rafmacalaba/datause-encoder-data huggingface dataset:
# **_b) Energy Sector_**

# **_Thresholds used for the average cost-recovery price in the power sector_**

Where data are not readily available on the Average Cost Recovery Price in the power sector the following thresholds could be applied. Thresholds for the Average Cost Recovery Price in the gas sector have not been provided due to the wide variation in this parameter across countries in ECA.

||**Residential customers**|**Industrial customers**|
|---|---|---|
|<US$0.04/kWh|Tariff<br>_insufficient_to cover basic<br>operating and maintenance costs|Tariff<br>_insufficient_<br>to<br>cover<br>basic<br>operating and maintenance costs.|
|>US$0.05/kWh||Tariffs likely to be making a significant<br>contribution towards capital costs, in<br>most types of systems.|
|>US$0.08/kWh|Tariffs likely to be making a significant<br>contribution towards capital costs, in<br>most types of systems.||

Source: Foster and Yepes, 2005

# **_Normative Loss Rates_**

In the event that country specific data are unavailable for the normative loss rate “ln”, we would suggest the use of 0.1 (electricity) and 0.02 (gas).

17
JMAICA: PARISHES, REGIUNAL ADMlNISTRATIVE AJUS AND DISTRIBUTION OF HOSPITALS <!-- Start of picture text --> 0 5 $4.<br>USw<br>3 3<br><!-- End of picture text -->

# Parishes and Project A4ministrative ltegions

West South North East 10 - Uaestnoreland 11 - St. Elizabeth 6. St. Aim 9 - Hanover 12 - Manchester 5. St. Mary 8 - St. James 13 - Clarendon 4- Portland 7 - Trelawny South East Hospitals 14- St. Catherine A Tertia 1/2 Kingston **and** St. Andrew A 3 - St. Thoms 3 _ Paed. Cb, N,GS C- Hidwifery, GM, GS
**-** **<u>Table 5. Exports of fresh, chilled and frozen fish fillets by destination, 1994 1996 (tonnes):</u>**

|**Country**|**1994**|**1995**|**1996**|
|---|---|---|---|
|Israel|3771|3034|3431|
|Netherlands|3389|2453|4485|
|Singapore|2300|530|1029|
|Greece|1950|975|1105|
|Spain|1174|568|1035|
|Germany|1041|956|1510|
|Australia|854|181|221|
|Italy|443|339|17|
|France|420|385|439|
|Belgium|314|576|274|
|HongKong|271|84|61|
|UnitedKingdom|248|858|573|
|USA|241|802|261|
|Portugal|123|613|597|
|Japan|44|724|674|
|Other|497|817|265|
|**TOTAL**|**13893**|**15974**|**17079**|

_Source: CBS (2002)._ Historically, the EU has also been the major market for Kenyan exports of frozen fish fillets, accounting for around 60 percent of exports in 1996. Close proximity and well-established connection, translating into lower sea freight costs, combined with higher prices for Nile perch have made EU countries an attractive market. However, the export market base is far more diverse than for fresh and chilled fillets. Indeed, there is much greater flexibility to divert exports between alternative markets.

# **6. HYGIENE STANDARDS AND RESTRICTIONS ON EXPORTS TO THE EUROPEAN UNION**

It is evident that a number of countries, including well-established exporters of fish and fishery products, have struggled to comply with the EU’s standards on hygiene in the processing, transport and marketing of fish. Thus, whilst the number of countries achieving Part I status increased from 27 in April 1997 to 83 in December 2003, 26 remain in Part II and face the possibility of being excluded from EU markets unless they achieve compliance by the end of 2005. More positively, however, more than half of the Part I countries are low or lower middleincome, including a number of low-income African countries (Figures 15 and 16).

37
# 3 Data

We employ a balanced dataset, from COMTRADE, covering the trade of 42 countries at the SITC rev. 2 3-digit level of manufacturing industries from 1986 to 2001. The sample consists of 28 OECD countries and 14 developing countries that are the largest manufacturing exporters outside the OECD (and have complete sectoral import data).<sup>17</sup> Inevitably, there exist some null observations since not all countries trade in each industry every year, and so it is appropriate to use the Tobit model for estimation.

