rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 4.    Number of lines with actual rates above 20 and 30% and maximum rates by year and industry**

|**_Industry_**|||||**_Y_**|**_ear_**|||||
|---|---|---|---|---|---|---|---|---|---|---|
||**_20_**|**_01_**|**_20_**|**_02_**|**_20_**|**_03_**|**_20_**|**_04_**|**_2_**|**_005_**|
||**_>20_**|**_>30_**|**_>20_**|**_>30_**|**_>20_**|**_>30_**|**_>20_**|**_>30_**|**_>20_**|**_>30_**|
|**_Non-ferrous metallurgy_**|**_0_**<sup>**_a_**</sup>|**_0_**|**_0_**|**_0_**|**_1_**|**_1_**<br>30<sup>b</sup>|**_0_**|**_0_**|**_0_**|**_0_**|
|**_Chemicals and petrochemicals_**|**_8_**|**_3_**|**_14_**|**_10_**|**_19_**|**_13_**|**_17_**|**_10_**|**_14_**|**_5_**|
|||125||154||186||181||198|
|**_Mechanical engineering and metal-working_**|**_51_**|**_18_**<br>198|**_59_**|**_38_**<br>338|**_63_**|**_44_**<br>1270|**_64_**|**_48_**<br>293|**_64_**|**_47_**<br>278|
|**_Timber, wood, pulp and paper_**|**_25_**|**_6_**<br>69|**_29_**|**_12_**<br>94|**_31_**|**_21_**<br>78|**_34_**|**_17_**<br>79|**_30_**|**_17_**<br>63|
|**_Construction materials_**|**_2_**|**_1_**|**_6_**|**_2_**|**_8_**|**_2_**|**_5_**|**_0_**|**_7_**|**_1_**|
|||48||81||60||29||30|
|**_Light industry_**|**_161_**|**_68_**|**_244_**|**_107_**|**_271_**|**_144_**|**_228_**|**_81_**|**_173_**|**_59_**|
|||518||284||342||163||85|
|**_Food industry_**|**_256_**|**_93_**|**_295_**|**_101_**|**_370_**|**_158_**|**_370_**|**_163_**|**_341_**|**_162_**|
|||193||483||323||256||470|
|**_Other industries_**|**_2_**|**_1_**|**_3_**|**_3_**|**_3_**|**_3_**|**_4_**|**_3_**|**_4_**|**_3_**|
|||39||63||54||57||67|
|**_Agriculture and forestry_**|**_2_**|**_2_**|**_25_**|**_22_**|**_31_**|**_22_**|**_32_**|**_19_**|**_26_**|**_14_**|
|||58||63||78||64||62|
|**_Other goods-producing sectors_**|**_1_**|**_1_**|**_1_**|**_1_**|**_1_**|**_1_**|**_1_**|**_1_**|**_1_**|**_1_**|
|||124||99||109||76||54|
|_Notes: a Number of lines_|||||||||||
|_b The highest rate in % in the subcatego_|_ry_||||||||||

19
nongovernmental organizations such as Reporters Without Borders, Freedom House, and the Bertelsmann Foundation. Finally, an important category of data sources for us are commercial business information providers, such as the Economist Intelligence Unit, Global Insight, and Political Risk Services. These last two types of data providers typically base their assessments on a global network of correspondents with extensive experience in the countries they are rating.

The data sources in Table 1 are fairly evenly divided among these four categories. Of the 35 data sources, seven are from commercial business information providers and the remaining categories have nine data sources each. However, an important distinction is that the commercial business information providers typically report data for much larger country samples than our other types of sources. An extreme example is the Global Insight Business Conditions and Risk Indicators, which provides information on 203 countries in each of our six aggregate indicators. Primarily for reasons of cost, household and firm surveys typically have much smaller country coverage. Our largest surveys, the Global Competitiveness Report survey and the Gallup World Poll each cover around 130 countries in 2008, and several regional surveys cover necessarily smaller sets of countries. Some of the expert assessments provided by NGOs and public sector organizations have quite substantial country coverage, but others, particularly regionally-focused ones again have much smaller country coverage. The top two panels of Table 2 summarize the distribution of countrylevel data points from all of our underlying sources for each of the six indicators in 2008. The 2008 WGI are based on a total of 12,114 country level data points (after averaging multiple questions from individual sources), of which 42 percent come from commercial business information providers. The remaining data points are fairly evenly distributed between the remaining three types of data providers.

