rafmacalaba/data-use-mentions-extended huggingface dataset:
French company law recognizes two main types of companies – commercial and civil. Commercial companies are of three types: unlimited liability companies, those where the liability of the shareholders is limited to their contribution to the capital of the company and those which comprise mixed shareholders- that is those who are personally liable and those shareholders whose liability is limited. A civil company may not engage in any commercial activity.

A simple comparison of the some of the corporate laws in different EU countries reveal differences in tax regulations. The corporate tax rate is 25% in Germany (though some types of companies have a special status), in the United Kingdom (UK) there is a two-tier tax rate with corporations whose profits are less than GBP 1.5 million paying 19% and the others 30%. The corporate tax rate is around 35% in the Netherlands (Richard, ed., (1992- 2004)).

The procedures required to start a business also vary. In France, minimum capital requirements amount to 7,500 Euros for a private limited liability company. In Germany, the minimum capital requirement for the same is 25,000 Euros and in the UK there is no minimum capital requirement. To register and clear the proposed name of the company requires eight days and 38 Euros in France, and in Germany it requires one day and there is no fee. To register new firms, there are altogether 10 distinct procedural/legal requirements in France, requiring over 53 days to complete at a total cost of 704 Euros. In the UK, the comparable numbers (at time of writing) are 6, 18 and $US 264, and for Germany, 9 steps, 45 days and 1,425 euros.<sup>4</sup>

> 4 Source:  Doing Business Database, The World Bank 2004.

15
both the basic employment outcomes and the determinants of household welfare using very simple techniques. We discuss what these problems mean for economic analysis relating these employment outcomes to other key variables at the household, sectoral or macro level in order to analyze the performance of economic development policy and processes.

The outcomes focused on here are the measurement of i) LFPR, ii) primary employment status, and iii) multiple economic activities and the related issues of underemployment and household livelihood portfolios. Although relevant for analysis, we do not discuss the issue of measuring individual and household earnings as we had trouble finding a baseline from which to work.

# **3.2 The measurement of labor force participation**

Until the 2009/2010 UNHS survey (except in the 2002/3 survey), in at least one employment status question, respondents were simply asked: “What is your primary activity?” without any preparation to clarify the meaning of “activity” – that is, work (Table 2). The problem was compounded by the inclusion of _inactivity_ response options alongside the activity response question – which often happens with failure to screen. We cannot isolate the bias associated with the inactivity response from the bias associated with lack of screening, so we consider them together, first with the earliest survey and then with the 2005/6 survey.

In the IHS 1992/93 survey, no screening was used and all employment status questions (primary and secondary) included inactivity responses such as “domestic duties”, “too young/old to work” and “student”. The inclusion of inactivity response options in employment status questions encouraged some respondents to provide these non-economic activity responses as their _primary_ employment status, and only list their economic activity as a secondary activity.<sup>15</sup> Table 3 shows the magnitude of the bias induced by this methodology, and it is large. One indication of the size of the bias is found by comparing primary and secondary activity. Many of those who
benchmark has modestly outperformed futures prices at horizons 3, 6 and 12 months. On the other hand, Bowman and Husain (2004) showed for several metals that models that incorporate futures prices in an error correction model performed better than models based entirely on historical data or judgment, in terms of directional accuracy and precision, particularly at longer forecast horizons. Complementing futures price data with other information—such as industrial production, exchange rate dynamics, commodity currencies, international metals stock index, structural breaks, and short-run common-cycle restrictions—has further improved forecast performance (Gong and Lin 2018; Issler, Rodrigues and Burjack 2014; Pincheira-Brown and Hardy 2019; Pincheira-Brown and Hardy 2021; Pincheira-Brown et al. 2021).

# **2.2.2 Univariate time series models**

Univariate time series models have performed better than no-change forecasts but underperformed other quantitative methods (Rubaszek, Karolak and Kwas 2020; Alipour, Khodaiari and Jafari 2019). For aluminum, copper, nickel, and zinc, univariate autoregressive models delivered significantly better forecasts than the no-change approach (Rubaszek, Karolak and Kwas 2020).

However, univariate time series models have generally underperformed other quantitative methods. For copper, the forecast performance of ARIMA and no-change forecasts was inferior to that of neural networks, dynamic averaging and selection models and stochastic differential equations (Alipour, Khodaiari and Jafari 2019; Buncic and Moretto 2015; Lasheras et al. 2015). For aluminum, VECMs have had better out-of-sample forecast accuracy than ARIMA and VAR models (Castro, Araujo and Montini 2013). For aluminum and nickel prices, a modified grey wave forecasting technique—a univariate technique that explicitly accounts for irregular fluctuations in time series—performed better than no-change or ARMA methods (Chen, He and Zhang 2016). For aluminum, copper, lead, and zinc, wavelet-autoregressive integrated moving average (ARIMA)-based models have outperformed traditional ARIMA models in terms of forecast accuracy (Kriechbaumer et al. 2014). For lead and zinc, He et al.
sup>11</sup> has highlighted biodiversity as a valuable entry point for intergroup dialogue and cooperation. Again, however, reliable and comparable biodiversity data are critical for the success of such initiatives. Accurate data can provide common ground for all stakeholders by promoting a clear understanding of biodiversity status, trends, and threats. Such information helps depoliticize discussions by focusing on objective information that can serve collaborative activities. Consistent and comparable data can also support effective monitoring and evaluation of biodiversity initiatives, ensuring that progress can be measured and adjusted as needed.

