rafmacalaba/datause-encoder-data huggingface dataset:
understanding both the influencing factors (institutional and economic) of the structure of decentralization for the road sector and for evaluating empirically the impact of decentralization on road service delivery. The study is also interesting in that it makes a distinction between the three types of road activities mentioned earlier and the desirability of decentralizing their provision.

# **_Outline of the Paper_**

The paper is organized as follows. Section two introduces the general and extended theoretical frameworks of the study. Section three provides a framework for the analysis of the optimal structure of road service provision. Section four presents the data set that will be used for the analysis. Section five studies the relationship between decentralization and road service delivery. It discusses the impact of decentralization on road performance for various activities. Further, it looks at the dynamics of decentralization for eight countries that have varying degrees of fiscal and functional decentralization for road works. This provides an understanding of the forces that derive decentralization and the potential tradeoffs that governments can make to reach optimal road service levels. The sixth section is a longitudinal analysis of the road provision experience of Korea. Korea has been selected because of its rich experience with decentralization in the road sector. Section seven presents the findings from a case study conducted for Germany. The analysis in this section is different in that it develops both vertical and horizontal measures of decentralization. Finally, based on the analysis conducted in sections three to seven, section eighth will attempt to develop guidelines for optimal provision of road services for various road activities.

# **2. Theoretical Framework**

# **_General_**

Governments have complex structures both horizontally in terms of numerous agents and vertically in terms of different levels of government. In many developing countries, the trend in recent years has been towards increasing decentralization in the direction of granting more authority to lower levels of government. Most interestingly, new levels of government have emerged to address the difficulties faced by central governments. The question of which level of government is the appropriate or desirable provider of local public services has received some attention in the literature on decentralization and local finance. This section reviews that branch of the literature that provides some useful insights into the determination of proper levels of government for provision of local goods.

# **_Public Finance Treatment of Decentralization_**

The fiscal federalism literature has developed very useful guidelines for the assignment of fiscal functions to various levels of government. Musgrave in the Theory 4
LAO PEOPLE DEN. REP.

- 2. Projections NRR-1 by 2035

|**_1wr_**|**Pqutitlan**|**IQP.**<br>ratio|Paiod|_ErmAth_<br>rate|CU|CDR|TFU|0<br>Nal_.|Oq<br>F_ls|In|zINO|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|4186|89.7|1990-94<br>|2.87<br>|43.5<br>|14.7<br>|6.18<br>|50.1<br>|53.1<br>|97<br>|159<br>|
|1995|4831<br>|93.1<br>|1995 -99<br>|2.71<br>|39.6<br>|12.5<br>|5.67<br>|53.1<br>|56.3<br>|84<br>|135<br>|
|2000|5532<br>|92.3<br>|2000-04<br>|2.61<br>|36.8<br>|10.7<br>|5.16<br>|56.1<br>|59.4<br>|72<br>|112<br>|
|2005|6304|85.1<br>|2005-09<br>|2.50<br>|34.8<br>|9.7<br>|4.65<br>|57.8<br>|61.2<br>|65<br>|100<br>|
|2010|7144<br>|79.3<br>|2010-14<br>|2.37<br>|32.6<br>|8.9<br>|4.14<br>|59.5<br>|63.2<br>|59<br>|**88**<br>|
|2015|8044<br>|74.7<br>|2014-19<br>|2.16<br>|29.7<br>|8.0<br>|3.63<br>|61.3<br>|65.2<br>|53<br>|75<br>|
|2020|8963|70.0<br>|2020-24<br>|1.88<br>|26.1<br>|7.2<br>|3.11<br>|63.1<br>|67.3<br>|46<br>|63<br>|
|2025|9849<br>|64.1<br>|2025-49<br>|1.30<br>|19.2<br>|6.3<br>|2.26<br>|67.7<br>|72.5<br>|29<br>|36<br>|
|2050|13641|45.4<br>|2050-74<br>|0.73<br>|14.7<br>|7.5<br>|2.09<br>|73.3<br>|78.7<br>|11<br>|13<br>|
|2075|16364<br>|54.2<br>|2075-99<br>|0.31<br>|13.0<br>|9.9<br>|2.07<br>|77.2<br>|82.9<br>|6<br>|7<br>|
|2100<br>|1W678<br>|66.1<br>|2100-24<br>|0.14<br>|12.3<br>|10.9<br>|2.06<br>|60.2<br>|86.3<br>|3<br>|4<br>|
|2125<br>2150|18294<br>18602|70.0<br>72.0|2125-49|0.07|12.0|11.3|2.06|81.2|87.7|2|4|

