rafmacalaba/data-use-mentions-extended huggingface dataset:
# TABLE 3 Variable list

|**Variable**|**Units**<br>**Source**<br>**Sample**|
|---|---|
|GDP in U.S. dollars|Millions of U.S. dollars, at<br>market exchange rates<br>IMF<br>World<br>Economic<br>Outlook<br>database<br>194<br>countries,<br>1980-2021|
|Real<br>GDP<br>in<br>loc<br>currency|al<br>Millions of local currency<br>Haver Analytics<br>93<br>countries,<br>1980Q2-2021Q4|
|GDP per capita|U.S.<br>dollars<br>at<br>market<br>exchange rates<br>IMF<br>World<br>Economic<br>Outlook<br>database; UN population statistics<br>182<br>countries,<br>1980-2021|
|Population, by age an<br>gender|d<br>Number<br>UN<br>population<br>statistics<br>and<br>projections<br>184<br>countries,<br>1950-2035|
|Labor force, by age an<br>gender|d<br>Number<br>ILO, Key Indicators of the Labour<br>Market (KILM) database; OECD<br>Labour Force Statistics<br>180<br>countries,<br>1960-2020|
|Investment growth|Percent<br>Haver Analytics<br>187<br>countries,<br>1961-2021|
|Secondary<br>educatio<br>completion rate|n<br>Percent of population that<br>completed<br>secondary<br> <br> <br> <br>Barro and
as % of revenue|%|88.4||131|113|140.6|
|Effective power tariffs (US cents/kWh)|**Mali**|Countries<br>predominantl<br>generati|with<br>y hydro<br>on|Countries w<br>predominantly t<br>generatio|ith<br>hermal<br>n<br>Othe|r developing<br>regions|
|Residential at 100kWh/month|21.0|10.7||15.7|||
|Commercial at 900kWh/month|18.6|12.9||19.0|5|.0 – 10.0|
|Industrial at 100 kVA|—|9.3||13.0|||

Source: Eberhard and others 2009.

Derived from AICD electricity database downloadable from http://www.infrastructureafrica.org/aicd/tools/data Other source include: Access data coming from Demographic and Health Surveys 1996 and 2001. Utility data from AICD electricity database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.** Data referring to outages is coming from the 2003 and 2007 Enterprise Surveys.

25
**Table 3: Occupational Pension Plans in Germany (1990-93)** (in percent of total).

|Type of Pension Plan|Assets|Employees|Companies|
|---|---|---|---|
|Book reserves|56.3|54.1|12.8|
|Pension funds|22.9|18.9|28.3|
|Life insurance|12.1|14.0|68.9|
|Support funds|8.7|13.0|1.6|
|Total|100.0|100.0|100.0|

Sources: Deutsche Bank Research 1996, Statistisches Bundesamt 1994 Note: Data for assets for 1993, for employee and company distribution for 1990 The share of book reserves in total assets of occupational schemes has been declining over the last decade. In 1981, book reserves accounted for 67 percent of all assets. Direct insurance has been growing the fastest with an increase from less than 5 percent in 1981 to 12 percent in 1993.11 But book reserves still are the most attractive form of pension scheme for sponsoring employers, as can be seen in the share of covered employees in table 3. By setting up book reserves, companies can reduce taxable profits, and the pension reserves can be reinvested in the company.Taxes become payable only when the reserves are released which increases profits; at the same time, pension payments are considered operating expenses which in turn offsets the increase in profits.

Contributions to support funds are tax-deductible up to certain limits depending on the type and average level of benefits promised. Companies who choose this option often take out reinsurance to cover the remaining unfunded liabilities. Support funds are not subject to investment restrictions; the most common forms of investment are loans to the sponsoring enterprise(s) and real estate. Due to the high degree of discretion for the sponsoring employer, support funds also have to be insured with the PSV
0.17 percent of total TRIAD imports.

