rafmacalaba/datause-encoder-data huggingface dataset:
We also found evidence of lagged effects of past incomes on _SAW_ . (Ravallion and Lokshin, 2002, report regressions including two years lags, using an earlier round of the RLMS.) However, given that we lose many observations, and including lagged effects did not change our main results, we confine attention here to the cross-sectional regressions for 2002.

Confirming the results of Figure 2, and consistently with the findings of Cockerham (2000) using the 1995 RLMS, we find no sign of an income gradient in _SAH_ controlling for other socio-economic covariates. However, a strong income effect for _SAW_ (for both men and women) is evident in Table 4.

We find generally more agreement between _SAH_ and _SAW_ in the effects of the “nonincome” variables. Both subjective variables tend to fall with age (though over the range of the data for health, and only up to around 50 years for welfare).<sup>16</sup> Both variables tend to be higher for adults living in larger households, though other demographic effects are in less agreement. Education tends to raise _SAH_ (though more so for women than men), as it does for _SAW_ . The region with the highest _SAH_ (controlling for our other covariates) tends to have the highest _SAW_ , though there are discrepancies in ranking amongst other regions. The similarity in the nonincome covariates generates a strong positive correlation between the predicted values for _SAH_ and _SAW_ ; the correlation coefficient between the estimated conditional expectations is 0.64.

There are some notable gender differences in the socio-economic covariates of health and welfare. Education has a much stronger effect on female _SAH_ than for men, while demographic effects are stronger for men. For men, being in the workforce (either with a job or unemployed) has a strong positive effects on _SAH_ (relative to not being in the workforce) but this effect is not evident for women. This could reflect a reporting bias whereby male leisure is seen to be more

> 16 It might be conjectured that the gradient is stronger for younger people, with little experience of the older regime of universal health care.  This does not appear to be the case; when we added the product of age and expenditure to the regressions in Table 4, the interaction effect was not significant.

19
# **Table** **<u>3. Descriptive Statistics</u>**

||(1)<br>Mean|(2)<br>SD|(3)<br>Min|(4)<br>Max|(5)<br>Obs|
|---|---|---|---|---|---|
|**Project level:**||||||
|Project Funds (in 1,000 ugx)|22750.6|32741.9|7612|162670|895|
|Livestock project (0/1)|0.709|0.454|0|1|895|
|Project start date (Period when grants were received)|38.188|3.935|1|48|812|
|Project overall score (std)|0.032|0.949|-2.9|3.28|872|
|Project quality score (std)|0.043|1.021|-2.86|3.23|871|
|Project quantity score (std)|0.016|0.928|-5.71|12.1|863|
|Project is missing (0/1)|0.027|0.162|0|1|895|
|Project Implementation Quality Index|2.38|0.905|0|4|705|
|Satisfaction with supplier Index|5.629|1.366|0|8|660|
|Hired a Contractor to Implement Project|0.388|0.487|0|1|800|
|Index of challenges in Contracting Process|3.332|1.868|0|9|301|
|Satisfaction with contractor Index|5.375|1.77|0|8|459|
|Index for Intensity of Project Community Monitoring|2.957|1.139|0|4|821|
|Index for Intensity of Social Accountability Committee Project Monitoring|0.831|0.954|0|4|863|
|Satisfaction with NDO Index|5.912|1.407|0|8|839|
|Satisfaction with District Vet Index|10.638|1.463|6|15|572|
|**Animal level:**||||||
|Animal dead (0/1)|0.13|0.336|0|1|6891|
|Animal sold (0/1)|0.051|0.22|0|1|6891|
|Animal stolen (0/1)|0.017|0.131|0|1|6891|
|Animal dead/sold/stolen (0/1)|0.198|0.399|0|1|6891|
|**Beneficiary level:**||||||
|Number of Cattle (Total)|2.452|10.5|0|800|6961|
|Number of Goats (Total)|4.206|7.018|0|230|6966|
|Number of Livestock in Tropical Livestock Unit|1.816|5.523|0|406.5|6952|
|Reporting NUSAF-related issues (total)|1.052|1.310|0|4|6966|
|Reporting NUSAF-related issues to LC1|0.405|0.491|0|1|6964|
|Reporting NUSAF-related issues to Subcounty|0.305|0.460|0|1|6961|
|Reporting NUSAF-related issues to District|0.203|0.402|0|1|6963|
|Reporting NUSAF-related issues to IG|0.141|0.348|0|1|6957|
|<br>Trust Project Leaders (1-4)|3.582|0.712|1|4|6952|
|Trust LC3 Chairperson (1-4)|3.181|0.93|1|4|6937|
|Trust Subcounty Bureaucrats (1-4)|3.295|0.809|1|4|6921|
|Trust LC5 Chairperson (1-4)|2.997|1.011|1|4|6892|
|Trust District Bureaucrats (1-4)|3.297|0.859|1|4|6933|
|TrustGovernment(1-4)|3.635|0.66|1|4|6932|