As in the past several years, we report the underlying data from virtually all of the individual data sources that go into our aggregate indicators at <u>www.govindicators.org.</u> The sources we have made available through our website are noted in Table 1. A number of our data sources, such as Freedom House and Reporters Without Borders have always been publicly available through the publications and/or websites of their respective organizations. Several of our other sources provided by commercial risk rating agencies and commercial survey organizations have only been available for a fee.

8
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# **4 A WAY FORWARD**

This section suggests principles that could guide developing countries in the regional upgrading and harmonizing of their standards and conformity assessment procedures.<sup>27</sup>

# **4.1 SUGGESTED PRINCIPLES FOR THE UPGRADING AND HARMONIZATION OF STANDARDS AND CONFORMITY ASSESSMENT**

**There is yet no clear best practice in the upgrading and harmonizing of standards and conformity assessment procedures.** However, it is possible to draw some common principles from RTAs’ past experiences and existing literature that can serve as guide to countries pursuing regional upgrading and harmonization of standards and conformity assessment procedures.  These principles are particularly relevant for RTAs integrated by developing countries with a low developmental stage and with low technological capacity that are seeking to gradually upgrade and harmonize standards at intermediate levels to get closer to international standards, rather than set them equal to international standards in one go.

- 1- **Prioritize:** A first step is to identify priority sectors for standards reform to keep costs low and gather momentum for reforms in other sectors.  The sectors to prioritize are those where trade costs arising from differences in standards and conformity assessment procedures are higher and where trade between members is larger.

- 2- **Build trust:** Trust between member countries is an important determinant of the success of the harmonization of standards and of conformity assessment procedures. To build this trust, it is important to have a balanced approach to the reforms so that all members perceive that there are gains to be gained from the process by all members.

- 3- **Need for a quality strategy** : To keep the reforms focused, it is important that regional regulators show that that their proposed regulations will meet the required quality before they adopt them.  Regulators could use as a reference the OECD Checklist for Regulatory Quality (Jacobs 2004).

Jacobs (2004) identifies five characteristics that are needed for a country to have a quality regulatory system. These characteristics can also be extended to a quality regional regulatory system:

1. Security: predictable enforcement of the rule of law and respect for market solutions.

2. Transparency: clear and simple rules, openness through the entire policy process, and less corruption.

3. Legitimacy: must protect consumer safety, health, and the environment.

> 27 A longer version of this section, which includes suggestions for a more effective and rewarding participation of developing countries in the international standards arena, is available on request.

30
Based on the estimated parameters for the asset classes, the modeler runs 10,000 simulations for each of the investment strategies and computes the average expected pension (i.e. replacement rate, pension wealth, or annuity), the PDF, and the utility function.

## **_Fifth step. Approximating the optimal portfolio_**

Comparing the expected pensions with the pension objectives of the pension system (provided exogenously), for example, 70 percent replacement rate.

Modifying the investment strategies in order to achieve the expected pension outcome. Based on the simulations from the previous step and parameters, the investment strategies are modified in order to achieve the expected replacement rate. For each portfolio, it is necessary to run 10,000 simulations and then to calculate the average (close to the expected outcome), the probability density function (PDF) at retirement age and the value of the utility function.

The utility function plays a very important role, as risk averse individuals might be comfortable with lower pension objectives (i.e. replacement rates) compared with individuals who seek to maximize their future pensions. While the expected value of the utility function plays an important role in the selection of the optimal expected strategy, other factors might also be relevant, as explained in the next section.

# **IV. A concrete example of the Approximation Methodology**

Using data for Chile, we calibrate a benchmark model for the pension system, but the results may be applicable to other economies as well.

## **_First step: Calculating the earnings profile_**

We calculate the earnings profiles of individuals based on equations (1) and (2), represented in Figure 4. In the absence of long dataset that that may capture the different stages of the economic cycle, we followed a strategy similar to the one presented by Huneeus and Repetto (2004), which builds a synthetic panel based on cohorts.<sup>20</sup> In the period 1990-2009, we follow the income process of individuals with similar gender, cohort and level of education.

We estimate of the autocorrelation coefficient and the variances of the permanent and transitory shocks of the income process using a panel regression with fixed effects for a male with 16.4 years of education (OECD 2014), as shown in Table 2. Parameters �� and �� are higher than the ones estimated by Gomes, Kotlikoff and Viceira (2008) for the case of the United States, which are 10.95% and 13,89% respectively. The higher value of these parameters is expected in emerging economies, subject to more frequent shocks and a labor market and a relatively high proportion of the labor force moving in and out from the informal labor market.