# 3.3 Empirical Results

Table 1 provides estimates of the subsidy that the TRIAD would pay in the absence of tariff reductions, which, as we previously noted, is equal to the tariff revenue currently forgone due to preferences given to LDCs. The total value is $763 million, of which the largest share is the E.U.’s because of three factors. First, the E.U. imports larger quantities from LDCs than Japan and the U.S. Second, European preferences tend to be more generous under the EBA than under the LDC regimes of Japan and the U.S. (including AGOA in the case of the U.S.), as we can see from Table A3. Third, the MFN tariffs

> 34 We have an additional 130 countries in the data set. The tariff data come from different sources and we employ the most recent year for which data is available between 1999 and 2004. Sources are UNCTAD’s TRAINS, WTO noti fi cations and OECD (2003) CD-ROM, as well as the earlier 2001 version.

> 35 When elasticities were not estimated for particular countries in those studies we took the trade-weighted average across all countries for those products. In the case of export supply elasticities, where for some tariff lines they were not able to estimate export supplies at the six-digit level, we took the country speci fi c average across all tariff lines within the four digit level.

16
*|3.7472***|4.8958***|1.9215***|3.0930***|
||(0.0390)|(0.2471)|(0.1687)|(0.1423)|(0.1733)|(0.1701)|(0.1796)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.364|0.164|0.107|0.148|0.228|0.149|0.091|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

87
percent; High Income Countries 79 percent; Sub-Saharan African 74 percent; and Middle East and North Africa 70 percent (Ackah, et al, 2008).

# **Definition of Scenarios**

We developed three scenarios and analyzed over the period 2009-2020.The first scenario is the Business as Usual ( _BaU_ ) scenario. This is the baseline scenario which incorporates a number of assumptions. The first set of assumptions is on the world prices of three sources of energy (coal, oil, and natural gas) which are exogenous variables in the model, whose values were derived from the projections calculated outside the model. Table 1 shows the price indexes of these three sources of energy used under the _BaU_ . The price of coal has the highest increase in 2004-20 (161 percent), followed by the price of natural gas (85 percent), and by the price of oil (113.7). There was an oil price spike in 2008 and 2009, but it stabilizes thereafter.

**Table 1:** **<u>World price index of energy</u>**

|Year|Worldprice of coal|Worldprice of oil|Worldprice of naturalgas|
|---|---|---|---|
|2004|100.0|100.0|100.0|
|2005|101.0|104.9|105.4|
|2006|102.1|110.0|111.1|
|2007|103.1|115.4|117.0|
|2008|104.2|121.0|123.3|
|2009|105.2|126.9|130.0|
|2010|106.3|133.1|137.0|
|2011|107.4|139.6|144.4|
|2012|108.5|146.4|152.1|
|2013|109
THE DEMOCRATIC REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 4. Benchmarking road indicators**

||**Unit**|**LIC**|**DRC**|**Fragile**<br>**states**|
|---|---|---|---|---|
|Paved road density|km/1,000 km<sup>2</sup><br>of land|16|1|21|
|Unpaved road density|km/1,000 km<sup>2</sup><br>of land|68|14|75|
|Geographic information system<br>(GIS) rural accessibility|% of rural population within 2 km of all-<br>season road|21.7|29.3|—|
|Paved road traffic|Average annual<br>daily traffic|1,027|257|843|
|Unpaved road traffic|Average annual<br>daily traffic|55|20|55|
|Paved network condition|% in good or fair condition|75|70|69|
|Unpaved network condition|% in good or fair condition|58|42|55|
|Perceived transport quality|% firms identifying as major business<br>constraint|23|30|—|

_Source:_ Gwillliam and others 2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. — = data not available.

# **Challenges**

The DRC’s vast landmass (equivalent to the size of Western Europe), low population density, and extensive river network makes road development particularly challenging. A large share of the land area is covered by dense tropical forest and crisscrossed by rivers that complicate road construction and maintenance due to the numerous bridges needed. For all these reasons,
constraints on their growth, however. Entrepreneurs could lack the knowledge of how to write a business plan, be successful at obtaining financing, and run a successful business. Even with knowledge of how to obtain it, entrepreneurs may lack access to financing due to market imperfections. High taxes could limit the scope for internal financing of growth. The necessary managerial or employee human capital could be in short supply. And state bureaucrats could hinder growth by extracting bribes rather than protecting property rights and enforcing contracts. Evidence from a special survey will be employed to determine which of these factors are most important for growth.

