rafmacalaba/datause-encoder-data huggingface dataset:
sustainable development, and subsequent growth-theoretic papers by Hamilton and Clemens (1999), Dasgupta and Mäler (2000) and Asheim and Weitzman (2001) provided the theoretical underpinning – net (or ‘genuine’) saving, calculated as the net change in real wealth, is a dollarvalued measure of the change in social welfare. Negative genuine saving indicates that the country is on an unsustainable path.

An overarching issue in measuring genuine saving is the need to measure the change in _real_ asset values, measured as a fixed price times the quantity which is added to or subtracted from the underlying stock. The relevant price is the shadow price of a unit of the asset, or the change in social welfare associated with a marginal change in the quantity of the asset.

Barbier (2012) provides a useful overview of how production function techniques can be used to measure marginal values of ecosystem services, and we generalize this to the case of biodiversity in Annex I. The basic idea is that we can conceive the flow of benefits provided by a natural area as the result of a ‘natural production function,’ where the inputs to production are the different properties of the natural area, including biodiversity as measured (for example) by relative species abundance. Changes in the flow of benefits as well as the associated changes in the properties of the ecosystem can be used to model the production function econometrically, and the coefficients on the inputs to the production function represent the marginal values of the different ecosystem properties.

While it is simple to write out the math for this technique, as shown in Annex I, Barbier (2007) enumerates the complexities involved. In particular, this assumes the availability of data on changes in the economic benefits derived from a natural area as well as the associated changes in the components and properties of the natural area, including, in this case, the quantity of biodiversity. A quantitative understanding of the structure and function of the ecosystem is required. Market imperfections have to be taken into account, as well as stock effects when changes in ecosystem services are sufficiently large. For insurance services provided by biodiversity, the production function approach requires the estimation of the damages to human wellbeing avoided by having biodiverse natural areas.

If the SNA boundary were extended and existence values were brought into the national balance sheet, many of the difficulties in measuring existence value using stated preference methods would come to the fore. And it is unclear whether the total existence value would fall if a single species became (locally) extinct – for example, people might value the existence of other species more as a result of the extinction. Existence values may simply not lend themselves to marginal valuation.

Conversely, if values of the benefits from the use of natural areas, based on surveys of willingness to pay, were brought into imputed measures of total wealth and national income, it 9
indicators has implications for how we interpret the error terms in the relationship between observed indicator scores and the underlying concepts of governance.

The third respect in which these indicators differ is in the sample of countries they cover. A number of indicators cover a very large and broad sample of developed and developing countries (EIU, DRI, HFWSJ, PRS and WDR), while others cover very narrowly-focused samples of countries (PERC for Asia, CEER and FHNT for transition economies). Some indicators cover primarily developed countries but also include major developing countries (WCR, GALLUP, BERI). This difference between indicators is perhaps the most important for the empirical work which follows. There is by now considerable evidence that govemance on average tends to be better in richer countries. This implies that the distribution of governance is likely to be very different in indicators which cover sets of countries with different average income levels. These differences need to be taken into account when placing the observed data from various indicators into common units and combining them into aggregate govemance indicators.

In order to distinguish between indicators in this dimension, we construct a simple coverage index which measures differences between the distribution of countries across income and regional classifications and the distribution of all countries in the world across these categories. In particular, we divide the world into a two-way classification by region and income, following the World Bank's 1998 World Development Report. For each of the sources of govemance data, we report one-half of the sum of absolute -deviations between the share of countries in each of the 45 region/income categories in that source and in the world as a whole. By construction, this measure ranges from zero to one, with low values indicating more representative indicators. We report this number in the fifth column of Table 1. The five indicators covering the largest number of countries (DRI, EIU, HF, PRS and WDR) are substantially more representative according to this measure than the others, with a value of the coverage index of less than 0.25. In our subsequent empirical work we will refer to these as representative indicators, and the remainder as non-representative indicators.

Finally, we note that each of these sources of governance data uses different units to measure governance. Most polls of experts report discrete categorical responses (e.g. the prevalence of corruption on an integer scale from one to four), while 6
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45
-8-

1984). Results are presented here for the two main poverty lines: a 1984 expenditure of Rp 11,000 per month per person and one of Rp 15,000. The lower povert) line is chosen to :orrespond to that used in past Bank studies, after adjusting for inflation (Rao, 1984, 1986). We take this to be the preferred poverty line.

Table 1 gives our cardinal estimates of poverty in Indonesia for various poverty measures and for both poverty lines. All three measures (including the preferred "distributionally sensitive" measure) and both poverty lines indicate a significant decrease in poverty for both urban and rural sectors.

