rafmacalaba/datause-encoder-data huggingface dataset:
**We find that a weighted predictive mean matching model is the best-performing model.** The weighted PMM approximates the underlying model well and allows us to weight regressions by survey weights. This result is in line with the previous literature, showing that PMM works well in the context of imputations (Kleinke, 2018). Given that we plan to use survey weights in future analyses performed on the generated dataset, we consider the weighted PMM most appropriate. We show that results across the multiple imputations are consistent. In addition, the distribution of the average imputed energy spending share closely resembles the observed distribution of energy spending shares when restricting the sample by households' gender composition and income categories. Results differ slightly by matching variables but are sufficiently similar. The same applies to results by non-matching variables. They also hold when using an alternative number of neighbors for the PMM.

**We use the generated dataset to demonstrate that the energy-poor and monetary-poor overlap.** Based on the synthetic EU-SILC, we show that all monetary poor, defined via the relative at-risk of poverty measure, are also energy poor. At the same time, not all the energy poor are at risk of poverty. Moreover, we show that energy expenditure shares are higher for lower-income quintiles. An important limitation of our analysis is that these insights highly depend on our chosen measure of energy poverty. Our main results rely on an income-based measure of energy poverty and a 10 percent threshold to identify the energy poor<sup>5</sup> . The overlap between energy and monetary poverty measures differs when using different thresholds or measuring energy poverty from a different angle.

**Our paper makes an important contribution to recent efforts exploring the potential of statistical matching techniques to improve the data environment in the EU.** Over the years, there has been an increasing effort in the EU to explore the potential of statistical matching to combine information from different datasets (Leulescu and Agafitei (2013); Serafino and Tonkin (2017); Moretti and Shlomo (2022)). Our paper contributes to this emerging literature by exploring the potential of statistical matching techniques in the case of energy expenditure shares and energy poverty in Bulgaria. To the best of our knowledge, we are the first ones to explore the potential of statistical matching techniques

> 5 Under this definition, energy poor households are those that spend more than 10 percent of their income on energy.

6
positive impact on the competitiveness of the firm. Another concern is that emission-intensive firms in the EU could relocate to regions with no or lesser carbon restrictions. The economic relocation would be accompanied by loss of jobs and market shares, as well as carbon leakage whereby emission reductions in the EU could be more than offset by increases elsewhere.

Two strands of the literature have addressed the above questions on competitiveness and carbon leakage. The first are _ex ante_ studies to simulate the potential carbon leakage in a range of energy-intensive manufacturing sectors. Based on assumptions on CO2 prices, demand elasticities and trade exposure, these studies project leakage rates ranging from very low to significant at 30 percent or more (Ponssard and Walker, 2008; Demailly and Quiron, 2008; Reinaud, 2008; etc.). For example, Demailly and Quirion (2008) examine the impact on the cement industry under a euro 20 per ton CO2 price. They found that the leakage rates range from 0.5 to 25 percent among EU-27 countries with a mean value of 6 percent.

The literature on _ex post_ empirical analysis is relatively thin. Abrell et al. (2011) assess the impact of the EU ETS on firm competitiveness based on data of 2,000 European firms during 2005-2008. They find no statistically significant impact of the EU ETS on firm value added, profit margin or employment. Anger and Oberndorfer (2008) examine the impact of the EU ETS on firm revenue and employment based on a sample of German firms over the period of 20052006. Their analysis suggests that the initial allocation of allowances did not affect revenue or employment and therefore the impact of carbon regulation on firm competitiveness is likely to be modest. Jaraite and Maria (2011) investigate the effect of Phase 1 (2005-2007) EU ETS on productivity growth of the power generating sector using macro-level data of 24 European countries during 1996-2007. They find that carbon pricing had a positive impact on technological change. Overall, the existing empirical literature seems to find little evidence to support the hypothesis that EU ETS would have a large adverse impact on competitiveness.

In this paper, we measure the effect of the EU ETS on firm unit material cost, employment and turnover based on a panel of 5,873 firms in the electric power, cement and steel industry from 10 European countries<sup>6</sup> during 2005-2009. The power sector is the most heavily affected by the carbon regulation. During the sample period, the sector was short by around 440 million

> 6 Austria, Belgium, Czech Republic, France, Germany, Great Britain, Italy, Netherlands, Poland and Spain.