# **Conclusion**

Given the unprecedented loss of biodiversity, it is essential for every country and territory to engage in conservation efforts to minimize further degradation and its impact on human wellbeing. Consistent, dependable, location-specific biodiversity data are crucial for identifying species at risk, monitoring environmental changes and informing policy decisions. These factors

> 11 <u>htps://www.worldbank.org/en/topic/environment/publicaton/defueling-confict-environment-andnatural-resource-management-as-a-pathway-to-peace</u>

22
**Figure 3.1  Urea use per/ha and land size, 2007**

<!-- Start of picture text --> 0 1 2 3<br>Land size (ha)<br>600<br>400<br>200<br>0<br><!-- End of picture text --> _Source_ : World Bank staff calculations.

**Table 3.1  Rice yields  and urea/SP36 use by** **<u>quintiles of land size, 2007</u>**

|Quintile|Rice Yields<br>(kg/ha)|Urea<br>Use<br>(kg/ha)|Intensity<br>Urea/Yields|SP36<br>Use<br>(kg/ha)|Average<br>Land Size<br>(ha)|
|---|---|---|---|---|---|
|1|4,836|343|7.1%|164|0.12|
|2|4,596|291|6.4%|151|0.25|
|3|4,447|268|6.1%|136|0.41|
|4|4,053|224|5.6%|118|0.73|
|5|3,490|170|5.0%|88|1.97|
|Average/Total|4,283|259|6.0%|132|0.7|

_Source_ : World Bank staff calculations.

The data on fertilizer consumption in Indonesia show that farmers use more urea than recommended by the MoA. While the optimal levels of urea required per hectare vary according to soil quality across islands in Indonesia, the MoA recommends using 200-250 kg/ha of urea and 50-100 kg/ha for SP36 fertilizer
24 Table 6 lists the growth, redistribution and residual components of poverty changes for the subperiods 1981-1993 and 1993-2004, as well as for the whole period 1981-2004. This is done both for Rocha (1993)’s region-specific poverty lines, and for the “administrative poverty line” of R$100 per capita per month. In the discussion, we refer predominantly to the administrative poverty line numbers.

**Table 6: Brazil 1981-2004: Decomposition of Changes in Poverty into Growth and Redistribution Components**

||**Regi**|<br>**onal Poverty Li**|**ne**<sup>**1**</sup>|**Adm**|<br>**inistrative Povert**|**y Line**<sup>**2**</sup>|
|---|---|---|---|---|---|---|
||Headcount|Poverty Gap|FGT(2)|Headcount|Poverty Gap|FGT(2)|
|**1981**|0.399|0.163|0.090|0.296|0.124|0.070|
|**1993**|0.466|0.215|0.130|0.326|0.151|0.093|
|**Observed Change**|**0.067**|**0.052**|**0.040**|**0.031**|**0.027**|**0.023**|
|**Growth**|0.023|0.012|0.007|0.017|0.009|0.006|
|**Redistribution**|0.048|0.039|0
We present below a list of generic household types that can be tailored to specific country contexts. At a minimum, these types should specify the sex and age ranges of household members, the relationships among the household members (for example, married or in consensual union, with and without dependents), and the employment or earning status of the principal adults in the household. Together, that information is important in establishing what the care and social reproduction needs of various households may be.

Constructing the household typology and undertaking the fiscal incidence analysis should follow a few steps. The first step is to compile information from censuses, household surveys, labor force surveys, and other sources, such as time use if available,<sup>21</sup> to get a big-picture understanding of the nature of gender inequalities in employment and social reproduction. The second step is to use this data to construct economic and demographic categories. A generic set of groupings that can be adopted in most countries is as follows:

# <u>Economic categories</u>

1. Single male/female member households, who are employed or not employed. In many countries, these are older retired adults.

2. Households with married couples or consensual unions.<sup>22</sup> This category should be divided into at least four subcategories: where both principal adults are employed, where only the principal male is employed, where only the principal female is employed, and where no adult is employed.

3. Households with working-age adults and dependents, disaggregated by which members are employed. Examples of this type of household may be an adult child living with an elderly parent or younger-generation children living with older-generation children (from the same parents).