## 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70-74<br>65-69<br>E § 5~~~60-64 {<br>55.59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Country with Region and<sup>Income Grour:, 1985-90</sup>

TFR **eo** Growth rate <!-- Start of picture text --> Country Region Income group~<br><!-- End of picture text --> <!-- Start of picture text --> Coun~try Region Income grou.p<br><!-- End of picture text --> <!-- Start of picture text --> Coun~try Region Income group<br><!-- End of picture text --> 7 1
WPS4193 <mark>P</mark> OST <mark>-C</mark> ONFLICT <mark>T</mark> RANSITIONS <mark>W</mark> ORKING <mark>P</mark> APER <mark>N</mark> O <mark>. 7</mark>

# **Horizontal Inequalities, Political Environment, and Civil Conflict Evidence from 55 Developing Countries, 1986–2003***

### **Gudrun Østby** **

## Technical Abstract

This paper investigates the relationship between socioeconomic horizontal inequalities (inequalities between identity groups), regime type, electoral system, political exclusion of minorities and civil conflict onset. A positive link between horizontal inequalities (HIs) and violent conflict has been established by some preliminary empirical studies, but no systematic large-N study has to date investigated whether and how the political environment can affect this relationship. Using _Demographic and Health Surveys_ from 55 developing countries in the period 1986–2003, I calculate welfare inequalities between ethnic, religious, and regional groups based on indicators such as household assets and educational levels. All the HI measures are positively associated with conflict, but the effects seem to be most robust when using the regional group identifier. The regional HI measures are interacted with terms for regime type; electoral system and political exclusion. The results show that the conflict potential of regional HIs is stronger for democracies and semi-democracies than for autocracies. Institutional arrangements also seem to matter since the positive effect of socioeconomic HIs on civil war increases with the level of inclusiveness of the electoral system. Furthermore, the interaction between political exclusion and socioeconomic regional HIs seems to make countries particularly at risk for conflict. This suggests that what is required to secure peace in developing countries is the combination of _politically_ and _economically_ inclusive government.
# **Appendix A. Additional tables and figures**

**Table A1. List of price surveys in Sub-Saharan African countries**

|No.|Country|Survey|Year|
|---|---|---|---|
|1.|Burkina Faso|EMC2014|2014|
|2.|Central African Republic|ECASEB2008|2008|
|3.|Gabon|EGEPII-2017|2017|
|4.|Ghana|GLSS3, GLSS4, GLSS5, GLSS6, and|1991, 1998, and 2005,|
|||GLSS7|2012/13, and 2016/17|
|5.|Guinea|EIBEP2002, ELEP|2002 and 2012|
|6.|Gambia, The|IHS2015|2015|
|7.|Guinea-Bissau|ILAP2010|2010|
|8.|Kenya|KIHBS2015|2015|
|9.|Liberia|HIES2014 and HIES2016|2014 and 2016|
|10.|Mauritania|EPCV2004. EPCV2008, EPCV2014|2004, 2008, and 2014|
|11.|Niger|ECVMA2014|2014|
|12.|Nigeria|NLSS2003-04, NLSS2008-09|2003/04 and 2008/09|
|13.|Sierra Leone|SLIHS2003|2003|
|14.|Uganda|UNHS2005, UNHS2012|2005 and 2012|
|15.|Zambia|LCMS2010|2010|
|16.|Zimbabwe|PICES2017|2017|