Has the economy received any productivity benefit from the destruction, or has the worker dislocation been all pain and no gain? Many cases of direct subsidization and other forms of support for weak and failing enterprises are known to have occurred, while discriminatory taxes, bureaucratic interference, poor contract enforcement, and uncertain property rights protection have impeded those that are more successful (e.g., Frye and Shleifer, 1997; Aslund, Boone, and Johnson, 1996). Russia and Ukraine could thus be subject to “sclerosis” (Caballero and Hammour, 2000), in which less productive resources remain employed due to market imperfections and government policies, while the creation of more productive matches of resources and enterprises is impeded. Alternatively, less productive firms may have been pressured by competition and profit-seeking private owners to downsize, while more productive firms seized new profit-making opportunities and created jobs in the process. Such a scenario would positively contribute to aggregate productivity growth. Not only does the answer to this question affect productivity growth during the transition, but it is also important for future growth prospects. Productive firms should be able to respond more quickly to the recovery in demand, as well as be in a better position to compete in world markets, so future growth prospects will be brighter if more resources are concentrated among the most productive firms.

The productivity effects of firm turnover are of particular importance. Exit tends to be more
)**|
|---|---|---|---|---|---|---|---|
|5 years or<br>younger|12|47|41%|4|2%|18%|78|
|6-15years|16|73|43%|10|0%|9%|22|
|16-25years|22|145|45%|20|0%|8%|49|
|>26years|40|273|46%|35|0%|7%|103|

_Source: Interim Enterprise Survey, 2009/10; Own calculations_

**Evidence across size and age classes confirms that large firms drive employment growth.** The median growth rate of large firms declines across age classes slightly declines, but remains significantly higher than the growth rate of other size classes across the board. Only companies which are younger than 6 years and medium sized outperform large companies of the same age class. These findings are independent to the choice of the median or mean (see figure 4).

13
Policy Research Working Paper 10180

# **Abstract**

Despite the magnitude of return migration from overseas to South Asia, the labor market outcomes of return migrants to this region have been understudied. This paper aims at filling this gap by examining systematic differences between the labor market outcomes of return migrants and nonmigrants in Bangladesh, Nepal, and Pakistan using nationally-representative surveys that include information on past migration. Conditional regression analysis is used with a focus on four labor market outcomes: (i) labor force status (ii) sectoral choice (iii) employment type, and (iv) earnings. The paper finds that return migrants are somewhat less likely to be employed than nonmigrants, which is mainly driven by returnees who returned at an older age. As evidenced in other contexts, return migrants in Bangladesh and Pakistan are more likely to become entrepreneurs compared with nonmigrants. Self-employed returnees are also more likely to hire paid employees and to be engaged in non-farm activities, compared with nonmigrant entrepreneurs. Return migrants who become employees earn a small wage premium relative to nonmigrants, compared with contexts where temporary migrants are higher-skilled. The returnee wage premium, however, is larger in the construction sector where most temporary migrants were employed overseas.

This paper is a product of the Social Protection and Jobs Global Practice and the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at lbossavie@worldbank@worldbank.org and hwang21@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
surveyed. This index provides support for a range of beliefs and values on both topics that are meaningful for the World Bank and other development partners to be aware of when engaging with public officials in EAP countries. We find no statistically significant link between this index and per capita GDP.<sup>14</sup> 10 This correlation is evidenced by the KMO statistics (the Kaiser–Meyer–Olkin (or KMO) test is a statistical measure to determine how suited data are for factor analysis), which are above 0.6 as seen in Table 4 in the Annex. The correlation coefficient of typical questions such as “schools should try to promote gender equality” and “Children with disabilities deserve the same level of access to public school” is about 0.25.