> 20 We estimate the synthetic panel based on the data from the Quarterly Survey for the Great Santiago, published by the University of Chile. We used the survey of the 2<sup>nd</sup> quarter between 1990 and 2009.

20
within the basin. Thus, our assumption about the catchment area being generally contained within a dam’s river basin appears reasonable.

# **<<B>>** _Cropland Productivity_

Time-varying standard agricultural measures of cropland productivity, such as crop yields, do not exist for South Africa at a spatial resolution as fine as our hydrological regions.<sup>9</sup> We thus follow Blanc and Strobl (2013) and use two satellite data sources. The first is the Global Land Cover 2000 data set (GEM 2011), hereinafter called GLC 2000, which classifies land cover across the globe into 22 distinct land cover categories at a 1 km resolution based on images acquired by the SPOT 4 satellite during 2000. We use the land cover category “Cropland” to spatially capture agricultural crop areas within our basins, although we also experiment with including the categories “Mosaic of Cropland/Shrub or Herbaceous Cover” and “Mosaic of Cropland/Tree Cover/Other Natural Vegetation”. The “pure” cropland cells constitute approximately 14 percent (56,000 km<sup>2</sup> ) of South Africa. According to the GLC 2000, 15 percent of the six-digit code basins did not contain any “pure” cropland cells. Of the 223 basins that did, 77 were located in the former homeland river basins. We depict the identified cropland areas within South Africa in figure 4. The largest areas of “pure” cropland are located in the northern part of South Africa, mainly in nonformer homeland territories.<sup>10</sup> Most dams depicted in the same graph are located near cropland, except in the central part of the country.

21
# Macroeconomic Management and Intergovernmental Relations in China

# Jun Ma

# I. Introduction

China has a population of 1.2 billion and covers an area of 9.6 million square Idlometers. Administratively, the country is divided into 30 provincial level localities, including 22 provinces, 3 municipalities directly under the central government, and 5 autonomous regions (unless otherwise stated, we herafter refer to these provincial level localities as provinces).' The average population size of the provinces is 40 million, larger than most states and provinces in other large countries. Under the provinces, there are two administrative levels. They include: 2,183 county, autonomous counties, and cities at the county level; and townships and towns and city districts.<sup>2</sup> China's vast population and territory, as well as its geographical and economic diversity suggest the great importance of policy coordination between the central and sub national governments.

Over the past 15 years, China has moved gradually from a highly centalized planning system to a very decentralized one in almost all aspects of its economic management: taxation, government spending, credit allocation, production and investment planning, .naterial allocation, wage and price control, internatonal trade management, etc. This decenation process has significantly changed the role of each level of government in economic management, and greatly complicated the relations between different levels of government. This paper analyzes three aspects of the cental-local relations and their impact on macroeconomic management and market development, and draws implications for the direction of China's future reform. The three key questions discussed in this paper are: _(1) On fiscal management:_ how do the central-local fiscal relations affect the central government's ability to achieve the goals of stabilization and equalization through fiscal policies?

(2) _On monetary management:_ how do the central-local monetary relations affect the central bank's ability to control money supply?

(3) _On regudtory framework:_ how does the division of regulatory power between the central and local governments affect the functioning of the market system?

It is argued in this paper that as for fiscal policy, the central-local fiscal contract system adopted in the early 1980s has left the cental government with very limited policy instruments.

# 'Taiwan Province of China is not included.

China Statistical Yearbook 1992. There used to be 338 prefectural level governments between provincial govenment and counties. They were abolisied in the recent organizatonal reform.

I
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 17. Indicative infrastructure spending needs in Burkina Faso, 2006–15**

|**$ million per year**||||
|---|---|---|---|
|Sector|Capital expenditure|Operation and maintenance|Total needs|
|ICT|87|32|119|
|Power (nontrade)|71|71|142|
|Transport (basic)|81|64|144|
|WSS|157|50|208|
|Total|396|218|613|

_Source:_ Mayer and others 2009; Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others 2009. Derived from models that are available online at www.infrastructureafrica.org/aicd/tools/models. _Note:_ ICT = information and communication technology; WSS = water supply and sanitation.

Burkina Faso’s infrastructure spending needs are low in absolute terms—and even more so relative to GDP (figure 25). At $613 million, in absolute terms, the country’s needs are among the lowest in West Africa. Relative to the size of Burkina Faso’s economy, that spending would amount to 11 percent of GDP. Investment alone would absorb around 7 percent of GDP, which is less than the 14 percent of the GDP China invested in infrastructure during the mid-2000s.