3
**_<u>________________________________________________________</u>_**

# **Annex B.   Investment Climate Assessments (ICAs)- Logistics Module**<sup>**6**</sup>

_Pl. note:_ _1. Questions below are categorized as those for (i) (senior) management [A] (ii) “operations” staff [B]._ _2. Where applicable, questions in the logistics module have been cross-referenced to relevant questions in the Productivity and Investment Climate Survey (PICS) Core Survey Instrument._

[A] 1. What are the top 3 products sold by your firm? If one or more of these products are exported please indicate what percentage of sales is exported. _[Links with Question 10 and 11a and b – in the Core PICS Survey Questionnaire]_

|Product<br>name(s)<br>(SITC or<br>HS 4-digit)|Value<br>of sales<br>(annual)|Percen.<br>exported<br>if<br>applicable|Value<br>(typical<br>consignment<br>order)|Weight(or<br>Volume)<br>annual|Weight (or<br>Volume)<br>(typical<br>consignment<br>order)|Consignment<br>type<br>(breakbulk,<br>bulk,<br>container)|
|---|---|---|---|---|---|---|

[A] 2. What are the top 3 inputs that your firm purchases (including domestic *sources and imports)? _[Links with Q12 in the Core PICS]_

|Product<br>name(s)<br>(SITC or<br>HS 4-digit)|Value<br>(annual)|%<br>imported<br>if<br>applicable|Value<br>(typical<br>consignment<br>order)|Weight(or<br>Volume)<br>annual|Weight (or<br>Volume)<br>(typical<br>consignment<br>order)|Consignment<br>type<br>(breakbulk,<br>bulk,<br>container)|
|---|---|---|---|---|---|---|

> 6 Please contact Uma Subramanian (PSAIC) at 202 473 4497 or usubramanian@worldbank.org for any comments/ questions.

43 ____________________________________________________________________________________________________________
- 25 -

NTBs in 1986 while these measures had a negligible impact on fuels. In Japan, close to 100 per cent of food and chemical imports were affected by NTBs while no Type I or Type II restrictions were applied to iron and steel products. Tables 6 and 7 also show that major differences exist in the typcs ot measures that are being employed in these two countries. In the United States the trade coverage ratio for hard core (Type I) NTBs is relatively close to the combined ratio for all Type I and II measures (11.9 as opposed to 16.8 per cent). However, in Japan the combined coverage ratio (36.9 per cent) is more than double that for the hard core restrictions. 19/

# IV. Summary and Conclusions

While major concerns have been expressed about the spread of nontariff barriers in developed countries, the lack of empirical information on the dimensions of the increase has affected the related policy debates. Using inventories of nontariff barriers in developed countries compiled for 1966 and 1986 this study develops quantitative information on the major expansion of NTBs that occurred over this 20 year period. In 1966 nontariff barriers affected 25 per cent of developed countries imports, while in 1986 this share had increased to 48 per cent. These figures show that $30 billion

> 19/ The reader should note that the su called Type II measures that appear to predominate in Japan's protectionist profile can be enforced in a way that their trade restrictive effect is more severe than that of Type I restrictions. Due to their nature, inventory tabulations of NTBs cannot provide any information on the trade effects of nontariff barriers, but merely document whether restrictions are or are not applied. It should be noted, however, that a heavy reliance on Type II restrictions to control trade can result in a major loss of transparency on the intent and effects of nontariff measures. It is also recognized that Japan's use of "informal" restrictions such as those on steel imports from Korea are not recorded in the UNCTAD Data Base. This underreporting would cause the Japanese NTB coverage and affected trade ratios to be downward biased.
country and their locations to assign imported products to them.<sup>3</sup> For landlocked countries, we identify ports at neighboring countries through which most imports come (e.g., in some cases, countries are in conflict with some bordering countries and imports do not come through them). Then, we identify the border crossings that are closest to those ports. Finally, we geocode all locations to determine their latitude and longitude. For each product, we define a “trading pair” as an origin and a destination location.

