rafmacalaba/datause-encoder-data huggingface dataset:
# **3.4 Developments in Lithuania**

Under the 2003 act that created the second pillar, the use of life annuities is mandated for retiring workers who have accumulated sufficient balances to receive an annuity equal to between 50 percent and 300 percent of the basic pension.<sup>34</sup> Lump sum payments are provided for workers with balances below the lower limit as well as for the excess balances of workers above the higher limit. The law does not specify which type of life annuity should be used other than requiring that payments should not be declining. Otherwise, all types of annuities are allowed, including fixed and escalating annuities as well as deferred annuities and annuities with guaranteed periods of payment.

The determination of whether retiring workers have sufficient balances to purchase an annuity is made by the pension fund management company on the basis of a methodology and data provided by the Insurance Commission. The calculation is based on interest rate and mortality data provided by the Commission for the standard fixed nominal single immediate life annuities. The use of joint life annuities, annuities with guaranteed periods of payment or escalating annuities, all of which have the effect of lowering initial payments, is not taken into account. However, insurance companies are free to price their products in accordance with their own parameters.

## **White Paper recommendations**

A Working Group with representatives from the government, the supervisory institutions and the private sector was created in 2007 to recommend changes to streamline the design of the payout phase. It published a White Paper in December 2008. This reaffirms the broad outline of the current approach but proposes the designation of four instead of three groups of retiring workers: the first group of workers, those with accumulated balances of less than 20 times the national basic pension, will receive a lump sum; the second group, those with balances exceeding 20 times the basic pension but not sufficient to purchase an actuarial annuity of at least half the basic pension, will receive temporary phased withdrawals equal to 35 percent of the basic pension until their account balances are exhausted; the third group comprises those workers with sufficient balances to purchase an actuarial annuity of between half and 3 times the basic pension; and the fourth group covers workers with higher balances, who will be free to withdraw their excess balances as lump sums.

The White Paper reaffirms the offer of a choice to retiring workers from the third and fourth groups between fixed nominal annuities, escalating annuities (rising at a 2 percent annual rate), and annuities with guaranteed periods of payment. To improve the marketing of annuities it recommends the implementation of an electronic quotation system (similar to Chile’s SCOMP) to be managed by the Lithuanian Social Insurance Agency (SODRA). The lack of protection against inflation risk is one of the main weaknesses of the Lithuanian proposal.

> 34 In April 2008 the basic pension amounted to LTL 360 per month, equivalent to EUR 104 or USD 139.

49
6 In this paper, we are interested in analyzing the individual and household-level correlates of infant mortality. For this purpose, we work with actual survival data of individuals rather than aggregate figures. This corresponds to the true cohort life table approach. Specifically, we use observations on children born alive 12-35 months before the interview. This analysis uses principally data from Kenya Demographic and Health Surveys (DHS) conducted in 2003 and 2008-09 (hereafter 2008). Given the precise dates of the DHS interviews, the infant survival data is based on a set of births that occurred April 2000-August 2002 and November 2005-February 2008. The sample consists of 2295 observations in the 2003 DHS and 2373 observations from the 2008 DHS. See the Annex for further details on sample restriction and data imputation procedures.

The specific choice of time window for eligible births (12-35 months before the DHS interview) was determined by a number of factors. The period must end 12 months before the survey date so that survival up through the age of one year can be determined for each observation. A time period extending more than 35 months would expand the size of the sample. However, some of the possible correlates for mortality—such as ITN ownership and water and sanitation access— are measured on the date of the survey. Consequently, the reliability of this information as a guide to the circumstances of the infant‘s first year of life declines as the window for observations is extended into the past. The 12-35 month window was chosen to balance the competing concerns of sample size and relevance of data observed on the date of the survey.

The analysis is restricted to infant mortality and does not examine the broader question of under5 mortality because under-5 mortality can only be determined for individuals once five years have passed since the child‘s birth. This means that, for example, an analysis of child survival based on individual data from the 2008 DHS could only use data on births that took place in 2003 or earlier. However, such births would have taken place in circumstances far removed in terms of time from the characteristics of the household (ITN ownership, water and sanitation access, etc.) at the time of the survey in 2008, and the survival of such children would be much less relevant to evaluating post-2003 changes in policies and programs.