**Figure 25. Burkina Faso’s infrastructure spending needs are greater than those of comparable countries**

Estimated infrastructure spending needed to meet targets, as percentage of GDP <!-- Start of picture text --> SSA<br>LIC-Fragile<br>LIC-NoFragile<br>ECOWAS<br>Resource-Rich<br>MIC<br>Senegal<br>Niger<br>Mali<br>Ghana<br>Benin<br>Cote d'Ivoire<br>Nigeria<br>Burkina Faso<br>Cape Verde<br>0 10 20 30 40<br>Percentage of GDP<br>Capital Investment O&M<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009.

_Note:_ LIC = low-income country; MIC = middle-income country; ECOWAS = Economic Community of West African States; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance.

Burkina Faso already spends a sizable amount ($406 million per year) to meet its infrastructure needs (table 18). About 70 percent of the total is allocated to capital expenditure and 30 percent to operating expenditures. Operating expenditure is entirely covered from budgetary resources and payments by infrastructure users. The largest source of funding for infrastructure investment is overseas development, which accounts for 40 percent of the total amount. The private sector accounts for a further 30 percent of current capital expenditure. The contribution of the public sector in financing capital investment is 17 percent, whereas the contribution of non-OECD countries is only about 7 percent. Water, sanitation, and power capture almost 90 percent of the resources allocated to operating expenditures. Private participation 36
main reasons. First, international standards are usually less restrictive than regional ones, and the cost of their adoption is therefore smaller. Second, their adoption expands the potential destinations that exporters can serve.

The rest of the paper is structured as follows. The next section surveys the literature to highlight our contribution. Section 3 describes the TBT provisions included in North-South EIAs. Section 4 presents our econometric specification and data. The results are discussed in section 5. Section 6 concludes.

# **2. Literature review**

In an early contribution, Baldwin (2000) examined different routes toward standard liberalization and argued that mutual recognition among developed countries could lead to a two-tier international trade system, with developing countries in the second tier. Since then, the literature, mostly empirical, has developed along two strands.

A first strand examines standards provisions in several EIAs and investigates whether they go beyond the World Trade Organization (WTO) Agreement on TBT. These papers do not quantify the trade impact of this regional liberalization. Covering 28 EIAs where the EU or the US is a partner, Horn et al. (2009) show that all but two US agreements include TBT provisions. Furthermore, for five EU and 11 US agreements, these provisions are legally enforceable, meaning that the agreement specifies clear legal obligations, which are more likely to be implemented.

Piermartini and Budetta (2009) survey 58 EIAs with TBT provisions. They carefully analyze the legal text of these EIAS and scrutinize whether the TBT provisions refer to the WTO TBT agreement and whether regional liberalization of TBTs through harmonization or mutual recognition is pursued. They also examine transparency requirements, institutional and 6
beverages over the entire range of the cumulative distribution of per capita expenditure. Figure 4.1 confirms this view. It plots the difference between the concentration of the consumption of cereals and that of beverages. The **DCC** curve lies entirely above the horizontal axis, thus it would be socially desirable to implement a commodity tax reform that would shift the tax burden away from cereals and towards beverages while keeping the public budget balanced. In addition, figure 4.1 reveals that at least 85 percent of the population would benefit from such a move.

It is useful to note the concept of TIP dominance in the context of truncated analysis. Given a poverty line z, if a TIP curve a lies entirely over another TIP curve b, we say that a TIP-dominates b. In other words, there is more poverty in situation a than in situation b, regardless of the dimension we choose to focus on: _incidence, intensity_ or _inequality_ among the poor, and for all poverty lines less than or equal to z.

Figure 4.2. A TIP Comparison of Poverty in Guinea by Area of Residence (Data Source: 1994 Integrated Household Survey) <!-- Start of picture text --> .20<br>Rural<br>-<br>.16<br>.1 2-<br>.08-<br>Other U rban<br>.04-<br>Conakry<br>.00- , I , I I I<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br><!-- End of picture text --> Figure 4.2 illustrates the point with three TIP curves for three population subgroups in Guinea based on the area of residence of the household in 1994: (1) Conakry, the capital city; (2) other urban centers and (3) the rural area. The dominance relation among these curves reveals that, regardless of the dimension considered, poverty in Guinea (as measured by the FGT class of indices) is essentially a rural phenomenon. About 53 percent of the rural population lived in poverty in 1994. The next poorest area consists of the urban centers outside of the capital city, where poverty incidence is estimated at 24 percent. Income poverty is lowest in the capital city of Conakry where incidence stood at 7 percent.

37