rafmacalaba/datause-encoder-data huggingface dataset:
- Income: sum of labor income and profits in past 12 months. Labor income is given by the answers to question tb35a from Section TB of Book 3A. It measures the sum of after-tax income from wage and salary, piecework, commissions and tips, extra hours, meals, housing, transportation, medical benefits, and others. Business income is either net income, or gross income if net income is missing.

- Education: from the Household Roster in Book C, question ls14 and ls15.

# First follow-up <u>(2005/06):</u>

- Migration: defined using several sources. First (i), we consider information from the household member’s roster, Section LS of Book C. For all individuals who left the household between 2001 and 2006, question ls19e (“Where does (...) live now?”) details if they live in the same locality, same municipality, same state, other state, or another country. According to the answer to this question, we define an indicator for migration that is equal to one if any type of migration has occurred. Second (ii), for individuals still living in the household, we verify whether the current residence of the household has changed with respect to baseline. Third (iii), we use information from the Permanent Migration module (Section MG of Book 3A). We know a respondent has migrated if she reports moving outside of the locality/neighborhood where she used to live in 2001 (question mg08a) and still live at the new location (question mg14). Last (iv), we add information on migrants to the US using the ”emigus” module.

# Second follow-up <u>(2009/12):</u>

- Migration: computed using several sources. First, individuals who moved out of their household at first follow-up are assumed to be currently living outside the household (and have positive migration status) unless they report having returned to the household in question ls01a of Section LS of Book C. Next, we identify individuals who moved between the first and the second follow-up using the analog of step (i) and (ii) of the procedure detailed for the first follow-up. We then consider information from the Permanent Migration module of Book 3A. Unfortunately, for confidentiality reasons the MxFLS does not report location codes of the destination of migration instances (although they are asked in question mg17 of Section MG of Book 3A). Therefore, for respondents migrating between baseline and first follow-up who also report a migration instance between the first and second follow-up we cannot distinguish whether the last destination is the same as the original location. We set the migration status of these respondents to missing. For those that report instead a migration instance

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# **3. SOME DATA ON COSTS**

Three studies have included calculations of the expected cost of a Safe Motherhood program. These include the Safe Motherhood Initiative: Proposals for Action, by Barbara Herz and Anthony Measham (1987), the PAHO Regional Plan of Action for the Reduction of Maternal Mortality in the Americas (1990), and work by the Columbia University Center for Population and Family Health (1986, 1991). Most other works have used cost assumptions from these studies.

It should be noted that these studies used data from different parts of the world and from different years. The Herz and Measham paper relied primarily on data from East Africa, as well as some data from Bangladesh. PAHO estimatt.s were from Latin America, while the Columbia data came from an extensive literature review encompassing a range of regions. Given varying socioeconomic characteristics, existing health infrastructure, needs and costs, these figures are unlikely to be comparable, nor can they be generalized to the full rarige of countries in which Safe Motherhood programs will need to be implemented. It should also be noted that each is based on differing assumptions regarding the impact of interventions, most of which have limitea empirical basis. As such, the estimates provide little guidance to resource allocation, since cost-effectiveness comparisons cannot legitimately be made.

Workshop participants noted that there is simply no substitute for "grubbing around" at the field level, taking the time to gather information about the costs of salaries, equipment, supplies, etc., so that detailed cost tables can be constructed on a country-specific basis.

Underlying these calculations are assumptions about the unit costs of the various inputs. This section examines these unit cost assumptions and the cost of the related programs. These studies generally divide the cost of a Safe Motherhood program into community level, health center level, and a hospital level costs, though sometimes the distinctions are not absolute.

At the community level, most of the estimated costs are associated with training. Herz and Measham estimate the following annual training requirements for each type of personnel. These cost are per person in 1987 dollars.

|TBAs|(5 days@$30/day)|$150|
|---|---|---|
|Outreach coordinators|<br>(5 days@$30/day)|150|
|Outreach volunteers|<br>(3 days @ $20/day)|60|
|Nurse-midwives|<br>(5 days@$30/day)|150|

These training costs per staff member are somewhat higher than PAHO estimates, even though PAHO estirmates are for 1990. This may be due to better initial education and skill level for Latin American health workers than the African and Asian workers considered by Herz and Measham. PAHO estirr.ates that continuing education for TBAs and lay midwives would cost about $90 per person per year, and that in-service training for nurses would cost about $250 per year.

Given their assumptions about training costs per person, Herz and Measham provide an 24
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# **V.   FACTORS EXPLAINING THE DECLINE IN TRADE ELASTICITY**

The previous sections have established that the trade-income relationship has changed over time and that the lower long-term trade elasticity helps explain the global trade slowdown in recent years and is likely to affect trade growth in the near future.