Conditional on being narrowly defined, we consider products that are broadly available across locations and periods in our sample. Specifically, we restrict samples to products for which prices are observed in the products’ source and destination locations in more than six months.<sup>4</sup> Table 1 describes the main features of the resulting samples we consider for each country. The number of products ranges from 9 in Madagascar to 43 in Rwanda. The full list of products for each country, including their brands, presentations, and manufacturers, is included in the Appendix A.2. The number of destination locations (markets, cities, or districts) ranges from 6 in Georgia to 38 in Nigeria. Figure 1 shows the locations for each country in our sample, as well as the major roads connecting them, and indicates the location where a product is manufactured or imported, on which we elaborate in section 2.2.<sup>5</sup> Table 1 also shows the periods covered by each country sample. With the exception of the Nigeria sample, which starts in January 2001, all samples start after January 2010. The longest panel corresponds to Madagascar and covers the period from January 2010 to April 2021. The shortest panel is the one for Kenya, covering from October 2018 to January 2022.

Average origin prices, price gaps between origin and destination locations, and distances, are reported in Table 2.

Table 1: Main features and coverage of the data

|Country|No. of products|Origins|Destinations|Type of locations|Start period|End period|
|---|---|---|---|---|---|---|
|Kenya|20|10|32|Cities|Oct 2018|Jan 2022|
|Madagascar|9|3|7|Major cities|Jan 2010|Apr 2021|
|Nigeria|25|5|38|Cities|Jan 2001|Jul 2010|
|Rwanda|43|4|13|Districts|Jan 2013|Dec 2020|
|Tanzania|32|5|20|Cities|Jan 2012|Apr 2021|
|Georgia|21|4|6|Cities|Jan 2012|Dec 2020|

Notes: this table describes the main features of the datasets for each country in our sample. Source: authors’ elaboration based on CPI data from countries’ NSO.

> 3Our main sources are Logcluster, a wiki with detailed logistics information about each country, and World Port Source.

> 4As discussed in section 3.2, estimation of pass-through rates relies on variation of prices over time for each product-destination pair.

> 5The data underlying the maps are taken from the Global Roads Inventory Project (GRIP), available at `https://www.globio.info/download-grip-dataset` .

5
Policy Research Working Paper 8349

# **Abstract**

This paper proposes a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. It illustrates how these methods represent a substantial improvement over existing empirical approaches to measure inequality of opportunity. First, the new methods minimize the risk of arbitrary and ad hoc model selection. Second, they provide a standardized way to trade off upward and downward biases in inequality of opportunity estimations. Finally, regression trees can be graphically represented; their structure is immediate to read and easy to understand. This will make the measurement of inequality of opportunity more easily comprehensible to a large audience. These advantages are illustrated by an empirical application based on the 2011 wave of the European Union Statistics on Income and Living Conditions.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dmahler@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 5700

# **Abstract**

Pension funds have been expected to invest in a wide range of securities and provide liquidity to domestic capital markets since they are the most sophisticated investors, with plenty of resources to gather private information and manage portfolios professionally. However, by analyzing unique, monthly asset-level data from the pioneer case of Chile, this paper shows that pension funds tend to herd. This is consistent with pension funds copying each other in their investment strategies as a way to extract information, boost returns, and reduce risk. The authors compute measures of herding across asset classes (equities, government bonds, and private sector bonds) and at different pension fund industry levels. The results show that pension funds herd more in assets for which they have less market information and when risk increases. Moreover, herding is more prevalent across funds that narrowly compete with each other, that is, when comparing funds of the same type across pension fund administrators. There is much less herding within pension fund administrators and across pension fund administrators as a whole. This herding pattern is consistent with incentives for managers to be close to industry benchmarks, which might be driven by both market forces and regulation.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sschmukle@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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> <sup>- 29 -</sup>

Provisions (type B) count as available capital when certain conditions are met. Special Bonus Provisions belong to policyholders, but are counted as part of the capital covering the solvency requirement. They bear the same risk and remuneration as equity capital. Special Bonus Provisions (type A) resemble subordinated loans. However, they have so far not been used by any life insurance company or pension fund.

