rafmacalaba/datause-encoder-data huggingface dataset:
# **<mark>VII. T</mark> ables**

# **Table 1: Description of variables used**

|**Variable**|**Source**|**Year**|**Definition**|
|---|---|---|---|
|Procedures<br>Getting<br>Electricity|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Number of procedures required to complete a<br>connection to electricity of a newly built construction<br>with a 140 kVA load demand.|
|Time Getting<br>Electricity|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Time in days required to connect a newly built<br>construction to electricity starting from the first<br>procedure required for the connection and until<br>electricity starts flowing.|
|Cost in USD<br>Getting<br>Electricity|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Cost in USD paid to a utility, electrical contractors and<br>other agencies for completion of an external electricity<br>connection of a newly built construction with a 140<br>kVA load demand.|
|Cost as % of GNI<br>per capita Getting<br>Electricity|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Cost as % of GNI per capita paid to utility, electrical<br>contractors and other agencies for completion of an<br>external electricity connection of a newly built<br>construction with a 140 kVA load demand. 2008 GNI<br>in local currency units from the World Development<br>Indicators is used as a denominator.|
|Country rank on<br>the ease of getting<br>electricity|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Rank of countries based on the procedures, time and<br>cost of obtaining an electricity connection where 1<br>indicates the highest rank.|
|GNI per capita<br>2008|World Bank (World<br>Development Indicators 2009)|2011|The gross national income, converted to U.S. dollars<br>using the World Bank Atlas method, divided by the<br>midyear population.|
|Private/public<br>utility|Author's classification: review<br>of utility’s web-sites and other<br>sources on the structure of the<br>electricity sector|2011|A dummy variable that identifies the ownership of a<br>utility providing the electricity connection. The<br>ownership is classified as public if more than 50% of a<br>utility is government owned.|
|Procedures<br>Starting a<br>Business|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Number of procedures that entrepreneur needs to carry<br>out to begin legally operating a firm involved in<br>industrial or commercial activity.|
|Time Starting a<br>Business|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Number of days it takes an entrepreneur to carry out all<br>the procedures necessary to begin legally operating a<br>firm involved in industrial or commercial activity.|
|Cost as % of GNI<br>per capita Starting<br>a Business|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Cost of starting a business includes all official fees and<br>fees for legal or professional services which have to be<br>paid to start up and formally operate an industrial or<br>commercial business..|

24
debate. Some authors argue that pension funds foster the deepening of domestic equity and debt markets through their demand for investment instruments and their effect on corporate governance, and that they add to the liquidity of these markets through their trading activity (Davis, 1995; Vittas, 1995, 1999; Catalán, 2004; Catalán et al., 2000; Lefort and Walker, 2000, 2000a, 2002b; Corbo and Schmidt-Hebbel, 2003; and Andrade et al., 2007). Others maintain that pension funds do not contribute as expected to the development of capital markets, and are not investing pensioners’ savings optimally (Arrau and Chumacero, 1998; Zurita, 1999; IMF and World Bank, 2004; Yermo, 2005; Olivares, 2005; Berstein and Chumacero, 2006; and The Economist, 2008).

This paper aims to shed light on the debate of how pension funds affect capital market development, especially that of secondary markets, by providing a systematic analysis of the pension fund investment behavior and the factors that constrain it. This is done by: (i) studying in detail, at the micro level, how pension funds invest; and (ii) discussing how their strategies vary with factors that can significantly restrict the funds’ ability to allocate assets and to contribute to local capital market development. In particular, the factors analyzed in this paper are: regulations, managers’ incentives, and liquidity.

We analyze the investment behavior of pension funds using a unique and rich dataset that contains the detailed portfolios of the universe of pension funds in Chile at a monthly frequency for ten years (1996 to 2005). This dataset is matched with a separate dataset containing the returns of each instrument included in these portfolios. The combined and cleaned dataset contains 7,501,210 observations, with information on the holdings and returns of 104,789 different securities, for up to 57 pension funds. All the information is analyzed by taking into account the regulatory framework in which funds operate and its changes over time. These regulations include macro and micro restrictions such as the list of investable assets.