In this section, we take a first look at the causes of the changing association between trade and GDP. We first analyze the dynamics of trade and income data at a disaggregated level to identify key countries and regions where a structural break has taken place. Second, we investigate possible explanations for the lower long-run response of trade to income: (i) Changes in the composition of world trade, particularly the relative importance of goods and services trade; (ii) Changes in the structure of trade, particularly those associated with the international fragmentation of production; (iii) Changes in the composition of GDP, particularly the share of investment in aggregate demand; (iv) Changes in the trade regime, particularly the presumed rise in protectionism. While we cannot directly test the relative importance of these factors, we provide some circumstantial evidence on how far each might have contributed to the observed trade slowdown.

Needless to say, these explanations are neither exclusive nor exhaustive. They are not exclusive as changes in the composition of international trade, for example, are associated both with changes in vertical specialization and in the composition of GDP. These explanations are not exhaustive because the diminished responsiveness of trade may be related to other factors. For instance, rising wages in certain developing countries or the shale gas revolution in the United States may also have had an influence on the trade-income relationship. However, the explanations listed above are the ones that are commonly discussed in the literature (see, in particular, Irwin (2002)) and are the logical starting point of any analysis of the structural causes of the global trade slowdown.
|**Table 1: Ben**<br>|**chmark par**<br>|**ameters:**<br>||||
|---|---|---|---|---|---|
|Preferences|α = 0.75;|ε = 9.0;|Residential<br>construction|κ<sup>r</sup>= 1.0;|ν<sup>r</sup>= 1.67;<br>|
|Firms|β =0.6;|γ = 0.2;|Commercial<br>construction|κ<sup>f</sup>= 1.0;|ν<sup>f</sup>= 1.25;|
|Productivity|η = 0.01;|χ = 0.10;|Labor supply<br>elasticity|0; closed.<br>|2; ‘open’<br>|
|Commuting:|utility left af<br>_c_= 0.7:|ter 1 radius|Outside rent|r|= 1|

Levels of employment and residential density are generated by the model and, using the parameters given in table 1, they generate a benchmark city with distribution of employment and residential population illustrated in Figure 1.1. The geographical space is a disk centerd at {0, 0}, covered by a lattice of 745 hexagonal cells. Those taking zero value (dark blue) are outside the city, those taking positive value are inside. Employment in the city (Figure 1.1a) is concentrated in the center because this has the best ‘worker-access’, an effect magnified by spatially concentrated agglomeration economies. There are residents everywhere in the city (1.1b) although relatively few in the center where they are priced out by firms and high land rents.

The city has radial symmetry, so details of it can be seen more clearly by taking a cut from the center, and values of different variables along such a cut are shown in Figure 1.2, which has distance from the CBD on the horizontal axis. Land-rent varies by a factor of approximately five (near five in the center, unity at the edge, the edge endogenously given at distance 0.7 radii from the center where ). This variation of landrent drives the construction of taller buildings in the center, panel (1.2.b).<sup>6</sup> The marginal cost of building tall ii rr −rr̅ is greater for residential than for commercial building ( > ), this giving different height profiles. Employment and residential densities (i.e. people per unit area) are given in 1.2.c.<sup>7</sup> These are based on a νν<sup>r</sup> νν<sup>f</sup> combination of building height (i.e. floor-space constructed) and the proportions of land in commercial vs residential use in each cell. As is apparent, employment is concentrated within 0.4 radii from the CBD. The concentration of employment in the CBD is driven by quite large and spatially concentrated productivity spillovers. Using the parameters of table 1 and the equilibrium distribution of employment, spillovers imply that productivity in the CBD is 27% higher than it is at the city edge (1.2.d), where it is set at base value of unity; the fact that there is a lower bound on productivity means that some employment continues to the city edge.

> 6 The lines at the city edge are ‘fat’ because the hexagon lattice does not produce a perfect circle: the lines are averages over the set of edge points.

> 7 These are densities, not total numbers of people at each distance. There is much more land area (more cells) in a circumference away from the CBD than in one close to the CBD; the number of people is density times area.

8
organization that facilitates systematic review and meta-analysis of published studies in order to draw overall conclusions about efficacy of various treatments. It publishes a handbook that guides authors on how to conduct these reviews. The handbook includes a section on how to evaluate the risk of bias in estimates generated by RCTs based on the body of literature linking certain trial design and reporting decisions to biased treatment effect estimates. The handbook is updated frequently and has been used in 8,600 systematic reviews of trials<sup>2</sup> , which have together assessed the risk of bias in hundreds of thousands of scholarly articles (The Cochrane Collaboration 2010). This increased scrutiny during peer review and after publication has resulted in a reduction, over time, in the presence of the biases described above in medical RCT reports (Plint et al. 2006, Kaplan and Irvin, 2015).