Infant mortality, which refers to deaths in the period up to 12 months of age, is comprised of neonatal and postneonatal mortality. Neonatal mortality refers to deaths taking place from birth
resources is unlikely. It is difficult, for example, to imagine that the enactment of a more efficient bankruptcy law would influence the exploration and usage of natural resources. In contrast, other available indicators of the business environment are aggregate or macro level measures. A possible feedback effect from macro level changes in the business environment to the exploration and usage of natural resources is plausible. This problem is identified in Glaeser et al. (2004), among others.

# **2.2** **_Explanatory variables_**

# _Resource abundance_

The main explanatory variable is a measure of natural resource abundance in a country. We follow the literature in using the value of primary exports as a percentage of GDP ( _SXP_ ) as our main measure of resource abundance. Primary exports are defined as those in the SITC (Revision 1) categories of 0, 1, 2, 3, 4 and 68.<sup>1</sup> Data source for primary exports is U.N Comtrade and GDP values are from World Development Indicators, World Bank. Values of _SXP_ are available on an annual basis but they show little variation over our sample period.<sup>2</sup> Hence, we use initial year values of _SXP_ . To eliminate extreme values or temporary shocks, we use log values of the variable averaged over the five-year interval prior to the reform years (1998-2002).

According to _SXP_ , Japan is least natural resource abundant (-1.28) followed by Bangladesh, United States (-0.02) and Cambodia (0.31). At the high end, the most natural resource abundant country is Swaziland (4.46), followed by Bahrain (4.14), Tajikistan

> 1 This definition of primary exports follows Sachs and Warner (1995).

> 2 The correlation between current and one year lagged values of _SXP_ over our reform period (2003-2008) equals 0.957.

6
# Pull Your Small Area Estimates the up by Bootstraps

Paul Corral,<sup>_∗_</sup> Isabel Molina<sup>_†_</sup> and Minh Nguyen<sup>_‡_§</sup>

**Key words:** Small area estimation, ELL, Poverty mapping, Poverty map, Empirical best, Parametric bootstrap

**JEL classification:** C55, C87, C15

> _∗_ The World Bank Group - Human Development Group Chief Economist’s Office

> _†_ Universidad Carlos III de Madrid - Department of Statistics

> _‡_ The World Bank Group - Poverty and Inequality Global Practice

> §The authors acknowledge financial support from the World Bank. We thank Samuel Freije-Rodriguez, Roy van der Weide, Alexandru Cojocaru, and David Newhouse for comments on an earlier draft. We also thank Kristen Himelein, and Carlos Rodriguez for comments, suggestions and overall guidance. Additionally we thank Carolina Sanchez for providing support and space to work on this. Finally, we thank the Global Solutions Group on Welfare Measurement and Statistical Capacity, as well as all attendants of the Summer University courses on Small Area Estimation. Any error or omission is the authors’ responsiblility alone.
assessment and ongoing risk monitoring, cost-efficiency, data quality, results orientation, and donor relations. Specialized risks associated with FIFs include potential impact on the Bank’s strategy, its country-based model, start-up costs, and management of partnerships and conflicts of interest. According to the 2007 _Trust Fund Management Framework_ , core operational and trust fund controls should capture generic risks. Distinct financial review processes are being applied for trust-funded innovations involving financial engineering components. Innovative initiatives that support regional or global programs are subjected to an upstream Senior Management Review instituted since 2006.<sup>18</sup> Efforts are ongoing to rationalize these various controls.

> 18 World Bank. _Trust Fund Management Framework,_ 2007.

