to 3.180 billion people between 1990 and 2000 (+24.3 percent). This global growth rate hides important changes across education categories. While the unskilled population increased by 19.7 percent, the skilled populaiton rose by 52.5 percent. Consequently, the proportion of post-secondary educated workers in the world adult population increased from 9.1 to 11.1 percent over the period. Although women still face unequal access to education in many countries, is worth noticing that women’s share in the skilled adult population increased from 40.4 to 44.5 percent (their share in the unskilled population remains above 55 percent). Our data reveal that gender gaps in human capital are strongly linked to the level of economic development. The share of women in the skilled population is still very low in low-income countries (30.3 percent) and in the least developed countries (28.5 percent). The educational achievement of women is particularly worrisome in Western Africa (13.3 percent) and Northern Africa (14.7 percent). Figure 4 compares the average annual growth rates of women’s total/skilled and men’s skilled adult population by region over the decade.

Figure 4. Annual average growth rate of total/skilled adult population (25+) Data by region (1990-2000) <!-- Start of picture text --> 20%<br>18%<br>16%<br>14% Women total LF<br>Women skilled LF<br>12%<br>Men skilled LF<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Southern Africa Northern Africa Others Oceania South-East. Asia Western Asia Central America South America Southern Asia Caribbean Eastern Asia North America Central Asia Eastern Africa Southern Europe Middle Africa Austr + New Zeal. Eastern Europe Northern Europe Western Europe Western Africa<br><!-- End of picture text --> 19
WPS3331

# **Fuelwood Consumption and Participation in Community Forestry in India**

### Sushenjit Bandyopadhyay and Priya Shyamsundar Environment Department, World Bank

_Abstract: Decentralized forest management is an important policy issue in India and elsewhere. Yet, there are few careful studies of the impacts of community forestry. We try to fill this gap by analyzing National Sample Survey data from 524 villages in five states in India. Our analysis seeks to answer two key questions: a) Who participates in community forestry and what are the determinants of participation? and b) What is the impact of participation on household fuelwood consumption? We find that proximity to forests, leadership, and fuelwood dependence are significant factors in explaining village participation in community forestry. Household participation is strongly correlated with scarcity, a result that has implications for a recent policy to expand community forestry from degraded to less degraded forests. Our most important findings are that fuelwood consumption and participation are linked, and household participation has a significant positive impact on consumption. However, the presence of a village level forestry institution itself does not have a direct effect._

### World Bank Policy Research Working Paper 3331, June 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._

## **Acknowledgements**

We are grateful to the Trust Fund for Environmentally and Socially Sustainable Development for generous financial support. Our thanks also go to Kirk Hamilton, Team Leader, Policy and Economics Team for his support. We thank E. Somanathan, Urvashi Narain, Shreekant Gupta, Peter Jipp, Nalin Kishor, Rama Reddy, Limin Wang, and other colleagues who gave us detailed comments, and Dina Umali-Deininger for making the NSSO data available to us.
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and we should pick the one that deviates as little as possible from the initial distribution (��∗ ). The solution to this problem is given by: where �<sup>�</sup> is the Lagrange multiplier for the constraint _E(X)_ , and Ω is the normalizing factor to scale the new population to the target size. In the simulation process we impose several population constraints which result from different growth rates by gender age group and governorate brackets.

The internally displaced population movement is taken into account after the above mentioned adjustment in weights has made. The input for this step is an out‐in flow matrix from the International Organization of Migration (IOM) which estimates the total number of families who had crossed governorate borders between June and December 2014. The lack of information about the characteristics of this population implied the following procedure: first randomly selecting families from governorates of origin like Anbar, Nineveh or Salahadin, and then allocating them to host governorates for instance, Dohuk, Erbil or Baghdad. It is relevant to note in this case that families and households are considered equivalent and all members of households classified as IDP are moved.

The rest of this simulation process follows Olivieri, S, et al. (2014).<sup>16</sup> Briefly, it uses predictions of behavioral models estimated on the baseline household data (as in step 1 above), and generates household and individual‐level predictions for employment, earnings and public and private transfers for 2014. Since an individual’s labor income depends on his/her employment status and labor earnings, how the output change in a particular sector is apportioned between employment change, earnings change and adjustments across sectors depends on how responsive (elastic) employment in that sector is to output change. It also implies that at the household level, the extent of the impact depends on the size of the aggregate change at the sector level and the demographics and characteristics of household members, which influence the labor force status and earnings of household members after the change.

To simulate changes in _non‐labor_ income, projections of changes in public transfers are assumed to grow at different rates accordingly to the simulated scenario, while private transfers are assumed to grow at the rate of aggregate GDP for the relevant period.

# **Assessment**

The final step is the process by which the information on individual employment status and labor income, as well as on non‐labor income at the household level, is used to generate income or consumption distributions and calculate various poverty and distributional measures that can then be used to compare different scenarios. There are two steps needed to obtain the final simulated distribution.

The first step consists in using price projections to adjust the poverty line to reflect the difference in food and non‐food inflation rates between baseline and projected year. Since the poverty line is typically

> 16 For further details about how the labor and non‐labor income components has been simulated see Olivieri, S, et al. (2014).

9