_Source:_ Authors

**Table A2. List of CPI price data in Sub-Saharan African countries**

|No.|Country|Geographic coverage of data collection|
|---|---|---|
|1|Burkina Faso|Urban|
|2|Cameroon|Urban|
|3|Congo, Dem. Rep.|Urban|
|4|Côte d’Ivoire|Urban|
|5|Ethiopia|Urban and rural|
|6|Ghana|Urban and rural|
|7|Madagascar|Urban|
|8|Mauritius|Urban|
|9|Mozambique|Urban|
|10|Nigeria|Urban and rural|
|11|Rwanda|Urban and rural|
|12|Senegal|Urban|
|13|South Africa|Urban and rural|
|14|Tanzania|Urban|
|15|Togo|Urban|
|16|Uganda|Urban|
|17|Zimbabwe|Urban and rural|

_Source:_ Authors’ work building on “Consumer Price Indices,” Laborsta Internet, ILO, Geneva (Dabalen, Gaddis, and Nguyen 2019).

28
**Table 9 Summary of world and regional impacts**

||**World**|**LA**|**MENA**|**SSA**|**EA**|**SA**|
|---|---|---|---|---|---|---|
|**Indicators**|||||||
||||**1m S**<br>|**LR**<br>|||
|**Area**|0.31|0.34|0.25|0.12|0.52|0.29|
|**Population**|1.28|0.57|3.20|0.45|1.97|0.45|
|**GDP**|1.30|0.54|1.49|0.23|2.09|0.55|
|**Urban extent**|1.02|0.61|1.94|0.39|1.71|0.33|
|**Ag. extent**|0.39|0.33|1.15|0.04|0.83|0.11|
|**Wetlands**|1.86|1.35|3.32|1.11|2.67|1.59|
||||**5m S**<br>|**LR**<br>|||
|**Area**|1.21|1.24|0.63|0.48|2.30|1.65|
|**Population**|5.57|2.69|7.49|2.38|8.63|3.02|
|**GDP**|6.05|2.38|3.91|1.42|10.2|2.85|
|**Urban extent**|4.68|3.03|4.94|2.24|8.99|2.72|
|**Ag. extent**|2.10|1.76|3.23|0.38|4.19|1.16|
|**Wetlands**|7.30|6.57|7.09|4.70|9.57|7.94|

LA: Latin America and Caribbean; MENA: Middle East and North Africa; SSA: Sub-Saharan Africa; EA: East Asia; SA: South Asia.

# _(ii) Most impacted countries_

Figures 6a to 6f summarize our results for each indicator by presenting the top 10 impacted countries (as a percentage of their national values). For this purpose, we use the 1m SLR scenario. For land area (Figure 6a), The Bahamas is by far the most impacted country, with close to 12% of its area affected. Around 10% of Vietnam’s and the A.R. of Egypt’s populations would be impacted with a 1m SLR (Figure 6b). Vietnam would also see 10% of its GDP (Figure 6c) and urban extent (Figure 6d) impacted. Mauritania’s GDP would be significantly impacted. The A.R. of Egypt’s agricultural extent would experience the largest percentage impact, reaching approximately 13%. Finally, nearly 28% of Vietnam, Jamaica and Belize’s wetlands would be impacted by a 1m SLR. Among all of the indicators used in this paper, _Vietnam_ ranks among the top 5 most impacted countries, with the A.R. of Egypt, Suriname and The Bahamas consistently ranking among the highest.

40
The above example is not unrealistic. In the last few years the dollar cost of living of Argentina's rain trading partners has significantly risen as a consequence of the U.S. dollar devaluation. However, during these same years, the dollar prices of the commodities Argentina exports and imports have fallen in nominal terms when measured in dollars. For this particular situation, if the objective were to be that of maintaining constant the price of home goods, the argentine currency should have been devalued versus the dollar as recommended by the Real Exchange Rate measure using actual traded goods prices. The Real Exchange Rate using baskets of CPI's would have wrongly recommended revaluation of the currency The experience of Argentina is not different from that of many other countries. The fact is that the sharp movements in the value of the dollar were not accompanied by movements in the opposite direction in dollar prices of internationally traded goods. This is particularly the case for those countries trading mainly in commodities. Between 1983 and 1986, the dollar prices of commodities, measured by the IMF Index of Commodity Prices, has moved in the same direction as the value of the U.S.Dollar, measured by the MIRM Effective Exchange Rate for the Dollar. This fact can be clearly appreciated in Figure 1.