12
**Table 43. Importance of IPA services: Personalized contact and responsiveness to your company**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0438|0.0263|||0.126|0.0687|||
||(0.0549)|(0.0511)|||(0.111)|(0.112)|||
|Export % of sales|0.00113|0.000774|0.000981|0.000706|0.00194|0.00131|0.00167|0.00111|
||(0.000689)|(0.000646)|(0.000702)|(0.000663)|(0.00138)|(0.00142)|(0.00144)|(0.00148)|
|Share of sourcing via imports|0.00171**|0.00113|0.00168**|0.00107|0.00341**|0.00208|0.00341**|0.00200|
||(0.000762)|(0.000726)|(0.000789)|(0.000762)|(0.00154)|(0.00156)|(0.00163)|(0.00167)|
|High-income source country|-0.0810|0.0145|-0.0998|0.00572|-0.151|0.0271|-0.194|0.0148|
||(0.0749)|(0.0717)|(0.0770)|(0.0725)|(0.153)|(0.157)|(0.160)|(0.161)|
|>250 employees|0.0745|0.0641|0.0794|0.0774|0.161|0.136|0.167|0.164|
||(0.0683)|(0.0642)|(0.0675)|(0.0645)|(0.141)|(0.141)|(0.141)|(0.145)|
|Cumulative inv. >US$10M|0.0512|0.102*|0.0455|0.0998*|0.110|0.236*|0.108|0.244*|
||(0.0653)|(0.0598)|(0.0653)|(0.0602)|(0.133)|(0.132)|(0.135)|(0.135)|
|Years in country|-0.00172|-0.00353*|-0.00130|-0.00375*|-0.00394|-0.00830**|-0.00313|-0.00867**|
||(0.00220)|(0.00198)|(0.00219)|(0.00200)|(0.00423)|(0.00415)|(0.00422)|(0.00423)|
|Level of foreign ownership (%)|-0.00163|0.00124|-0.00174|0.00119|-0.00323|0.00273|-0.00354|0.00252|
||(0.00114)|(0.00110)|(0.00113)|(0.00109)|(0.00241)|(0.00246)|(0.00242)|(0.00245)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.010|0.115|0.025|0.126|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Personalized contact and responsiveness to your company]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

60
# 2 Data and Background

## 2.1 Setting and sampling

Madagascar is a poor, rugged, and transport-deficient island-nation of Sub-Saharan Africa. After long years of neglect, the government, supported by international donors, has recently begun to spend substantially to rehabilitate and expand its road network.

The setting for our study is approximately 50 square kilometers of the Madagascar highlands, southeast of one of the largest towns in the country, Antsirabe. Households were surveyed over a broad backward L-shaped swath of countryside from Betafo, a town just west of Antsirabe, southwards to the environs of Bemaha above the Mania river, which practically seals off the region from the south,<sup>4</sup> and then eastwards to the isolated village of Andrembesoa and the valleys to its south and east (see figure 1).

### Figure 1: Map of Study Area

For sampling purposes, the study area was divided into three zones according to remoteness. The least remote zone, the Betafo-Bemaha axis, consists of four communes contributing 900 households, or half the sample. Within each commune, five fokontany (extended villages) were randomly selected excluding the principal village of the commune (chef de commune). Then, within each of these fokontany, five hamlets were randomly chosen and a household list drawn up. Nine households were randomly selected from this list for interview. The second and third zones consist of the remote commune of Andrembesoa with its five fokontany (excluding the chef de commune). Four of these fokontany, make up our second zone, in which half of all households in each hamlet were randomly sampled (640 in all). Finally, fokontany Lohandany, nestled in the most westward and isolated valley on the map, makes up zone three, in which every one of the 258 households were surveyed.

Although the most distant households in our sample are less than 60 km from Antsirabe as the crow flies, the walk would take a local person about 3 days. Access to the main north-south highway in the east is blocked by mountain ranges (see figure 1). Moreover, due to the extremely steep terrain, much of the region, outside of the main valleys, is inaccessible to wheeled transport. Thus, agricultural output must generally be headloaded to the larger villages (e.g., Bemaha or Andrembesoa) for trans-shipment by ox-cart to the vicinity of Antsirabe.

Transport cost data are obtained as follows: Key informants in each of the 101 hamlets were asked about all markets utilized by the inhabitants for selling output, which in many cases were only intermediate points of sale on the way to Antsirabe. For each market,

> 4 The Mania has no bridges (except on the national highway far to the east) and it is difficult and/or expensive to organize a crossing by pirogue.

3
# **3. Descriptive Features**

Table 1 presents the top 20 countries outsourcing work to India on oDesk. The United States is by far the largest source of oDesk contracts going to India, with 31,261 contracts over the fiveyear period. A majority of all contracts on oDesk originate from the United States. The distribution of contract counts has a prominent tail. The United States is followed by Australia, the United Kingdom, and Canada, which combined equal about a third of the U.S. volume. Spain, the 10th largest country in terms of volume, has less than 1% of the U.S. volume.