On the asset side, quantitative limits apply. Especially, there are restrictions on the share of assets which can be made up of so-called "risky assets", notably equities. Moreover, quantitative limits are applied in order to reduce concentration risks. For example, quantitative limits apply to the possible exposure to _one_ issuer of mortgage bonds or the proportion of total assets which might be invested in the securities of just _one_ company or a group of closely related companies.

While quantitative limits still apply, there has over a number of years been a gradual move toward regulation based more on the Prudent Person Principle (PPP). The PPP implies that greater emphasis is placed on the pension institution behaving prudently, controlling and monitoring risk, rather than on specific quantitative limits.

Especially, over a longer time span, the share of assets which can be invested in "risky assets", like equities, has been increased. In 2001 the proportion of assets covering technical provisions, which could be invested in risky assets, was raised from 50 to 70 percent. The reason for the increase was that some institutions had reached the former ceiling of 50 percent and still wanted more freedom to invest.

The increase, however, was not granted without conditions. It was accompanied by a political wish that the actual share of risky assets in the portfolio should be considered against so-called "objective criteria - in the sense that the share of risky assets should be based upon an assessment of the company's actual capital strength measured against its risks on investments and commitments".

These objective criteria were implemented in terms of the so-called system of traffic lights known from the banking sector (a political aim was to ensure that identical investment risks in different financial sub-sectors would imply the same solvency requirement). Since the introduction of the traffic light stress testing, the share of risky assets (equities) has steadily fallen, mainly because of adverse market developments.

The traffic lights are described below. The flexibility in the current investment regulation is so great that asset allocation strategies are probably more influenced by internal asset/liability considerations and capital rules (like the traffic light system) than by investment rules and limitations per se.

# **C.   Register of Assets Covering Technical Provisions**

All life insurance companies and pension funds have to keep a register of the assets covering technical provisions. At any time there must be assurance that the company in question holds enough assets to cover its technical provisions. A certain procedure and
**Table 2.3 Loans and deposits in the focus countries and comparators**

|Country|Longest term available<br>for loans (years)|Longest tenor for time<br>deposits (years)|Lending rate<br>(%) /a|Spread<br>(percentage<br>points) /a|
|---|---|---|---|---|
||2006|2006|end-2006|end-2006|
|Benin|10+|1+|—|—|
|Burkina Faso|10+|1+|—|—|
|Cameroon|—|—|15|11|
|Cape Verde|5|1+|12|9|
|Chad|—|—|15|11|
|Congo, Dem.<br>Rep.|3|1|67|52|
|Côte d’Ivoire|10+|1+|—|—|
|Ethiopia|—|2 +|7|3|
|Ghana|20|3|26|17|
|Kenya|10+|1+|14|9|
|Lesotho|20|1|12|8|
|Madagascar|See note b|—|30|7|
|Malawi|5|1|29|20|
|Mozambique|10+|10+|19|8|
|Namibia|20|—|11|5|
|Niger|10+|1+|—|—|
|Nigeria|5+|1|17|7|
|Rwanda|10 +|1|16|8|
|Senegal|10+|1+|—|—|
|South Africa|20|—|11|4|
|Sudan|—|—|11|—|
|Tanzania|5 +|2|15|9|
|Uganda|20|2|19|10|
|Zambia|20|7|23|13|
|Chile|25|2|8|3|
|Malaysia|—|5+|6|3|

Sources: Central banks; IMF _IFS_ . Data for 2006 with the exception of the Democratic Republic of Congo, Nigeria. and Rwanda (2005). — = Not available.

a. Lending rates can differ significantly according to borrower creditworthiness and financing objectives.

b. Madagascar’s seven commercial banks offer only very basic savings and credit vehicles to select clients.

The unwillingness to tie up savings in relatively low-yielding bank time deposits is demonstrated by the relatively high share of demand deposits in total bank deposits in the focus countries. That share exceeds 40 percent in 17 countries, contrasting with the ratios of 14 percent and 16 percent for Chile and Malaysia (figure 2.1). Given that total deposits in some countries (such as the Democratic Republic of the Congo) comprise a large amount of foreign currency deposits held abroad for some countries, the actual 12