For the lower poverty line we find that the proportion of the population who are poor decreased from about one in three at the beginning of the period, to slightly over one in five by 1987; this is a substantial contraction over just three years. The corresponding absolute number of poor persons declined from 52 million to 36 million. The poverty gap measure implies that the aggregate consumption shortfall of the poor declined from about Rp 937 per month per head of Indonesia's population (representing about 5.5 percent of national mean consumption) to Rp 464 in 1987 (about 2.3 percent of the national mean). Such calculations are, however, contingent on the precise poverty line chosen. The higher poverty line in Table 1 also ir.dicate **s** ignificant declines in poverty, though the numbers are a good deal less dramatic.

Are the qualitative results robust to the choice of poverty line and measure? Figure 1 gives the cumulative frequency distribution of consumption for the two dates. The 1984 distribution lies entirely above the 1987 distribution. Thus the first-order dominance condition holds and so one can conclude that all well-behaved ("monotonic") poverty measures and
# **VII. Benefits from CONALEP Reformed Program**

Given that the CONALEP graduate tracer surveys of 1994 and 1998 are comparable, this section analyzes the impact of modular courses and reforn programs, innovations implemented by CONALEP after 1992. The cohorts from the survey of 1994 are considered to be graduates of the pre-reform program including cohorts who graduated in 1991, 1992, and 1993. Additionally, cohorts from the survey of 1998 are considered to be from the post-reform program including cohorts who graduated in 1994, 1995, 1996, and 1997. The first subsection describes the methodology used in the analysis, and the second discusses the results.

# **VH.1 Methodology**

There are several methods for estimating duration models. The Kaplan Meier and the proportional hazard model were calculated to analyze whether graduates from the cohorts of the 1998 survey of the reformed program found a job faster than those from the cohorts of the 1994 survey of the pre-reformed program. In addition, multinomial models were estimated to assess if the reformed program increased individuals' probability of studying further after completing CONALEP. They also permitted estimation of the probability of having a certain status in the job market. Regression models were estimated to assess if the reformed CONALEP program increases CONALEP graduates' earnings. See Annex 3 for details of the methodology used.

# **VII.2 Results**

Table 17 shows the Kaplan-Meier estimates of the mean and median time of job search after graduation. The median time for cohorts graduating before the reforms were introduced in CONALEP (Survey 94) is 4 months, while for those cohorts in the reformed program (Survey 39
Figure 1. Diagnostic algorithm for breathlessness <!-- Start of picture text --> Fever &/or YES Acute onset? YES Pneumonia<br>lcough?<br>NO | TB.<br>_ , ~~~~~NO<br>Swelling both YES<br>legs/feet? > Cardiac failure<br>|NO<br>|Wheezing? YES Acute onset after YES<br>injection/snakebite? Anaphylaxis<br>|NO<br>NO [Past history of YES Asthma or chronic<br>wheezing attacks? i obstructive<br>airways disease<br>NO<br>_ _ ~~~~~ - -- i~~~~>Other respiratory<br>Pale, cold, YES Severe abdominal NO )Ohrrsiatr<br>sweating? pain? algorithm forsee<br>shock<br>NO YES<br>Delirium, history of thirst++, polyuria? YES Diabetic keto-<br>acidosis<br>A NO<br>.Female of YES<br>reproductive age? Pregnancy<br>NO f YES/ complication?<br>NO YES/<br>NO<br>Sickle cell in YES<br>population?<br>I | Jaundice?<br>NO<br>NO YES<br>Sickle cell<br>crisis<br>NSee algorithm<br>for shock<br>L Pleural effusionor other<br>Notes: respiratory cause<br><!-- End of picture text -->

This algorithm assumes external and obvious maternal causes excluded. Based on algorithms designed for clinical use by Essex (1980).

41.
compute H-statistics using data on large banks rather than all banks for countries with many banks.

# 4. DETERMINANTS OF THE COMPETITIVENESS INDICATOR

# A. Cross-Country Regressions

We next identify factors that can explain the assessment of the competitiveness of the banking system across countries. To do so, we regress the average H-statistic on a number of country characteristics.<sup>8</sup> The regression model is as follows: _Hi = a + /iB,_ + _e,_ where _Hi_ is the average H-statistic for country i, based on individual bank data for the period 1994-2001, and B, is a vector of country characteristics. We run these cross-country regressions for our regular sample of 50 countries that includes only countries with at least 50 bank-year observations and at least 20 banks. As a robustness, we also run the cross-country regressions using a smaller sample of 39 countries that includes only countries with at least 100 bank-year observations and at least 20 banks.