We identify the industries, countries and the time period that are a¤ected by each directive or MRA drawing upon the relevant o¢ cial documents, and then construct the variables displayed in Table 1. The harmonization directives and MRAs are not directly related to speci...c products but to product attributes.<sup>18</sup> Thus a single industry (e.g. electronic products) may be a¤ected by multiple directives (e.g. those pertaining, respectively, to low voltage equipment and electromagnetic compatibility). Di¤erent approaches can be taken to quantify the harmonization measures, depending on how these measures are believed to a¤ect trade. It is simplest, and seems reasonable, to assume that the impact is linearly related to the number of directives in each industry, i.e. each additional directive in any industry has an identical incremental a¤ect on trade. Alternative assumptions are possible, e.g. that additional directives have a diminishing e¤ect on trade, or that the nature of the impact di¤ers in each industry. We established that the results are not sensitive to the choice of approach.

HARijrt counts the number of directives applicable to industry r between exporter i and importer j in year t. HAR_Mijrt counts the number of directives applicable to industry r between importer j and any country other than exporter i in year t. For instance, HAR_Mijrt = 1 for imports of United Kingdom from China in the sector of machinery tools since 1995, because the United Kingdom has implemented a single directive applicable to machinery tools along with other EU members at the end of 1994. HAR_Eijrt measures the number of directives implemented in industry r between exporter i and any country other than importer j in year t.

> 17 Czech Republic and Slovak Republic are excluded because of the lack of sectoral trade data in 1993 while Belgium and Luxembourg are considered as one unit throughout the period.

> 18 The lists of EU directives and MRAs are provided Table A.1 and Table A.2 in Appendix A.

15
cases, our results indicate that the impact of Fonden is not permanent, and that its dynamics can be characterized by a three phase process: setup for reconstruction, reconstruction, and catch up by municipalities not covered by Fonden.

# **5.3 The dynamic impact of Fonden on construction employment**

Next we turn to quarterly employment data. These data allow us to provide direct evidence of the impact of Fonden on local economic activity, as well as to document the time path of reconstruction work. While we find no evidence of an overall effect on employment, see figure 7, we do find that Fonden has a considerable impact on construction employment. Specifically, figures 8a to 8f plot the mean growth in construction employment, at different points in the post disaster period, relative to rainfall millimeters (mm) to the threshold. Specifically, the six outcome variables are the log difference in construction employment between the quarter the disaster takes place and the next six quarters, in one quarter steps.

In spite of the coverage of the LFS only allowing us to use half of the previous sample, we find that the time path of growth in construction employment is consistent with our previous findings. In particular, in the very short run, we find no evidence of differential growth in employment at threshold. However, at 6 months or more, we begin to observe a differential effect at the threshold. This effect appears to peak, in a very clear jump of roughly 25% 12 months after a disaster has taken place.

As in the previous case, we provide a more detailed account of the dynamic impact of Fonden on employment growth by sequentially estimating equations 1 to 3 for each of the six quarters following a disaster (3 to 18 months). Each plotted coefficient corresponds to a separate OLS regression. In all regressions, it is assumed that function _g_ ( _·_ ) is linear, and the sample is restricted to the set of observations that fall within the average optimal bandwidth.

Figure 9a plots the first stage coefficients. These coefficients provide strong evidence of a first stage among the municipalities that make up this smaller sample. Specifically, we find that crossing the threshold leads to an increase in the probability of receiving Fonden of 35%. Next figure 9b plots the reduced form coefficients, and figure 9c plots the 2SLS coefficients. As expected given the smaller sample size these coefficient are noisly estimated. Nonetheless, the estimated effect sizes are of considerable economic magnitude. Moreover, the path of the coefficients strongly indicate that 6 months after a disaster, Fonden leads to a large and sustained increase in the growth of employment in the construction sector. At its peak the point estimates indicate, that Fonden increased construction employment by as much as 76%. This effect size is reasonable because municipalities are likely to operate as local labor markets,<sup>10</sup> because

> 10Less than 3% of LFS respondents report moving to a different municipality in order to find or keep their current job.

14
cannot be explained by the conventional indicators. In this model, a one-percentage point increase in suffering increases the number of nonviolent conflict events by 3.2%. For comparison, this effect is slightly larger than the effect of a one-percentage point decrease in GDP growth, which increases nonviolent uprisings by 2.8%.