The US Food and Drug Administration (FDA) uses a similar set of standards to approve the sale of pharmaceuticals for public consumption. The progress of studies through each stage of this approval process illustrates the importance of such standards in screening false-positive results. For a drug to be approved by the FDA, it must pass three “phases” of trial. There is increasing scrutiny at each phase, such that phase two trials have a higher burden of proof than phase one but less than phase three, whose standards most resemble the CONSORT standards. Among trials that enter phase two, only 70 percent progress to phase three. Of those, 40 percent fail to show positive results in the two phase three trials usually necessary for FDA approval (Danzon, Nicholson, and Pereira 2005).

> 2. According to http://www.cochranelibrary.com/cochrane-database-of-systematic-reviews/index.html, accessed 11 February 2015.

7
45. Balance sheet ratios _alone_ should not be used to measure liquidity risks since they fail to properly capture<sup>45</sup> : expected funding needs or commitments, available borrowing sources, and banks’ ability to convert assets to cash. They are point-in-time indicators but not reliable risk measures, hence they should not be relied upon solely but used in conjunction with dynamic forward-looking tools such as cashflow reports or sources and uses of funds analysis. They should always be used in conjunction with more qualitative information about borrowing capacity, such as the likelihood of increased requests for early withdrawals, decreases in credit lines, transaction sizes, and concentration of assets, or shortening of term funds available to the bank. These exercises should also be stressed not to assume that ample liquidity is a given.

46. Moody’s Analytics<sup>46</sup> recommends a balanced approach to put into perspective ratio analysis, looking a trilogy:

|Absolute Standards (ratios)|Trend Analysis Peer Analysis|
|---|---|

Some additional useful balance sheet ratios are<sup>47</sup> :

- **(Short-term liquid assets/short-term liquid liabilities):** ideally greater than one (after a discount of asset prices to reflect potential volatility, although most likely these assets trade near par in ready markets even in periods of stress).

- **(Total loans/ total deposits ratio),** this ratio has been particularly important in the crisis countries in Europe and Central Asia where ratios in excess of two in countries like Kazakhstan and Latvia signaled great vulnerability to rollover and foreign exchange risks as it showed that more half of domestic FX loans were being funded with FX external borrowing with large maturity gaps.

- **(Borrowed Funds/total assets ratio) or (Market Funds – Liquid Assets)/ Total Assets** to assess market funds reliance.

- **(Liquid assets – Short term liabilities):** basic surplus measures the cushion that liquid assets provide over immediate funding needs.

- **(Total and net overnight funding volume/total assets):** this ratio shows what proportion of funding is purchased in the overnight funding markets.

- **(Loan commitments/outstanding loans ratio)**

- **Liquid assets ratio: (Liquid assets/short-term assets)**

- **Leverage ratio: (total assets/total equity),** this ratio is really a component of the return on equity ratio (ROE) which can be decomposed as the product of the return on assets (ROA) times the leverage ratio (ROE=ROA x Leverage).

- **(Total loans/ total equity capital)**

> 45 Office of  Comptroller of the Currency, ‘The Challenges of Sound Liquidity Risk Management: OCC Expectations and Policy for Community Banks’ , May 2001

> 46 Moody’s Analytics, “Restructuring Financial Institutions: Training Course”, World Bank, October 26-29, 2009. 47 OCC, Handbook, page 62
# <u>Introduction</u>

In 1993, anticipating the imminent majority government by the African National Congress, researchers in South Africa sought to establish a baseline of economic welfare in order to guide and assess the new government’s progress. However, as the [then] Central Statistics Office was widely distrusted, the project was undertaken by a wide consortium of social scientists, health specialists, labor union representatives, and members of civil society convened by the Southern Africa Labour and Development Research Unit (SALDRU). To no surprise, the process of designing a survey by committee generated a number of lively debates. One, in particular, was in regards to documenting informed consent.

Many participants felt strongly that it was imperative that the survey teams not only inform the respondents of the research nature of the survey and the goal of confidentiality but also that the respondent had to indicate that he or she understood this with their signature. Others felt that in the prevailing political climate and with the limited literacy in rural areas this requirement would doom the project. Not only would a significant share of the sample decline to participate, as this group would be self-selected, the sample would no longer be representative of the population. Consensus, not voting, was a means for decision making among the team designing the survey, but casual observation suggested that the views of the social scientists differed from those in health fields, with economists offering an additional nuance, that answering the questions in the survey instrument was a form of revealed preference and thus implicit consent, a view that eventually prevailed. In the event, few respondents refused to be interviewed.

This incident serves as an introduction to some of the themes of this essay. It illustrates that many of the practical issues confronted in designing ethical field trials also need to be 2
# **Appendix 1:** Beta Convergence

We measure the convergence of income and inequality among the EU regions using Beta convergence. This method was developed by Sachs and Warner (Sachs and Warner 1997) following the Solow Growth Model. This tool can measure the trend of two groups of regions in terms of economic growth, presenting the dynamic picture of their discrepancies. Old member states (OMS) and new member states (NMS) were analyzed separately in order to show the income gap within the EU.