We use these data to address a series of questions regarding pension fund portfolio allocations and trading strategies. The questions related to portfolio allocations that guide our research are: Where do pension funds invest (both in terms of asset classes, type of assets, country origin, and maturity)? To what extent do pension funds diversify their holdings? How do pension fund portfolios vary with different degrees of regulatory 4
**Figure 1: Private credit to GDP in Central America (average 1995-2001)**

<!-- Start of picture text --> 45%<br>40%<br>40%<br>34%<br>35%<br>30% 28%<br>25%<br>20%<br>17% 17%<br>15%<br>10%<br>5%<br>0%<br>El Salvador Costa Rica Guatemala Honduras Nicaragua<br><!-- End of picture text --> Source: Central American Executive Secretariat of the Monetary Council and Central Bank of El Salvador.

30
11 equal to the child care credits that are granted.) After that, the benefit increases again linearly up to the maximum benefit (which is twice the minimum) up to the upper limit of 6 times the minimum (or 3 times the maximum) benefit.

**Figure 6  First Pillar Benefits as a Function of Qualifying Average Income**

<!-- Start of picture text --> Pension<br>Benefits<br>2 x Min. Benefits<br>1.5 x Min. Benefits<br>Minimum Benefit<br>Minimum Benefit 3 x Min. Benefits 6 x Min. Benefits<br>Average Qualifying Income<br><!-- End of picture text --> Source: Bundesamt für Sozialversicherungen As most people earn an average income clearly exceeding the three-times-minimumbenefit threshold, the effective linkage between pre–retirement earnings and the benefit level has become considerably weaker in the last two decades. A large majority of (potential) beneficiaries with a full contribution period are entitled to maximum benefits, so that earnings history only matters for people with low average wages and/or contribution gaps. An average married couple, for example, receives more than 92 percent of the maximum benefits.