_Table 2: Baseline results: OLS regression with the number of anti-government demonstrations and strikes as the dependent variable_<sup>_10_</sup>

||(1)|(2)|(3)|
|---|---|---|---|
|Suffering (%)||<br>0.035***|<br>0.032***|
|||(0.009)|(0.009)|
|Struggling (%)||0.018*|0.017*|
|||(0.008)|(0.007)|
|GDP (ln)|-0.364||-0.078|
||(0.333)||(0.306)|
|GDP Growth (%)|-0.034**||-0.028**|
||(0.011)||(0.010)|
|Inflation (%)|0.001||-0.005|
||(0.009)||(0.009)|
|Oil Rents (% of GDP)|0.025<sup>+</sup>||0.029*|
||(0.013)||(0.013)|
|Infant Mortality|0.004||0.006|
||(0.023)||(0.022)|
|Autocracy|0.034||0.046|
||(0.071)||(0.069)|
|Corruption|-0.012||-0.009|
||(0.010)||(0.009)|
|Political Terror Scale|0.136||0.154<sup>ꝉ</sup>|
||(0.084)||(0.080)|
|Mobile phones|0.000||0.000|
||(0.003)||(0.003)|
|Urban population (% of total)|0.035||0.036|
||(0.044)||(0.043)|
|Constant|2.406|-1.129*|-2.558|
||(4.628)|(0.561)|(4.484)|
|Country FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|
|Observations|862|862|862|
|Number of Countries|118|118|118|
|R<sup>2</sup>overall<br>|0.003|0.096|0.068|
|R<sup>2</sup>within<br>|0.258|0.260|0.285|
|R<sup>2</sup>between|0.009|0.017|0.042|

The number of anti-government demonstrations and strikes is transformed using the inverse hyperbolic sine transformation. Robust standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05,<sup>+</sup> p<0.1

> 10 We also estimated regression models for strikes and demonstrations separately and the results of the two separate models are similar to each other and the results reported here.

20
**Table 6: Inter-rater reliability (Percent of raters scoring within 1 or 2 points of each other)**

|**Le**|**vel 1**||**Level 2**|||
|---|---|---|---|---|---|
||Within 1<br>point|Within 2<br>points||Within 1<br>point|Within 2<br>points|
|**CLASS (7-point scale)**||||||
|Emotional support|43.1|81.8|Positive Climate|45.5|88.6|
|Classroom organisation|95.5|100|Teacher Sensitivity|47.7|77.3|
|Instructional support|45.5|84.1|Regard for Adolescent Perspective|79.5|95.5|
|Overall CLASS score|56.8|97.7|Behavior Management|79.5|93.2|
|Student engagement|61.4|90.1|Teacher Productivity|63.6|75|
||||Negative Climate|97.7|100|
||||Instructional Learning Format|56.8|84.1|
||||Content Understanding|65.9|84.1|
||||Analysis and Inquiry|54.5|81.8|
||||Quality of Feedback|65.9|86.4|
||||Instructional Dialogue|59.1|84.1|
||||Student Engagement|61.4|90.1|
|**Teach (5-point scale)**||||||
|Classroom culture|100|100|Supportive Learning Environment|96.4|100|
|Instruction|98.2|100|Positive Behavioral Expectations|100|100|
|Socioemotional skills|100|100|Lesson facilitation|98.2|100|
|Overall Teach score|100|100|Checks for understanding|96.4|100|
||||Feedback|96.4|100|
||||Critical Thinking|96.4|100|
||||Autonomy|100|100|
||||Perseverance|100|100|
||||Social and collaborative skills|100|100|

44
Policy Research Working Paper 6015

# **Abstract**

The paper examines the link between poverty, the middle class and institutional outcomes using a new crosscountry panel dataset on the distribution of income and expenditure. It uses an econometric methodology to gauge whether a larger middle class has a causal effect on policy and institutional outcomes in three areas: social policy in health and education, marketoriented economic structure and quality of governance. The analysis finds that when the middle class becomes larger (measured as the proportion of people earning more than US$10 a day), social policy on health and education becomes more progressive, and the quality of governance (democratic participation and official corruption) also improves. This trend does not occur at the expense of economic freedom, as a larger middle class also leads to more market-oriented economic policy on trade and finance. These beneficial effects of a larger middle class appear to be more robust than the impact of lower poverty, lower inequality or higher gross domestic product per capita. That may be linked to the evolution of the middle class: they are more enlightened, more likely to take political actions and have a stronger voice. They also share preferences and values for policy and institutional reforms, as well as higher stakes in property rights and wealth accumulation.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region; and Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jrigolini@worldbank. org and nloayza@worldbank.org, respectively.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|_Any antibiotic available_|0.92|0.81|0.85|
|---|---|---|---|
|_All antibiotics available_|0.07|0.04|0.22|
|**Rehydration**||||
|_ORS packet available_|0.8|0.77|0.87|
|_Sodium chloride inj. available_|0.45|0.68|0.7|
|_Has ORS and sodium chloride available_|0.39|0.58|0.62|
|_Neither ORS nor NaCl available_|0.14|0.13|0.04|