39
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23 Table 8. Shae of Potential Exports from the NIS Vulneable to EC Trade Banirs (perent)

||NTBs|Share of Exp<br>and/or|orts wbject to<br>both N|TBs and|Nonta|iff Brrir||iff|
|---|---|---|---|---|---|---|---|---|
|||||||aes|Ta|rs|
||Higher T<br>Inter-rep|ariffs<br>Extra-re|Higher<br>Intrr|Tariffs<br>E|||||
||.<br>|p.|e-ep.|xtrt-rep.|Itr-rep.|Extra-rep.|Inter-rep.|Exta-rep.|
||(a)<br>|(b)<br>|(c)<br>|(d)|(e)|(0|(g)|(h)|
|Armenia<br>|64<br>|71<br>|25<br>|11|_5_|9|37|_55_|
|Azerbaijan<br>|93<br>|95<br>|13|8|14|10|_75_|_85_|
|Gcorgia<br>|79<br>|93<br>|30<br>|19|30|22|46|82|
|Estonia<br>|84<br>|73<br>|19|16|25|44|39|25|
|Latvia<br>|70<br>|57<br>|13<br>|8|14|22|48|33|
|Lthuania<br>Bl|69<br>|77<br>|11<br>|3<br>|13|10|42|66|
|earus<br>Mld|83<br>|90<br>|8<br>|3<br>|6|3|73|87|
|oova<br>|82<br>|85<br>|22<br>|15|22|27|46|24|
|Russia<br>|71<br>|70<br>|10|4|10|6|65|66|
|Ukraine<br>|80<br>|76<br>|21<br>|16|25|18|68|62|
|Kazakhstan<br>Kh|64<br>|90<br>|23<br>|28<br>|14|27|42|81|
|yrgyzstan<br>|75<br>|95<br>|10|4|13|8|55|86|
|Tajikistan<br>|91<br>|99|26|18|28|19|52|80|
|Turkmenistan<br>|97<br>|98<br>|21|68|22|70|69|28|
|Uzbekistan<br>|88<br>|96<br>|26<br>|56|24|57|58|39|
|FSU ALL|75|73|14|7|14|9|63|66|

Source: Based on the 1990 Goskomstat inter-and extra-republic tiade data and data on taiffs and NTBs compiled fiem SMART.
smoothed since the accessibility and the urbanization variables do not depict the same high variability as the formers.

Table 1: Share of complete patterns in panel databases

|Sector||Initial|Moving ave<br>|rage panels<br>|
|---|---|---|---|---|
|<br>Code|Sector|<br>panel|excluding<br>missing<br>observations|including<br>missing<br>observations|
|31|Food, Beverages and Tobacco|65%|75%|75%|
|32|Textile, Wearing Apparel and Leather Industries|37%|48%|50%|
|33|Wood and Wood Products, Including Furniture|17%|29%|33%|
|34|Paper and Paper Products, Printing and Publishing|22%|30%|41%|
|35|Chemicals and Chemical, Petroleum, Coal, Rubber and Plastic Products|36%|41%|46%|
|36|Non-Metallic Mineral Products, except Products of Petroleum and Coal|39%|60%|60%|
|37|Basic Metal Industries|31%|35%|52%|
|38|Fabricated Metal Products Machinery and Equipment|51%|63%|63%|
||Source: Authors’ calculation.<br>,||||

# _Spatial concentration of sectors_

Before estimating the econometric relation derived in Section 2, it is worth exploring some basic patterns describing Turkish provinces and their sectoral distribution. To prospect the spatial concentration of the eight manufacturing sectors, we use the absolute Herfindhal index computed as follows: where _nr,s_ is the number of firms of sector _s_ operating in province _r_ , and _ns_ is the total number of manufacturing firms operating in sector _s_ . The maximum value of _Hs_ is 1 and corresponds to a totally concentrated sector, while its minimum (1/65) corresponds to a perfectly dispersed sector. Figure 2 plots the evolution of the Turkish sectoral Herfindhals over the period 1980-2000.PF<sup>4</sup> FP The general trend of the Herfindhals is decreasing, indicating a dispersion of the manufacturing sectors over time. The textile sector was concentrating until 1990 and then started to spread too. The most concentrated sector is the Paper and Paper Products sector with a Herfindhal varying form 0.5 in 1980 to 0.30 in 2000.