32
**Table 6:** Practices 2015

||(1)<br>Existing<br>Total practices<br>applied|(2)<br>practices<br>Standardized<br>Index|(3)<br>All new<br>Total practices<br>applied|(4)<br>practices<br>Standardized<br>Index|
|---|---|---|---|---|
|Extension (1=Yes)|0.04|0.06|0.35**|0.33**|
||(0.08)|(0.13)|(0.11)|(0.14)|
|Individual recommendation (1=Yes)|0.03|0.05|-0.33**|-0.28*|
||(0.07)|(0.09)|(0.14)|(0.16)|
|Grant (1=Yes)|0.04|0.12|2.21***|1.63***|
||(0.08)|(0.11)|(0.14)|(0.16)|
|Flexible (1=Yes)|-0.01|-0.01|-0.07|-0.05|
||(0.07)|(0.08)|(0.14)|(0.18)|
|Observations|678|678|678|678|
|R-squared|0.07|0.07|0.55|0.34|
|Mean dep. var. control|2.38|0.00|0.32|0.00|
|SD dep. var. control|0.61|1.00|0.69|1.00|
|T1: _βE_+_βI_+_βG_ =0|0.11|0.03|0.00|0.00|
|T2: _βE_+_βG_ =0|0.25|0.09|0.00|0.00|
|T3: _βE_ + _βG_ + _βF_ =0|0.32|0.09|0.00|0.00|

Note: this table reports results on the agricultural practices performed by the farmers in our study in the 2017 season. Using the full sample of 678 farmers, we run the following regression: _Yit_ = _β_ 0 + _βE Extensioni_ + _β I IndRecommi_ + _βG Granti_ + _βF Flexible_ + _αc_ + _εit_ , where _i_ corresponds to a farmer, _Y_ is the outcome of interest and _t_ is the time period. We include randomization strata fixed effects and compute robust standard errors. At the bottom of the table, we report the mean and the standard deviation of the outcome for the control group, the omitted category in our regression. We also report p-values of linear combinations of the estimates coefficients that map into the original study design. We use data from the Follow-up survey conducted in August 2015. The dependent variable in column 1 is a sum of individual dummies. Each dummy takes value of 1 if the farmer performed one of the so-called existing agricultural practices. In column 2, the dependent variable is the standardized index of the outcome in column 1, computed by standardizing each dummy individually, adding them all and standardizing the sum. We use the mean and standard deviation of the control group as reference for the standardized index. In columns 3 and 4, the dependent variables are analogous to the outcomes in columns 1 and 2, computed for the so-called new practices. The existing practices are: 1.Ploughing, 2. Using inorganic fertilizer and 3. Covering the fertilizer. The new practices are: 1.Ripping, 2.Using hybrid seeds, 3.Fertilizing at sowing, 4.Sowing with precision machinery, 5.Using YARA’s fertilizers, and 6. Using preemergent herbicide Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

36
Table A.12. Average effect of energy policy packages, by the second-stage lag; replacing shares of “Renewable energy consumption” and “Renewable electricity output” with shares of “Modern Energy Generation over Total Generation” and “Modern Energy Capacity over Total Installed Capacity” to exclude large hydropower

|**Energy policy package **|**3**|**5**|**7**|
|---|---|---|---|
|**Legal Framework**|0.86|0.93|0.43|
|**Planning for expansion**|-2.13|-1.49|2.88|
|**Incentives and regulatory support**|-0.18|0.84|-1.51|
|**Attributes of financial and regulatory incentives**|0.79|4.07|9.18|
|**Network connection and use**|1.70|2.19|1.35|
|**Counterparty Risk**|2.53|1.66|1.11|
|**Carbonpricing and monitoring**|0.09|||