±Breakdowns by cadre and education were unavailable for facilities with over 50 medical staff in Tanzania so the counts of doctors, clinical officers, nurses, and other (and counts of staff with different levels of education) are only available for facilities with 50 or fewer medical staff. However, the counts of total medical staff include these larger facilities (Tanzania has 35 facilities with more than 50 medical staff, which increase the averages of total medical staff from 9.37 to the 12.47 reported in the table above).

> _++_ Clinical Officers were not a medical staff cadre in Sierra Leone

δ Provider education was missing for 1.1% of the sample resulting in the sums of the categories of education equaling less than the total number of medical staff

*Acceptable waste disposal methods include: Open burning in pit or protected ground, placed in covered pit or pit latrine, placed in pit or protected ground, off-site removal (with protected storage) and one or two chamber incinerators.

> _+_ Other arrangements for emergency transport include other official vehicles, private vehicles or another mode of emergency transport.

26
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.93*** 0.93*** 0.92***<br>H<br>Kendall tau-b 0.83*** 0.83*** 0.78***<br>Spearman 1*** 0.87*** 0.88***<br>A<br>Kendall tau-b 1*** 0.72*** 0.7**<br>Spearman 0.97*** 0.98*** 0.92***<br>M0<br>Kendall tau-b 0.89*** 0.94*** 0.78***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>B. Sensitivity Analysis: Changes in weights.<br>B1. Change in the weights assigned to each dimension. Baseline scenario: Evenly distributed weights across all indicators<br>(25% each). Alternative scenario: Skewed weighting scheme in favor of the Social Inclusion dimension (40% for Social<br>Inclusion, and 20% for the rest of dimensions).<br>Peru<br>B1.1. FOSD: Counting Vector B1.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>B1.3. SOSD: Social Sustainability Index B1.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.93*** 0.96*** 0.77***<br>H<br>Kendall tau-b 0.79*** 0.84*** 0.57***<br>Spearman 0.95*** 0.88*** 0.65***<br>A<br>Kendall tau-b 0.84*** 0.75*** 0.52***<br>Spearman 0.96*** 0.95*** 0.77***<br>M0<br>Kendall tau-b 0.85*** 0.83*** 0.56***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.3<br>1<br>.8 .2<br>.6<br>.1<br>CCDF<br>.4 Difference<br>.2 0<br>0 -.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _B1. Change in the weights assigned to each dimension. Baseline scenario: Evenly distributed weights across all indicators (25% each). Alternative scenario: Skewed weighting scheme in favor of the Social Inclusion dimension (40% for Social Inclusion, and 20% for the rest of dimensions)._ South Africa B1.1. FOSD: Counting Vector B1.2. Dominance Analysis 41
Figure 2: RCA against Bank Private Credit to GDP (Df1), 2005 <!-- Start of picture text --> Basic & Fabricated Metals (low SII) Food, Beverages & Tobacco (high SII)<br>IRL DNK<br>SVK<br>SVN BRA<br>NLD<br>JPN<br>CZE<br>BRA KOR<br>ITA IDN<br>CYP<br>TUR FIN AUTCHNDEU ESP LTU BEL AUS<br>RUS<br>ROM SWE<br>POL<br>POLBGR FRA<br>BEL<br>AUS EST<br>MEX CAN LVA<br>FRAMLT ESPPRT<br>IND USA TUR CZE AUT<br>HUN LUX PRT GBR ROMSVKBGR ITA CHNCAN GBR<br>GRC NLDDNK GRC DEU<br>LVA HUN<br>EST MEX SVN FIN SWELUX<br>IND USA<br>IDN CYP<br>LTU KOR JPN<br>MLT IRL<br>RUS<br>0 .5 1 1.5 0 .5 1 1.5<br>(Bank Private Credit)/GDP (Bank Private Credit)/GDP<br>2 3<br>1.5<br>2<br>1<br>RCA RCA<br>1<br>.5<br>0 0<br><!-- End of picture text --> Notes: The vertical axis represents the RCA based on DVA in gross exports for Basic & Fabricated Metals sector and Food, Beverages & Tobacco sector, respectively. The horizontal axis represents a measure of financial development – the ratio of bank private credit to GDP. The data for bank private credit are from the World Bank Global Financial Development Database (GFDD). Only the data for year 2005 are used in this diagram.