11 Officials were asked to rate statements using a discrete scale: “Strongly agree”, “Agree”, “Disagree”, “Strongly disagree”, “Do not know”.

12 The sample appears inadequate according to the KMO test if we only analyze gender views, meaning that the items are not correlated enough to reflect an underlying trait. We therefore combined the views on gender with other measures of inclusion in this discussion. See Table 4 in the Annex for details of the analysis.

13 The probability that the averages are different is about 4 percent in a t-test.

14 As evidenced by the p-value of the F test, which is about 0.35.

12
(2022) and add several indicators identified from our review of the relevant literature.** The table reveals that energy poverty indicators in the EU mainly rely on three different types of data sources: EU-SILC (European Union Statistics on Income and Living Conditions), HBS (Household Budget Survey), and BSO (Building Stock Observatory) (Gouveia et al., 2022). We add several indicators from our literature review, which we outline in the next subsections.   Most indicators can be classified into three different measurement types (expenditure, consensual, and direct measurement), and some additional ones are more related to the building stock features and energy prices.

_Table 1: Different indicators measuring energy poverty_

|**Type of**<br>**Measure**|**Indicator**|**Definition**|**Source**|**Availability –**<br>**Bulgaria**|
|---|---|---|---|---|
||Ind<br>M/2 measure|icators from Gouveia et al.(202<br>Absolute (equivalized)<br>household energy<br>expenditure below half<br>the median|2)<br>HBS|Yes|
|Expenditure-<br>Based|2M measure|Share of household<br>energy expenditure<br>(compared to disposable<br>income/expenditure)<br>above twice the national<br>median|HBS|Yes, when<br>using HBS<br>income|

7
# I. **Introduction**

Property rights of state owned enterprises (SOEs) display large cross-sectional and time series variations. A case in point is Chinese SOEs in the 1 980s. As will be shown later, Chinese SOE managers faced firm-specific profit retention rates, had different production decision rights and discretion in employee wage determination, and financed investments from different sources---some relied more on govermment funds, some on bank loans, and the others on their own retained profits. What determines how the involved parties partition property rights? What are the behavior patterns of the government and SOEs in this process? These questions are the central concerns of this paper. A positive study of the political economy of property rights will help us better understand SOEs' behavior, including the objectives and constraints of the government; it may, therefore, enable us to offer better prescriptions for reform of SOEs. As yet, these questions have not been addressed systematically, and this research attempts to fill that void.

The theoretical approach used here highlights the asymmetry of information between the government and managers to examine how the principal (the government) and agents (managers) partition control rights and incentives. To curb the information advantage of SOEs, the government designed incentives and control rights based on the finns' characteristics---such as the risks they faced, their sizes, capital intensities, and past performance. For instance, for firms facing a "nosier" environment, the government often designed lower profit sharing and retained more centralized production decisions to mitigate risk; for firms with higher capital intensity, the govermment's goal of maintaining the value of equipment was likely to be in conflict with the employees' short-run bonus motives when profitability-based pay was imposed. Thus monitoring was more likely to be used than pay sensitivity in inducing internal labor incentives in capitalintensive firms.

The empirical implementation and tests were carried out using _A Survey of State Enferprises: 1980-1989,_ a panel data set consists of 769 firms over 1980-1989. Firms
VoxEU. April 9, 2020).</u> Espitia, Alvaro, Nadia Rocha, Michele Ruta (2020b). “Database on COVID-19 trade flows and policies”. Washington, DC: World Bank.

EUI-GTA-WB (2020). “Covid-19 Trade Policy Database: Food and Medical Products”.

FAO (2018). The future of food and agriculture. Alternative pathways to 2050. Food and Agriculture Organization. Rome, Italy.

FAO (2009). The State of Agricultural Commodity Markets. High Food Prices and the Food Crisis: Experience and Lessons Learnt. Food and Agriculture Organization. Rome, Italy.

Fontagné, L., H. Guimbard and G. Orefice (2019). “Product-Level Trade Elasticities.” CEPII working paper 2019-17.