Notwithstanding the changes in the structure of the first pillar, the payroll tax rate has remained unchanged at 8.4 percent since 1975, and the ratio between average pension benefits and the average labor income has remained almost constant for more than 30 years. Figure 7 depicts the evolution of the first pillar contributions and the relative benefits since the beginning of the AHV insurance system. The benefits are deflated by the nominal wage growth to depict their relationship to the wage level. Figure 7 as well as Table 3 shows that the implied replacement rate as well as the average replacement rate has stayed approximately constant in the last three decades.
Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1278 Regulation and Commitment in the<br>Development of Telecommunication<br>in Chile|Ahmed Galal<br>s|March 1994|B. Moore<br>38526|
|WPS1279 Optimal Hedging Strategy Revisited:<br>Acknowledging the Existence of<br>Nonstationary Economic Time Series|Ying Oian<br>Ronald Duncan|March 1994|S. Lipscomb<br>33718|
|WPS1280 The Economic Impact of Export<br>Controls: An Application to Mongolian<br>Cashmere and Romanian Wood Prod|Wendy E. Takacs<br> <br>ucts|March 1994|M. Pateina<br>37947|
|WPS1281 Human and Physical Infrastructure:<br>Public Investment and Pricing Policie<br>in Developing Countries|Emmanuel Jirnenez<br>s|April 1994|L. Longo<br>37786|
|WPS1282Copper and the Negative Price of<br>Storage|Donald Frederick Larson|April 1994|A. Kim<br>33715|
|WPS1283 Interest Rates in Open Economies:<br>Real Interest Rate Parity, Exchange<br>Rates, and Country Risk in Industrial<br>and Developing Countries|Dipak Das Gupta<br>Bepoy Das Gupta|April 1994|B. Kim<br>82467|
|WPS1284 The Soviet Economic Decline:<br>Historical and Republican Data|William Easterly<br>Stanley Fischer|April 1994|R. Martin<br>31320|
|WPS1285 Capital Fundamentalism, Economic<br>Development. and Economic Growth|Robert G. King<br>Ross Levine|April 1994|P. Sintim-Aboagye<br>38526|
|WPS1286 Economic Transformation and the<br>Fiscal Crisis: A Critical Look at the<br>Central European Experience of the 1|Luca Barbone<br>Domenico jr. Marchetti<br>990s|April 1994|S. Fiarbi<br>37143|
|WPS1287 Unstable Inflation and Seignorage<br>Revenues inLatinAmerica: How Man<br>Times Can the Government Fool Peo|Jacques Monsset<br>y<br>ple?|April 1994|D. Jenkins<br>37890|
|WPS1288 The Public Finance of Infrastructure:<br>Issues and Options|Vinaya Swaroop|April 1994|C. Jones<br>37699|
|WPS1289 A Fiscal Needs Approach to Equali-<br>zation Transfers in a Decentralized F|Anwar Shah<br>ederation|April 1994|C. Jones<br>37754|
|WPS1290 Oil Price Instability. Hedging, and an<br>Oil Stabilization Fund: The Case of<br>Venezuela|Stijn Claessens<br>Panos Varangis|April 1994|D. Gustatson<br>33714|
|WPS1291 A Survey of Viet Nam's Legal<br>Framework in Transition|NatalieG. Lichtenstein|April 1994|M. Rangarajan<br>81710|
13 The breakdown of the total poverty<sup>rate between different</sup> social groups Is presented<sup>In Table 6.</sup> Following Polish classification, we have used four social groups:<sup>farmers', mixed,</sup> workers', and pensioners' households.<sup>Mixed households are those</sup> where at least one member<sup>works full-time In</sup> agriculture (generally, cultivating his own land) while<sup>another Is employed In</sup> Industry or services. Workers'<sup>households include both blue- and</sup> white-collar workers. Definitions of other<sup>types of households</sup> are self-evident. 11 Consider first Poland. Poverty<sup>rate among farmers Increased</sup> from about 15 percent to some 20<sup>percent. The peak was achieved</sup> in 1983, following a 10 percentage<sup>point decllne In terms of trade</sup> between agriculture and industry.<sup>Poverty rate among workers</sup> Increased almost fourfold. At the<sup>outset of the crisis, workers</sup> had the lowest poverty rate among<sup>the four groups: only 6 to 7</sup> percent of people belonging to workers'<sup>households were classified</sup> as poor. In 1987, one out of<sup>four persons living in workers'</sup> households was poor. Increase<sup>In the poverty rate mirrors a 30</sup> percent decline In workers' real<sup>wages and per capita real Income</sup> (see Section 2). Already by 1983<sup>average per capita Income of</sup> workers' households was _lower_ than<sup>that of mixed and farmers'</sup> households. The tendency continued<sup>so that by 1987 farmers' and</sup> mixed households had average per<sup>capita incomes respectively</sup> 9 and 3 percent higher than workers (see<sup>Table 7).</sup> _Table 7. Relative Per Capita Income_<sup>_of Different Social Groups_</sup> _(per capita workers' Income In each_<sup>_country=100)_a/</sup>

||1978|1983|1987|
|---|---|---|---|
|Poland||||
|Mixed|81.8|100.4|102.6<br>|
|Farmers|90.8|107.0|109.2|
|Pensioners|76.6|78.3|93.4|
|Yugoslavia||||
|Mixed|70.1|86.8|91.1|
|Farmers|50.1|82.2|81.6|
|Hungary||||
|Pensioners|84.4|93.3|96.4|

a/ Workers households In Poland; non-agricultural<sup>In</sup> Yugoslavia; active in Hungary. The<sup>latter Include both workers and</sup> farmers.
10 administered prices from 50% to 10%.'° The extent of upward adjustment was often bigger than expected and resulted in almost 80% inflation in January 90. 1" .

Following sharp upward movements of individual nominal prices, price relations have undergone significant shifts. It is obvious that inflation has not been even across all sectors. Some sectors, especially those in need of establishing higher relative prices of their products, have led the inflation process with others lagging behind. Even prior to looking at the distributions of relative price changes it should be expected that in many periods individual inflations of CPI components were dominated by one or two disproportionately big price increases (see footnote 10). Those could result from administrative price increases or occur in sectors struck by unusually large supply shocks. Whatever the reason, the outcome should be high positive skewness of the distribution as few outlier price hikes are matched with a large number of relatively small price increases."<sup>2</sup> According to the theory, it is these asymmetries that can fuel the inflation process contemporaneously as well as wiith a time lag.