## <u>Demographic categories</u>

Using the economic categories above, households should then be further classified by their care needs:<sup>23</sup>

1. without dependents and with dependents

2. total dependents 3. dependents disaggregated
the tax year 2016. The aggregation is conducted on regions and the total national amount is equal to €238 billion;

- Production taxes redistributed on value added plus IRAP paid by public administrations. The production taxes are the sum of other taxes on production and negative taxes on production. The total collected production taxes are equal to €34 billion;

- IRES (CIT) is regionalized starting from national SAM 2016 data. The total national amount is equal to €30 billion;

11
have access to academic, technological, and financial resources required for their activities. As of August 2010, the RVC Seed Fund had awarded venture partner status to 58 companies from different regions of Russia. By late 2010, the network included about 100 venture partners. Nevertheless, companies implementing innovation projects at the earliest stages would receive up to 75 percent of the investment required from the Seed Fund, meaning they have to match the remaining 25 percent, which often proves difficult.

# **II.4. Intellectual Property Legal Framework**

Patent applications by Russian scientists – a prerequisite for subsequently commercializing research through licensing and start-ups based on intellectual property (IP) – increased from 2005 to 2009. However, the ratio patent per researcher is much lower than in more advanced economies. The number of patent applications averaged, in 1999–2007, only 0.05 per researcher. The average for the same period in the United States, for example, was almost three times as high, at 0.14.

Patent applications by Russian scientists are often made solely in Russia, that is, there is no protection in global markets which reflects a lack of an international commercialization strategy. In Russia only 4 percent of total applications are registered abroad, against, for example, 26 percent in Korea (calculations based on WIPO 2010<sup>10</sup> ). The large Russian home market, at least in certain product categories, may not push companies to foreign markets, and therefore they feel no need for IP protection abroad (Kaartemo 2009). Yet, truly groundbreaking inventions may indeed have an international market. The proportion of patents filed by foreigners in Rospatent<sup>11</sup> has steadily increased between 2005 and 2009, reaching 34% of total patent applications. While the success in getting the patent granted has increased for foreigners, reaching 67% in 2009, Russian applicants have an even higher and increasing success rate, reaching 92% in 2009 (Table 7).
excessive deficits in good times. Fiscal rules also tend to foster creative accounting, as governments seek to comply with the rules without making unpopular choices. More generally, fiscal rules strengthen the unintended effects of accounting choices. Rules that limit the cash deficit, for example, can encourage the build-up of spending or tax-refund arrears and discourage spending on revenue-generating public investments (Easterly and Servén 2003, Blanchard and Giavazzi 2008, Servén 2008). At worst, governments may adopt fiscal rules with little intention of following them, perhaps to please international advisers and lenders (see Andrews 2003, Schick 2013).

**To work, fiscal rules need the support of public financial management** . It must be possible to measure whether the government has complied with the rules, so reliable accounts and statistics are needed. The measurements should also be credible to outsiders, so it is helpful if accounts and statistics follow international standards and are independently audited or prepared. It is easier for a government to follow fiscal rules if it has unbiased and reasonably accurate forecasts and understands the risks surrounding those forecasts. Having an independent fiscal institution make or check the forecasts can help ensure that the government’s predictions of compliance with rules are believable. Since most tax and spending programs cannot efficiently be changed overnight, effective systems of medium-term planning are valuable. And good budgeting and expenditure control can help the government to stick to the spending plans it has made.

**The main alternative to fiscal rules is to rely on quantitative fiscal targets and good public financial management.** Well-designed budgeting, forecasting, accounting, and fiscal statistics can reveal the long-term fiscal costs of short-sighted policies, and a focus on prudent fiscal policy can be encouraged by a procedural rule requiring the government to announce its fiscal targets and to report publicly on whether it has achieved them. This approach is not legally constraining, but by raising the political costs of running of an imprudent fiscal policy, it may have an effect similar to that of fiscal rules. Indeed, though
Lastly, Figures 3 and 5 underscore that reporting has tended to be less dependable for the most recent years. While there is significant interest in the latest data, it is also more prone to discrepancies as recent data are continuously updated in the UN Comtrade systems.

# **Discrepancies increase with product disaggregation.**

Discrepancies in merchandise trade statistics tend to intensify with greater levels of product detail, suggesting misclassification or misinvoicing. We support these findings by comparing differences in successful mirroring between highest and lowest level of product granularity within HS and SITC nomenclatures. We find that an increase of product granularity significantly decreases the number of successfully mirrored records and the portion of trade value that can be verified (Figure 6). This pattern was consistently observed across the various nomenclatures considered in this study. Two of them are highlighted in Figure 6 and seem to suggest less reliable recording of higher-resolution data. Consider, for instance, percentage of dual recording for H0 nomenclature in 2017. At aggregate level, 56% of records demonstrate dual recording. However, when zooming into a more granular 6-digit level, this figure drops to 39%.