As explanatory variables we use a number of variables also used in other cross-country studies to explain banking system performance, stability, and competitiveness. The explanatory data fall in four categories: market structure, contestability, inter-industry competition and general level of development. Data on banking structure and contestability typically refer to the 8 We also performed all regression results using the four individual estimates for each estimation technique as robustness test (not reported).

15
usual number of hours they reported working on Saturdays whenever the respondent reported working any of the six other days of the week for which direct observational data were available. For weeks for which no other work was reported, no adjustment was made; and (iii) for respondents reporting working only some Saturdays we followed the same procedure but instead of adding a day and hours to each week in which they reported doing any work, we randomly selected a number of weeks in which they reported doing any work. Selection probabilities corresponded to the number of times respondents typically worked on a Saturday in any month (e.g., if a respondent reported working two Saturdays each month, then additional days and hours would be added to 50% of the weeks they reported doing any work). Appendix C furnishes descriptive evidence suggesting that imputing Saturday hours based on usual hours reported at endline is not a bad (though certainly not ideal) approximation for actual hours worked.

# **5. Survey Mode Effects**

This section analyzes differences in reporting between the weekly in person and weekly phone groups during high-frequency interviews to assess survey modality effects. We use the weekly in person reports as the benchmark, since in-person interviews remain the dominant survey mode for both labor and household surveys. We restrict attention to those who reported being employed at baseline.

The following equation was estimated to assess the impact of survey modality on the reporting of labor market outcomes by respondents: where i is a labor market outcome of interest of individual _i_ in week _w_ (whether individual _i_ reported doing any work in week _w_ , days worked per week, and hours worked per week, both YY unconditionally and conditional on having reported any work that week); W is a dummy variable that takes the value 1 if respondent _i_ was interviewed over the phone once a week ii PPho oo WWo WW and 0 otherwise; the omitted category is thus the weekly in-person interviews; τ is a vector of calendar week dummies; and i is a random error term. Standard errors are clustered at the ii εε 11
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44
<mark>Maliszewska, M. and Van Der Mensbrugghe, D., 2019. “The Belt and Road Initiative: Economic, Poverty and Environmental Impacts.” Policy Research Working Paper 8814. Washington, DC: World Bank</mark> Morten, M. and Oliveira, J., 2018. “The Effects of Roads on Trade and Migration: Evidence from a Planned Capital City.” National Bureau of Economic Research Working Paper No. 22158.

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25

**Fig.** 2.6. Households' plans to improve housing conditions in the near future (by quintiles based on per capita income; % of each quintile)

<!-- Start of picture text --> 35 - 35 - 35 -<br>30 -30 -30-<br>5.7<br>25 25 - 9 25 -<br>6.5<br>20 12920 V10 20<br>15e15 15 33<br>10.9 97 9.5 5<br>10 2.710 S 10 8.3<br>20 \0i14 7<br>1 2 3 45 12345 1 2 5<br>Moscow, 1996 Vladimir, 1996 Gorodets, 1996<br><!-- End of picture text --> Note: 1- lowest quintile, 5 - highest quintile <!-- Start of picture text --> Sources: on a chargeable basis EL on free-of-charge basis<br><!-- End of picture text --> Sources: The Moscow Longitudinal Household Survey, January 1996; Vladimir and Gorodets Longitudinal Household Survey, December 1996.

Fig. **2.7.** Share of Moscow households who plan to improve housing conditions by income (by deciles based on per capita income; % of each decile) <!-- Start of picture text --> 35-<br>32.8<br>30-<br>8.128<br>25<br>20-<br>10~~~~~~~~~~~~~~1<br>15<br>10<br>I 5 6 7 S 9 10<br>-01993 ~ Z1996<br><!-- End of picture text --> Note: 1- lowest decile, 10 - highest decile Sources: Household Survey in Seven Cities, Moscow, Decemnber 1993; The Moscow Longitudinal Household Survey, January 1996.
Table 5. Meat Production ('000 mt)

||1975|1980|1985|1989|1989 asSof<br>1970|1989 as % of<br>world total|
|---|---|---|---|---|---|---|
|Bulgaria|553|651|723|814|147.20|0.48|
|Czechoslovakia|1,304|1,426|1,486|1,635|125.40|0.97|
|GDR|1,721|1,783|1,914|1,987|115.50|1.18|
|Hungary|1,297|1,441|1,595|1,588|122.40|0.94|
|Poland|2,786|2,858|2,513|2,801|100.50|1.66|
|Romania|1,337|1,774|1,835|1,628|121.80|0.96|
|EEUR/6|8,998|9,933|10,066|10,453|116.20|6.19|
|USSR|15,060|14,981|17,131|19,970|132.60|11.83|
|World|119,853|141,081|151,209|168,860|140.90|100.00|

_Source:_ FAO production yearbooks, 1975-89, Rome.