# 3.2: STATISTICAL EVIDENCE

The statistical data I use have been collected from vrarious issues of Central Statistical Office (GUS) Monthly Bulletins on Price Changes."<sup>3</sup> They are individual inflation rates for more than 60 groups of goods and services. The level of disagregation varies within the sample for different years reflecting the availability of data. The number of categories is: 61 (for 1989 and 1990), 62 (for 1991 and 1992), 64 (1993, 1994 and 1995) and 63 (for 1996 and 1997). They cover the entire basket of goods and services constituting the basis of the Polish CPI index, so that the weights of all categories add up to one for each year **14** to Wellisz (1997) p. 157

> **_"_** In January '90 the price of bread rose by **147%/o,** electrical energy by 370% and furnace fuel, central heating and hot water by almost 400%. **12** Unlike in the analysis of American inflation, the mean of the distribution in the case of Poland cannot be assumed to be close to zero and the distribution itself cannot be thought of as being composed of price increases and commensurate price declines. Rather, with the mean located at some positive 7n, the distribution is composed of big price increases matched with small price increases on the other side of the distribution.

> 3 Source: GUS, Miesieczna Informacja o Zmianach Cen,, various issues

> 4 The weights used by GUS to calculate inflation were updated every year and this paper takes that into account.
**Table 1.2 Cruise Lines Serving the OECS Ports**

||**St. John’s**|**St. George’s**|**Roseau**|**Basseterre**|**Long Point**|**Castries**|**Kingstown**|
|---|---|---|---|---|---|---|---|
|Aida Cruises|X|X|X|||X||
|Azamara Club Cruises|X||X||X|||
|Carnival Cruise Line|X||X|X||X|X|
|Costa Cruises|X|X||X||X||
|Celebrity Cruise Lines|X|X|X|X||X||
|Compagnie de Ponant||X||X||X||
|Club Med Marine||X||||X||
|Crystal Cruises|X|||||||
|Cunard Line||||X||X||
|Disney Cruise Line|X|||||X||
|Fred Olsen Cruise Line|X|X||X||X||
|Holland America Line|X|X||X|X|X|X|
|Island Cruises||||||X||
|Mediterranean Shipping||||||X||
|MSC Cruises||X||||X||
|Norwegian Cruise Lines|X|||X||X||
|Oceania Cruises Inc.|X||X|||X|X|
|Ocean Village||||||X||
|P&O Cruises|X|X|X|X||X|X|
|Passat Ship Management||||||||
|Princess Cruises|X|X||X||X|X|
|Regent Seven Seas Cruises|X|X||X||||
|Royal Caribbean Cruise Line|X||X|X||X||
|Saga Cruises|X|X|X|X||X||
|Seabourn Cruise Line|X||X|X|X|X|X|
|Silversea Cruises|X|X|X|X||X|X|
|Star Clippers Cruises|X|X||X|X||X|
|SeaDream Cruise||X|||X||X|
|Thomson Cruises|X|X|X|X||X||
|TUI Cruises|X|X|X|X||X||
|Windstar Cruises|X|X||X|X||X|

_Source:_ Authors’ compilation of data provided by port authorities and cruise agents. _Note:_ OECS = Organization of Eastern Caribbean States.

**With the exception of St. George’s (Grenada), which seems to be exemplary among OECS ports for protecting the cargo ship berthing window, ports give cruise ships precedence over cargo** . This implies that, in practice, the efficiency of cargo handling in ports is largely defined by the behavior of the cruise ship passenger market. Although most OECS ports have dedicated cruise terminals, they are forced to use cargo terminals to receive cruises due to high demand; container ships must wait to berth until the cruise ship is gone. For example, in St. Lucia, Castries has a dedicated terminal for cruise ships (Pointe Seraphine) with two berths, but it is very common for cruise vessels to be sent to the cargo terminal during the peak tourist season. Cruise operations are between 6 a.m. and 6 p.m. (sometimes until 8 p.m.) and cargo operations can start only after the cruise ship leaves, delaying the cargo handling to overnight hours (when workers will be paid overtime) or until the next day.