By computing for each sector the threshold ( _H s_ max + _H s_ min ) / 2 and the average number of operating firms, we can focus on provinces receiving higher than average share of firms operating in spatially concentrated sectors as depicted in Table 2. This Table indicates

> <sup>4</sup> We use the five-year moving average database excluding missing observation to compute these Herfindhal index.

8
# **1. Introduction**

The World Bank has used an income classification to group countries for analytical purposes for many years. The method was presented in the first _World Development Report_ (World Bank, 1978), and its origins can be traced even further back. In 1965, for instance, a published essay “The Future of the World Bank” used gross national product (GNP) per capita to classify countries as very poor, poor, middle income, and rich (Reid, 1965).

The current form of the income classification has been used since 1989. It divides countries into four groups—low income, lower middle income, upper middle income, and high income—using gross national income (GNI) per capita valued annually in US dollars using a three-year average exchange rate (World Bank, 1989). The cutoff points between each of the groups are fixed in real terms: they are adjusted each year in line with price inflation. The classification is published on http://data.worldbank.org and is revised once a year on July 1, at the start of the World Bank fiscal year.

The World Bank uses the income classification in _World Development Indicators_ (WDI) and other presentations of data; the main purpose is analysis. The classification is often mistaken as being the same as the Bank’s operational guidelines<sup>1</sup> that establish lending terms for countries (International Development Association, 2012). While the income classification itself is not used for operational decision-making by the World Bank and by itself has no formal official significance, it uses the same methods to calculate GNI per capita and adjust the thresholds that are used in the operational guidelines. The methods currently in use for this have previously been agreed with the World Bank’s Board of Executive Directors (World Bank, 2000).

Multiple users, ranging from policy makers, the business community, media, and students, have become familiar with the Bank’s datasets and income classification. Over time it has become part of the development discourse, and academia and the news media frequently find it a useful benchmark to analyze development trends. The classification is used by other international organizations and bilateral aid agencies for both analytical and operational purposes. Some use it to inform decisions regarding resource allocation; governments in Europe and the United States have used the classification for setting rules regarding preferential trade

> 1  World Bank Operational Policies, OP3.10.

2
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1. GDP 1960<br>2. Confucian 5. Sub - Saharan<br>3. Life Expectancy 4. Equipment Investment<br><!-- End of picture text -->

Fig 4. Pairwise joint growth determinants. (Nodes and graphs are proportional to posterior probabilities.) see no jointness beyond the first five variables, these additional regressors have to be alternating. Evidence suggesting that variables do appear on their own, but not jointly will be denoted by _disjointness_ . This can occur, _e.g._ , when variables are highly collinear and are proxies for eachother. We can, again, interpret posterior odds directly, and similar thresholds (now favoring models with separate inclusion) can be used for disjointness. See Fig. 3 for a graphical display. Table 3 summarizes the posterior odds for assessing disjointness. Now 757 pairs of variables (92.3% of total) display some degree of disjointness—leaving only 63 pairs with posterior odds, _i.e._ , _Jij_ , larger than 1 _/_ 3. In line with Fig. 2, decisive disjointness occurs only for determinants with small posterior inclusion probabilities (usually under 5%). The only exception is decisive disjointness between Civil liberties and Political rights, which are indeed likely to be good substitutes for each other. For triplets of variables, we get even more evidence of disjointness: 8,752 of the 10,660 possible triplets ( _i.e._ , 82.1%) indicate decisive disjointness.