Freund, C. and C. Ozden (2008). "Trade Policy and Loss Aversion". _American Economic Review_ , 98:4, 167591.

Giordani, P. N. Rocha, M. Ruta (2016). “Food prices and the multiplier effect of trade policy.” _Journal of International Economics_ , Volume 101, Pages 102-122.

Grossman, G.M. and E. Helpman (1994). "Protection for Sale." _American Economic Review_ , 84:4, 833-850.

Kee, H.L. (2020). “COVID-19 and Trade Disruptions Due to China.” Washington, DC: World Bank.

Kee, H.L. and A. Nicita (2020). “Trade Frauds, Trade Elasticities and Non-Tariff Measures.” Work in progress. Washington, DC: World Bank.

18
4,270|
|Pseudo R-squared|0.99|0.99|0.99|0.99|

Note: Dependent variable is the stock of immigrants in country _j_ originating from _i_ . Data on bilateral stock of migrants by macro-occupation are from the OECD DIOC dataset `https: //www.oecd.org/els/mig/dioc.htm` . Primary educated immigrants are those in ISCO occupation 92, 93 and 81. Robust standard errors in parentheses. ***, **, * significantly different from 0 at the 1%, 5%, and 10% levels respectively.

29
data on villages. The EPM uses multi-stage sampling, splitting observations into strata and clusters. Strata are identified by province (“Faritany”) and by urban vs. rural area; clusters are identified by administrative units (“Fokontany”) randomly drawn in each stratum. The sample in this paper includes 5,951 agricultural housholds. Although repeated, the EPM is not a panel as only a small number of households were sampled repeatedly in 1997 and 1999.

Commune data (collected in 2001 and covering 1’385 communities at the level of administrative units above Fokontany) is from the commune census of Cornell University’s ILO program. It includes, inter alia, data on the commune’s level of development, infrastructure and aggregate agricultural production.

Producer prices are taken from the Food and Agriculture Organization (FAO), for all years between 1991 and 2001. Prices are in current Malagasy francs (MGF). Climatic data was taken on the one hand from the Ministry of Agriculture, Livestock and Fisheries of Madagascar and on the other hand from a paper on climatic perturbations (Rambeloalijaona & Randrianarivelo, 2003).

The first column of Table 1<sup>8</sup> shows descriptive statistics computed allowing for the sample design (weights, strata and clusters<sup>9</sup> ). The first striking feature of the data is agricultural household’s extreme poverty.<sup>10</sup> With a yearly income of US $566 for a family of five (implying about 30 cents per day per person) the average Malagasy household lives well under the international dollar-a-day poverty line.

The second is the degree of farm specialization. On average, a farm’s first crop accounts for 80 percent of agricultural income, and about 70 percent of total income comes from crop sales. With 7.5 percent of full income<sup>11</sup> from livestock sales, the diversification of sources of income is somewhat limited.

At about 1.
Table 5: Growth and Conflict in the Cross-section

**Panel A: Country Level, Penn World Table GDP per Capita Growth**

|sample<br>num|ber of observations|mean|standard deviation|Min|Max|
|---|---|---|---|---|---|
|peaceful countries|4535|2.69|6.35|-42.36|115.43|
|civil war countries|4107|1.73|7.69|-64.61|90.03|
|civil war countries during peace|3163|2.03|7.17|-39.11|90.03|
|violent countries during violence|673|0.29|10.19|-64.61|52.07|

# **Panel B: Country Level, Worldbank GDP per Capita Growth**

|sample<br>num|ber of observations|mean|standard deviation|Min|Max|
|---|---|---|---|---|---|
|peaceful countries|4244|2.52|5.53|-45.83|142.07|
|civil war countries|3832|1.78|7.64|-65.03|182.36|
|civil war countries during peace|3224|2.14|7.20|-34.96|182.36|
|violent countries during violence|608|-0.14|9.44|-65.03|50.03|

**Panel C: Country Level, Night Light per Capita Growth**

|sample|number of observations|mean|standard deviation|Min|Max|
|---|---|---|---|---|---