Columns 4 and 5 show the share of contracts originating from each country that go to India, both in total and relative to cross-border contracts only (i.e., excluding oDesk contracts formed with workers in the source country). Contracts to India represent a 29% share of all contracts originating from the United States and a 33% share of cross-border contracts. Across the top 20 countries, India’s share of a country’s contract total volume ranges from 18% in Switzerland to 55% in the United Arab Emirates (UAE). The unweighted average of the top 20 countries is 28%. The UAE is an exceptional case that we describe further below.

Column 6 documents the share of company contacts in each country with an ethnically Indian name, regardless of how they use oDesk, while Column 7 provides the ethnic Indian percentage of company contacts on contracts that are being outsourced to India. For the United States, 3.9% of all company contacts who use oDesk are ethnically Indian, while the share is 4.6% for work outsourced to India.<sup>12</sup> This higher use for India specifically can be conveniently expressed as a ratio of 1.18 between the two shares. The average ratio across all 20 countries is 1.30, with 13 nations having a ratio greater than one. Finally, Column 8 of Table 1 lists the average hourly wage paid to Indian workers on outsourced contracts. The range across the top 20 countries is from $7 to $12, with an average of $10. As the average wage on oDesk for data entry 12 To put these figures in perspective, 0.9% of the U.S. population in the 2010 Census of Populations was born in India. These numbers are not exactly comparable, as our measure is based off of ethnicity, rather than country of birth, and includes South Asia more generally. Nonetheless, even after taking these features into account, the role of Indians on oDesk is perhaps twice as strong as the overall Indian population share. As a second comparison point, Kerr and Lincoln (2010) estimate the ethnic Indian share of U.S. inventors to be about 5% in 2005 using patent records from the United States Patent and Trademark Office. This second comparison point uses the same name matching approach as the current project. It thus suggests that Indians may use oDesk somewhat less as a share of total users compared to their general presence in high-tech sectors.

10
When , the absolute value of the derivative is bigger in (5b). This is an important implication of accounting for MR.

As we generalize to agreements with a small number (>2) of countries, MR becomes more important for the actual comparative static but also for the comparison between South-South and North-South agreements.<sup>9</sup> When countries 1 and 2 are part of an agreement involving 4 countries – 2 Northern and 2 Southern – the analogue to (5a) is where 1 and 2 are a Northern importer and a Southern exporter (or vice versa). When all 4 countries are Southern, the analogue to (5b) is Thus, with estimates of Beta from a gravity model and data on the GDP shares, we can conduct appropriate comparative statics. Table 1 gives illustrative values for the values of the terms in brackets, which we refer to as the multipliers. Based on 2000 real GDP values, the average share is 1.04%. This is dominated by a handful of large countries. For example, the United States and Japan have a combined GDP share of 45% with a resulting multiplier that is well below unity. While specific agreements with specific multipliers could be analyzed, our application will be to generic groupings. For this purpose, the table also has information on the average size of a Northern country and multipliers involving only Northern countries. Our main comparison of interest is between South-South and North-South agreements, where we see the former multiplier does not stray far from unity but the NS multiplier is below 0.9 once four countries are involved.

||Share of World GDP<br>(θ)|Multiplier<br>(2 countries)|Multiplier<br>(4 countries)|
|---|---|---|---|
|Average Pair|1.04%|0.979|0.938|
|US; Japan|27.8%; 17.6%|0.645|-|
|NN|3.40%|0.934|0.81|
|SS|0.25%|0.995|0.985|
|NS|3.40%; 0.25%|0.964|0.893|
|Ratio NN:SS|13.34|0.939|0.822|

Table 1: Shares and multipliers. Shares of world GDP based on 2000 GDP values in dataset. Multipliers use formula from equations 5 or 6 with share values as arguments.