**Table 3.** Pairwise Disjointness

|Evidence|posterior odds|Number|Percentage|
|---|---|---|---|
|Favorable|1<br>10 <sup>_< PO ≤_1</sup><br>3<br><br>|79|10.4|
|Strong|1<br>30 <sup>_< PO ≤_</sup><br>1<br>10|182|24.0|
|Very Strong|1<br>100 <sup>_< PO ≤_</sup><br>1<br>30<br>|353|46.6|
|Decisive|_PO ≤_<br>1<br>100|143|18.9|
|Total||757|92.3|

In conclusion, the five most important variables (at the top of Table 2) are not mutually exclusive determinants and tend to appear jointly. This is perhaps not surprising as they seem to capture rather different explanations for growth. The variables in the data set with moderate marginal inclusion probabilities do not have strong jointness or disjointness relations (with one exception), while the variables with least marginal importance tend to avoid occurring jointly and alternate their presence in the visited models.

9
organizations such as the International Energy Agency (IEA); World Resources Institute (WRI), a Washington DC based environmental policy research organization; national institutions particularly those preparing National Communications to the UNFCCC, etc. The selection of EIA for data source is based mainly on two reasons. First, the focus of this study is on future emissions instead of the historical emissions, sources like WRI and UNFCCC do not forecast future emissions<sup>6</sup> . IEA projects future emissions, but with a lower details as compared to EIA<sup>7</sup> . Second, the IEA does not provide the projection of emission intensities which is the primary element of this study. Note, however, that emissions projections between IEA and EIA are not significantly different; IEA’s reference case projections of energy related global CO2 emissions are 2 percent and 6 percent smaller than those of EIA’s in 2015 and 2030, respectively.

# **3. Global CO2 Emissions and Intensities**

> 6  While UNFCCC provides historical emissions for all Annex I countries, it does not cover those Non-

> Annex I countries which have not submitted necessary information, such as, National Communications.

7 IEA (2006) does not provide CO2 emission forecasts for years 2010, 2020 and 2025, whereas EIA (2007) provides the forecasts at 5 years interval for the 2004-2030 period.

-8-
in ENILEMS 2010 using the official algorithm for generating the CURP.<sup>8</sup> A simple merge of ENILEMS and ENLACE Grade 12 using the pseudo-CURP and CURP, respectively, was able to match 2,820 observations (40 percent of the ENILEMS sample). This relatively low matching rate is almost entirely explained by the lack of the last three digits in the pseudo-CURP. An additional 18 percent of the sample was recovered manually by identifying coding or registration errors in the CURP generation process (i.e. errors in birth date or misspelled names). Overall, 58 percent of the individuals in the ENILEMS sample were matched to their ENLACE Grade 12 test scores. After eliminating missing observations in the ENLACE score, the panel reaches a total of 3,714 observations. The ENILEMS-ENLACE panel also includes the information from ENOE for all 3,714 matched observations plus information from ENLACE’s context questionnaire for 13 percent of the panel. Table A.2 in the Online Appendix reports differences in means between the observations of ENILEMS that were merged with ENLACE scores and the ones that were not. Statistically differences are very small and hard to interpret, as they relate to the survey sampling, design, or implementation.

Table 2 presents summary statistics for the ENILEMS-ENLACE panel dataset. Columns 2 and 3 in Table 2 decompose the sample into those who are enrolled in college and those who are not. Individuals enrolled in college have on average considerably higher Grade 12 ENLACE scores than individuals out of college (by around 0.38 SD), but they are also more likely to have a higher GPA in upper secondary, to have graduated from a private secondary school, and to live in an urban area. Interestingly, about 26 percent of college students are employed versus 58 percent of those out of college being employed.

> 8The CURP is an 18-digit unique personal identifier formed by a combination of letters and numbers taken from the individual’s full name, date of birth, sex, and state of birth, plus a three-digit code assigned by RENAPO, the Mexican population council. For more information, see https://renapo.gob.mx/swb/.

11
tenure, the APC approach cannot account for these compositional changes, and a different approach is needed to estimate tenure trends.

This section presents an individual-level analysis of the evolution of job tenure conditional on workers’ and employers’ characteristics that can change over time. The primary objective of this analysis is to understand whether a long-term decrease in job tenure -like the one found in the APC analysis- could be explained by changes in individual characteristics of the European labor force. The persistence of a trend in tenure after controlling for these characteristics would imply that other factors, probably operating at the economy-wide level, may drive the evolution of job tenure.