Typically, some countries will be impacted by product disaggregation to a larger extent. Figure 7 shows the average annual per country reduction in successful mirroring that occurs in disaggregated data for the selected nomenclatures. In the H0 nomenclature for 2017, the rate of successfully mirrored records at the most detailed level fell by an average of 37% compared to the aggregated level. Similarly, the share of verifiable trade value decreased by an average of 15% at the most detailed level. Consequently, countries displaying larger reductions in verifiable data at finer levels of product detail are likely to see more substantial distortions in their export and import structures.

A common belief is that dual recording discrepancies at broader categorizations often stem from errors in attributing trading partners. In contrast, discrepancies at finer levels typically indicate challenges in correct product categorization. This divergence in matched transaction percentages across product granularity levels is crucial to note since many applications
# **3. Study design and context**

The data used in this paper were collected by the Institute of Statistical, Social and Economic Research (ISSER) of the University of Ghana and the World Bank during the main rainy season (roughly March to September 2015) in the Mampong Municipal, Ejura Sekyedumasi, Nkoranza South, and Pru districts of Ghana. A random sample of 720 agricultural households was selected from 20 enumeration areas (henceforth denoted as villages) and then randomly assigned to one of three alternative survey designs. All households were administered a baseline survey at the beginning of the season, which collected a roster of plots and household members working in agriculture, and basic demographic and plot-specific information. After the main harvest, households were administered an endline survey, which was modeled after the design of the LSMS-ISA survey series (i.e. a multi-topic household survey with an agricultural production module).<sup>7</sup> In terms of capturing farm labor, the three survey designs differed as follows:

- **A. Weekly visit:** Following the baseline survey in March 2015, these households were visited weekly from April to September 2015, which in turn was followed by the endline survey in October/November 2015. At baseline (visit 0), households reported on the number of days worked and total number of hours worked, per person per plot, since starting preparing the plot for the season. During the season households were visited every week (visits 1-23), and asked to report on the number of hours worked on each day of the past week, per person per plot, and the range of activities (but not hours per activity).<sup>8</sup> At endline (visit 24), households report the total number of days and the average hours per day, per person per plot, since the last weekly visit. The design for this group minimizes recall periods and (mostly) avoids the need for complex aggregations
35

**Figure 3: Neonatal Mortality vs. Insecticide-Treated Net Ownership by Province and Urban/Rural Location by Year and Average Malaria Prevalence**

<!-- Start of picture text --> Graphs by Year of the DHS survey<br>2003 2008<br>3<br>2 15<br>9<br>12<br>78 4621410151 13 12 63 8 4 710 14 9<br>1<br>11<br>11 5 13 5<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Fraction of households with at least one ITN<br>Risk of malaria according to malaria transmission rate<br>low risk<br>intermediate risk<br>high risk<br>150<br>120<br>90<br>60<br>30<br>0<br><!-- End of picture text --> Source: Authors ̳ calculations using Demographic and Health Surveys microdata. Notes: Trends in the number of children who died before the age of 1 month. Measured per 1000 live births. For legend, see Figure 7. These figures are calculated using the actual live births that took place during the period 12-35 months before the survey date.
6 the actual costs of severance pay based on data from the two surveys examined in this paper are 10 to 14 percent of the costs suggested by the statutory regulations. Three possibilities could account for the discrepancy: employees are not aware of the statutory regulations; compliance by firms is low; enforcement of the regulations relating to severance pay appears to be low in Indonesia.

It seems likely that employers avoid paying severance by signing short-term contracts with employees, some of whom are unaware of their right to severance pay. Other employees may know of their rights but avoid involvement in the legal process to enforce their right due to delays and litigation costs. This paper does not examine these issues because we do not have the requisite data on knowledge, costs and delays. Gathering such information is an important direction for future research and could be achieved by adding supplementary questions into the surveys.

While the data do not permit us to directly examine the enforcement of severance pay regulations in Indonesia, we can explore the first two possibilities. Both surveys can directly address the concern raised by employee representatives: compliance of severance pay regulations by firms is low. Prior to the introduction of a set of survey questions about severance pay, the claims of employee representatives were speculative in nature. The two surveys that collected data on severance pay for the first time in 2007 and 2008 give us the opportunity to test the validity of this claim. The following section presents information on the scope of severance pay and compliance of pay regulations.

# **3.  Coverage and Scope of Severance Pay**

The Indonesian Family Life Survey (IFLS) collected data on severance pay for the first time in 2007, while the National Labor Force Survey (Sakernas) collected data for the first time in 2008. Three aspects of severance pay are described in the analysis below: coverage of and eligibility for severance pay; the scope of severance pay; and the compliance of current severance pay regulations.

# 3.1 _Coverage of and eligibility for severance pay_

Data from