Beta convergence is the result of regressing the annual growth rate of the analyzed variable on the log of the variable from the previous year: We use both the OLS regression and the fixed effects model (LSDV) to estimate the beta convergence. The regressions are weighted using the average population in each region across the analyzed period (2005 – 2014).

Table 1 shows the results on several income and inequality measurements that we selected. The speed of convergence (β) is calculated as: Where T is the elapsed time, which equates 11 in this case.

The half‐life of convergence indicates how long it would take for the two groups of economies to converge. Following the interpretations in Mathur (2005), it is roughly equal to<sup>log �2�</sup> � . β

# **Appendix 2:** Country specific effects

A key step in analyzing the countries’ stances in using the CAP fund is to calculate the country specific strength of association between poverty and CAP as well as between poverty and agriculture. We try to gain a comprehensive understanding on two issues: if the CAP funds are going to the poor areas in a country and if a country’s agricultural activities take place mainly in the poor areas.

In order to draw a picture of where each country stands, we use the 2011 NUTS 3 level data for this analysis. The CAP funds distribution data are from the DG AGRI Clearance Audit Trail System database (CATS), provided by the European Commission. The 2010 agricultural indicators from the EU Farm Structure Survey are used to represent the regional agricultural situations. Since the German agricultural data are only available at the NUTS 2 level, they are used as proxies for the NUTS 3 level agricultural variables.

22
<!-- Start of picture text --> NSSO expenditure as percent of NAS expenditure<br>100.0<br>90.0 O6<br>80.0 7.6<br>70.0<br>60.0 2<br>50.0<br>43.9<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>1972-73 LOTT-78 1983-84 LOST-RS 1993-94 2004-05 2009-10<br>==NSS0 expenditure as percent of NAS expenditure<br><!-- End of picture text -->
# **1. Introduction**

Peru experienced strong and sustained economic growth in the post-1990 period, which supported remarkable gains in social and economic development. Between 2002 and 2019, Peru grew at an annual 5.1 percent rate on average (Figure 1), well above the 2.7 percent average in Latin America. High growth rates enabled Peru to double its per capita income and reach upper-middle-income status in 2008. Between 2002 and 2013, GDP growth was exceptionally high, averaging 6.1 percent annually (5.2 percent in per capita terms), driven by good economic reforms and the commodity boom.

However, economic growth halved to 3 percent in the 2014-2019 period, partly driven by the end of the commodity supercycle and because of a lack of substantial reforms. The less favorable external environment during the 2014-2019 period brought to light the more profound structural challenges of Peru’s growth model, such as low productivity growth (World Bank, 2022). The pursuit of economic development in Peru in the coming decades requires the search for new growth drivers.

This paper studies Peru’s long-run economic growth prospects and what it would take for the country to attain high-income (HI) status. Accelerating economic growth is crucial. For example, if Peru continued at its current trend growth rates in the post-COVID period of 2.5%, it would take almost half a century to achieve HI status. In contrast, if Peru grew at a rate of 5 percent, the average rate during the 2002-2019 period (including the commodity boom and bust period), it would only take 14 years (Figure 2).

Figure 1. Historical GDP Growth Figure 2. Real GNI Per Capita<sup>2</sup> <!-- Start of picture text --> 10.0 32,000<br>Actual<br>5.0<br>16,000<br>0.0<br>8,000<br>-5.0<br>4,000<br>-10.0 Forecast<br>-15.0 2,000<br>HL GDP growth GDP PC growth GNI Per Capita Simulated GNI per capita */<br>Forecast HL GDP growth Forecast GDP pC growth High Income Threshold Baseline<br>Source: World Development Indicators and WB staff`s Source: World Development Indicators.<br>forecasts */ Simulated with a constant growth rate of 5% (2022-19<br>Note: The series are the 3-year centered moving average. average).<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050<br><!-- End of picture text --> We use the World Bank Long-Term Growth Model suite (LTGM, Loayza and Pennings 2022) to evaluate ways to boost growth and achieve high-income status more quickly. The standard LTGM is a Solow-Swan 2 Methodology used for Figure 2: Calibrate the levels to the GNI per capita Atlas method (NY.GNP.PCAP.CD) for 2018, and then cast backwards using real GNI growth (NY.GNP.PCAP.KD.ZG). The horizontal line shown is the 2022-23 high income threshold.

2
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# Fig 22

<!-- Start of picture text --> WAGES + BENEFITS shares from cost side<br>esots euf rtm Ill<br>0.6<br>0D.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0-<br>1 Q91 2Q91 3Q91 4Q91 1Q92 2092 3092<br>wage bil bonus fund social is. tax C emprloMent fund<br>pensmin fund [ social fund<br><!-- End of picture text -->