Why is MR more important when bigger countries are involved? MR is a GDP-weighted average of bilateral trade costs, so big “third countries” have a large weighting in the other countries’ baskets. Changes in trade costs with these big countries therefore have a big impact on multilateral resistance. For example, if the third country is Uruguay, which is small, then trade cost changes have a relatively 9 We can generalize for the change in trade between countries a and b when k countries sign an agreement: . is the applicable coefficient. The bigger is k, the more important is MR. The extreme case is where k=n ie all countries sign a deal except with themselves, which is the multilateral reduction case applied to distance in Behar & Nelson (2009).

9
# _2.1.1. Agricultural residues_

Agricultural residues differ in their chemical composition, which leads to different biofuel yields per unit of feedstock. Table 1 shows the composition of select agricultural residue feedstocks, fraction of crop residues produced, and potential ethanol yield.

**Table 1: Composition and yields of different feedstocks (based on dry mass)**

||Residue<br>/crop ratio|Crop Dry<br>matter<br>(%)|Lignin<br>(%)|Carbohydrat<br>es (%)|Biofuel yield<br>(L kg<sup>-1</sup>of dry<br>biomass)|Yield<br>(kg/ha)|Biofuel<br>yield lt/<br>ha|
|---|---|---|---|---|---|---|---|
|Barley straw|1.2|88.7|9.0|70.0|0.31|1,184|367|
|Corn stover|1.0|86.2|18.7|58.3|0.29|1,734|503|
|Rice straw|1.4|88.6|7.1|49.3|0.28|1,399|392|
|Sorghum straw|1.3|89.0|15.0|61.0|0.27|736|199|
|Wheat straw|1.3|89.1|16.0|54.0|0.29|1,413|410|
|Sugarcane<br>bagasse|0.6|26.0|14.5|67.2|0.28|11,188|3,133|

Source: NRC (1958), EIA (2001), Kim and Dale (2004), and US DOE (2008a). Potential biofuel yields are estimated from the “Theoretical Ethanol Yield Calculator” (US DOE 2008b). Biofuel per hectare is calculated from average world yields.

We estimate the availabilities of agricultural residues extending the approach followed by Lal (2005) to calculate the global amount of crop residue production. Since data on the amount of crop residues produced is usually unavailable, these are approximated using the ratio between residue and crop production (by commodity, see Table 1) and the commodities production levels. A second step would involve determining how much of the crop residues produced could be actually removed and used for biofuel production.

Medium-run (2015/16) projected crop production levels for major producing countries were obtained from FAPRI (2009). The amount of residues that can be sustainably collected is still a contentious issue, and is affected by many factors, including topography, nutrient management, crop yields, climate, and tillage practices (Andrews 2006; Blanco-Canqui and Lal 2009). Given the large geographic distribution of potential availability and possible feed and other usage, following Kim and Dale (2004) we assume in this study that 40% of total produced residues can be used for biofuel 5
# **Table 5. Data Sources for Macroeconomic and Financial Variables**

|**Variable Name**|**Source**|**Series Name**|
|---|---|---|
|LN GDPPC|IMF WEO|GDP per Capita, Current Prices, U.S. Dollars|
|Inflation % change|IMF WEO|Inflation, End-of-period Consumer Prices, Percent<br>Change|
|Voice and Account.|World Bank<br>Governance Project|Voice and Accountability|
|Political Stability|World Bank<br>Governance Project|Political Stability and Absence of<br>Violence/Terrorism|
|Trade/GDP|WB WDI|Trade (% of GDP)|
|Dom. Deposits / GDP|World Bank FinStats|Domestic Bank Deposits/GDP, Data from<br>International Financial Statistics|
|Foreign Own.|Cull, Peria, and Verrier<br>2018|Foreign Ownership, Percent|
|Gov. Ownership|Cull, Peria, and Verrier<br>2018|Government Ownership, Percent|
|NPLs / Total Loans|IMF Financial<br>Soundness Indicators|Deposit Takers, Asset Quality, Non-Performing<br>Loans to Total Gross Loans, Percent|
|3 Bank Asset Conc.|World Bank FinStats|3 – Bank Asset Concentration, Data from<br>Bankscope|
|Strength legal rights|World Bank Doing<br>Business|Getting Credit Strength of Legal Rights Index|
|Depth credit info.|World Bank Doing<br>Business|Getting Credit Depth of Credit Information Index|

32