**In terms of cruise vessel calls, the ports of St. John’s (Antigua), Basseterre (St. Kitts), and Castries (St. Lucia) together receive about 60 percent of OECS passenger traffic.** Antigua has the highest

11
- 3 -

rivals’ behavior and the size and nature of projects being auctioned. On the other hand, auctioneers also desire to limit competition in the sense that they might disqualify some of the initial applicants prior to taking their price bids into account. Project owners often want to exclude from the selection process those who would likely fail to fulfill the agreed contract.

Without doubt, the number of bidders observed at the final price evaluation stage results from a sequence of choices made by prospective firms and an auctioneer. For this reason, partial observability must emerge as a potential empirical problem. One can observe bids only if firms participate in the tender and are qualified through prerequisite conditions. Auction theory has discussed the endogeneity issue associated with bidders’ entry (e.g., McAfee and McMillan, 1987; Levin and Smith, 1994; Menezes and Monteiro, 2000). Basically, it shows that high entry costs would reduce the optimal number of bidders, and bidders would enter until their expected profits are driven to the entry cost. Unfortunately, however, the selection is rarely singleton in practice. Auctioneers can be selective as well. Moreover, even qualified bidders may desire to withdraw their proposal in the middle of the process.

The current paper collects data on potential bidders from public procurement auctions for road development projects in developing countries and aims at tracking their sequential decisions toward the final decision about who would be the awardee. In general, it is empirically uneasy to identify potential bidders, simply because they are not observable.<sup>2</sup> The current paper pays attention to those who purchased prequalification or bidding documents as a good set of serious prospective bidders. They may or may not participate in the tendering, as will be described later. The paper, using the sequential-response model (Amemiya, 1975; Maddala, 1983), examines how auctioneers and individual firms decide whether or not to proceed to the next step toward the final price bid evaluation stage. Then,

> 2 In the existing endogenous entry auction literature, this difficulty tends to be avoided by relating the realized (not potential) number of bidders to explanatory variables in a Poisson or negative binomial regression model (e.g., Li and Perrigne, 2003; Li and Zheng, 2006; Ohashi, 2008; Estache and Iimi, 2008b). Otherwise, one can assume that those who participated in a series of auctions at least once in the past would constitute a set of potential bidders.
| POLICY RFSEARCH WORKING PAPFR 2076

# **Summary** findings

Using household data from five successive national surveys, Wodon analyzes the microdeterminants of (and changes in) consumption, poverty, growth, and inequality in Bangladesh from 1983 to 1996.

Education, demographics, land ownership, occupation, and geographic location all affect consumption and poverty. The gains in per capita consumption associated with many of these household characteristics tend to be stable over time.

Returns to demographics (variables in household size) have the greatest impact on growth, perhaps because of improving employment opportunities for women.

Education (in urban areas) and land (in rural areas) contribute most to measures of between-group inequality. Location takes second place, in both urban and rural areas.

Wodon introduces the concept of conditional between-group inequality. Existing group decompositions of the Gini index along one variable do not control for other characteristics correlated with that variable. Conditional between-group Ginis avoid this pitfall. He also shows how to use unconditional and conditional between-group Ginis for simulating policies.

This paper-a product of the Poverty Reduction and EconomicManagementSector Unit, SouthAsia Region-was written as part of background work for the Bangladesh poverty assessment. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Jillian Badami, room MC10-336, telephone 202-4580425, fax 202-522-2428, Internet address jbadami@worldbank.org. Policy Research Working Papers are also posted on the Web at http:i//www.worldbank.org/html/dec/Publications/Workpapers/home.html.The author may be contacted at qwodon@7worldbank.org. March 1999. (33 pages) _The Policy Research Workong Paper Series disseminates the findings of work in progress to encourage the exchange of ideas arolit develo pment issuies. An objective of the series is to get the findings out ,auickly, even if the presentations are less than fuilly polished. The papers carry the names of the autbors and sbould be cited accordinzgly. iDbe findings, interpretations, and contclusions expressed ill this paper are enttirely those of the authors. They do not necessarily represeni_ **_t_** _he view of the World Bank, its Executive Directors,_ **_o;-_** _the countries they represent._ Produced by the Policy Research Dissemination Center