30
# **A.1 Real GNI**

For all countries, we deflate Gross National Income (GNI) (source: WDI) by the all-item consumer price index (CPI) for the baseyear 2000 (source: WDI) except for Serbia and Zimbabwe. For these two countries, we use GDP deflator (source: WDI) for the baseyear 2000.

# **A.2 Real GNI per capita**

For all countries, we deflate GNI per capita (source: WDI) with the CPI except for Serbia and Zimbabwe. For these countries, we use GDP deflator (source: WDI).

# **A.3 Trade facilitation Indicators**

# A.3.1 Port efficiency

The Port efficiency for each country is the average of two indexed inputs from GCR: ■ Port Infrastructure Quality (Port facilities and inland waterways in your country are: 1 = underdeveloped; 7 = as developed as the world's best) ■ Air Transport Infrastructure Quality (Air Transport in your country is: 1 = infrequent and inefficient; 7 = as extensive and efficient as the world's best)

# A.3.2 Customs environment

The customs environment for each country is the average of two indexed inputs from GCR: ■ Irregular Payments in Imports and Exports (How common do firms in your industry give irregular extra payments or bribes connected with import and export permits: 1 = common; 7 = never) ■ Business Cost of Corruption (Do unfair or corrupt activities of other firms impose costs on your firm? 1 = impose large costs; 7 = impose no costs/not relevant)

# A.3.3 Regulatory environment

The Regulatory environment for each country is constructed as the average of three indexed input from GCR: ■ Irregular Payment in Public Contracts (How commonly do firms in your industry give irregular extra payments or bribes connected with public contracts/investment projects: 1 = common; 7 = never) ■ Favoritism in Decisions of Government Officials (When deciding upon policies and contracts, government officials: 1 = usually favor well-connected firms and individuals; 7 = are neutral among firms and individuals)
The literature has thus exploded with a plethora of reviews and surveys of the history of East and Southeast Asian migration and migration policy.<sup>3</sup> These studies delineate a region that has a long history and culture of migration, predating the colonial era. At the same time, they describe a region whose countries have a great deal of heterogeneity in the levels of formality and structure in migration management policies.

Net migration – i.e. the difference between migrants entering a country and migrants leaving a country – within the EAP region has generally increased over time (Table 2). Korea is the most notable exception, with a general fall in migration rates; and Malaysia’s migration has been variable in the past with a large increase in the 1960s, followed by a small decrease in the 70s; most recently migration has steadied at a healthy 4-5%. Migration to Indonesia has steadily declined over the entire period, as migrants from other countries have left Indonesia and returned to their countries of origin.

**Table 2: Percentage Change in Net Migration* per Annum**

||**1960-1970**|**1970-1980**|**1980-1990**|**1990-2000**|**1960-2000**|**1980-2000**|
|---|---|---|---|---|---|---|
|**China**|1.3|-15.1|5.2|9.0|-0.3|7.1|
|**Hong Kong**|0.5|1.9|0.7|1.9|1.2|1.3|
|**SAR, China**|||||||
|**Indonesia**|-4.5|-4.5|-4.5|-10.7|-6.1|-7.7|
|**Japan**|0.5|1.0|2.9|4.6|2.2|3.8|
|**Malaysia**|29.2|-0.9|3.5|4.7|8.5|4.1|
|**Philippines**|-0.1|-5.6|1.1|9.0|1.0|5.0|
|**Singapore**|0.2|-0.1|3.3|6.4|2.4|4.8|
|**South Korea**|2.9|11.4|0.8|-0.1|3.6|0.4|
|**Thailand**|-3.3|-2.4|0.5|9.1|0.9|4.7|
|**Vietnam**|1.0|1.0|4.1|18.7|6.0|11.2|

Source: Özden et al. (2011) Note: *Net migration refers to migration into a country minus migration out of a country.

Athukorala (2006) reviews migrant labor policies in East Asia and points out that most countries in East Asia have historically lacked well-defined migrant labor management programs. An exception is Singapore. It has had a long-standing and well-defined migrant labor policy, applying industry-specific quotas and levies on employers for hiring low-skill migrant workers, and has been able to leverage its migrant labor force to make substantial contributions

> 3 Please see Debrah (2002), Ananta and Arifin (2004), Massey and Taylor (2004), and more recently, the Asian Development Bank (2008)

8