We estimate the probability that a person holds a job for less than a year, between 5 to 10 years, and more than 10 years over the time span of our surveys.<sup>12</sup> We capture both the short- and longterm (countercyclical) effects of business cycles on job tenure by covering these tenure periods. In the period of economic expansion, new workers are hired, lowering the average tenure; during an economic contraction, hires are reduced, increasing the average tenure (Arozamena and Centeno <u>2006). If job turnover increased over time, the share of employees with less than 1 year of tenure</u> (short-term jobs) should have increased, while the shares of employees with more than 5 years (medium-term jobs) and more than 10 years (long-term jobs) should have declined.

The economic models of quits and layoffs (Parsons 1986; <u>Mortensen 1986) provide the basis for</u> the empirical estimation strategy presented in this section.<sup>13</sup> The likelihood that a worker reaches a given job tenure depends on the probability of being terminated. That probability is a function of the worker’s characteristics, her job characteristics, and the characteristics of the employer. The worker’s outside opportunities are affected by her age, education, and occupational group. Characteristics of the household the worker resides in, and the worker’s marital status affect her preferences about job stability. For example, married workers may be less inclined to relocate to a job if their spouse is also working. Workers with school-age children might be reluctant to move to take a new job. Race, ethnicity, disability, and chronic illnesses may also affect the probability of job termination.

> 12 Ten years is about the average job tenure for the workers in our sample. Gregg and Wadsworth (2002) used similar tenure categories in analyzing the evolution of job tenure in Great Britain between 1975 and 2000. 13 Sickness and retirement might also determine the duration of job tenure. We assume that the factors explaining the probability of layoffs and quits are also relevant for the retirement decision and determine the probability of getting sick (Burgess and Rees 1998).

15
**<u>Table 1A: Cross-country regressions explaining variation in returns to schooling and returns to experience, by gender and region</u>**

||**To**<br>|**tal**<br>|**M**<br>|**ale**<br>|**Fe**<br>|**male**<br>|**Ur**<br>|**ban**<br>|**R**<br>|**ural**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|**Institutions**|**_Ed:_**���|**_Exp:_**���|**_Ed:_**���|**_Exp:_**���|**_Ed:_**���|**_Exp:_**���|**_Ed:_**���|**_Exp:_**���|**_Ed:_**���|**_Exp:_**���|
|Economic Freedom|0.089**|0.086**|0.082**|0.099**|0.088**|0.044|0.084**|0.086**|0.057|0.091**|
|Index|(2.44)|(2.48)|(2.18)|(2.69)|(2.35)|(0.96)|(2.12)|(2.57)|(1.23)|(2.27)|
|Glblii Id|-0.001|-0.048*|-0.003|-0.039|-0.018|-0.074*|0.007|-0.076**|0.001|-0.018|
|oazaton nex|(0.01)|(1.82)|(0.08)|(1.39)|(0.51)|(1.91)|(0.19)|(2.76)|(0.02)|(0.56)|
|Empowerment rights|0.209|0.058|0.117|0.041|0.229|0.303|0.198|0.092|0.304|0.070|
|index|(1.07)|(0.33)|(0.65)|(0.24)|(1.04)|(0.87)|(1.06)|(0.45)|(1.12)|(0.36)|
|Democracy (Freedom|-0.057|-0.219|0.031|-0.220|-0.157|-0.366|-0.011|-0.349*|-0.207|-0.202|
|House/Imputed Polity)|(0.29)|(1.17)|(0.16)|(1.16)|(0.76)|(1.17)|(0.06)|(1.66)|(0.78)|(0.88)|
|Women in Leislature|0.107**|-0.008|0.094*|0.004|0.101*|-0.024|0.128**|0.019|0.097*|-0.013|
|g|(2.12)|(0.28)|(1.95)|(0.11)|(1.91)|(0.64)|(2.46)|(0.51)|(1.74)|(0.34)|
|Ctt|0.98|3.51**|0.865|2.22|4.20**|6.65**|0.63|5.67**|2.58|0.88|
|onsan|(0.48)|(2.32)|(0.43)|(1.31)|(1.99)|(2.24)|(0.29)|(3.22)|(0.98)|(0.43)|
|R2|0.17|0.11|0.16|0.10|0.10|0.09|0.19|0.16|0.08|0.08|
|Number of observations|122|122|122|122|122|122|122|122|113|113|

t-statistics corrected for clustering by country are reported in parentheses. ** p<0.05, * p<0.1 19