_Notes: Regression specification: RISE index, UNGA affinity IV with five years moving average._ Table A.13. Average effect of all energy policy packages together on two absolute energy outcomes, by the second-stage lag

|**Energy outcome**|**3**|**5**|**7**|
|---|---|---|---|
|**Absolute modern renewable capacity**|0.21|0.34|0.49|
|**Absolute modern renewable generation**|0.55|1.07|1.48|

_Notes: Regression specification: RISE index, UNGA affinity IV with five years moving average._

# APPENDIX B: DENDROGRAM OF POLICY DATASET

Dendrograms are widely used to find homogeneous groups in observations, or in our case, policy instruments, that differ from each other. They can help the researcher to identify the structure of the data or to group variables based on their similarity. The results of the dendrogram lead us to group policies within the Headings that were provided to us by ESMAP.

To depict how the policies relate to one another, we first create a dissimilarity (1similarity) matrix based on the Jaccard coefficient. In brief, the Jaccard coefficient ( <mark>Eq. B.1)</mark> is 66
**<u>Table 1: Thresholds used for evaluating cost recovery of water tariffs</u>**

||Developing countries|Industrialized countries|
|---|---|---|
|<US$0.20/m|Tariff_insufficient_to cover basic operating<br>and maintenance costs|Tariff<br>_insufficient_<br>to<br>cover<br>basic<br>operating and maintenance costs.|
|US$0.20-0.40/m<sup>3</sup>|Tariff_sufficient_to cover operating and<br>some maintenance costs|Tariff<br>_insufficient_<br>to<br>cover<br>basic<br>operating and maintenance costs.|
|US$0.40-1.00/m<sup>3</sup>|Tariff_sufficient_to cover operating,<br>maintenance and most investment needs|Tariff_sufficient_to cover operating and<br>maintenance costs|
|>US$1.00/m<sup>3</sup>|Tariff_sufficient_to cover operating,<br>maintenance and most investment needs<br>in the face of extreme supply shortages|Tariff_sufficient_to cover full cost of<br>modern water systems in most high-<br>income cities|

Source: GWI, 2004 Based on this framework, it is possible to assess the progress actually made towards cost recovery over this period by expressing tariffs in US dollar terms and comparing them against these benchmarks (Table 2)<sup>1</sup> . The average real residential water tariff in the 13 Latin American cities considered rose from US$0.47 to US$0.51 per cubic meter. However, this result is driven by some particularly large tariff increases in Colombia (recall Box 1), so that the median tariff actually fell from US$0.51 to US$0.38 per cubic meter. As a result, the percentage of utilities within the indicative range for some degree of capital cost recovery fell from 62% to 46%. However, the number of utilities whose tariffs do not even cover operation and maintenance expenditures fell from 8% of the sample to 0%. The average real industrial tariff fell substantially from US$1.91 to US$1.11 over the period 1997/03. Nevertheless, industrial tariffs in 1997 were so far above cost recovery levels that even a decline of this magnitude did not bring them below the threshold for full capital cost recovery in any of the utilities considered.

> 1 The real tariff trends reported in LCU in Figure 1 do not necessarily coincide with the real tariff trends reported in US$ terms in Table 2. The differences are due to the fact that some countries experienced a depreciation of the real exchange rate against the US$, while others experienced an appreciation of the real exchange rate against the US$ over the same period. A more extensive explanation of this issue is provided in Appendix 1.

6
for India. Sections 9 and 10 discuss the prospects for adoption of improved stoves and cleaner fuels, and Section 11 provides a summary and conclusions.

# **2.  Pollution Factors, Sampling Strategy and Data Description**

Previous studies have identified several potential determinants of exposure to indoor air pollution: fuel type, time spent in cooking, structural characteristics of houses, and household ventilation practices (opening of windows and doors, etc.) (World Bank, 2002, Brauer and Saxena, 2002, Moschandreas et al, 2002, Freeman and Sanez de Tajeda, 2002). All of these factors may be important in Bangladeshi households, which exhibit significant diversity in cooking fuels, stove types, cooking locations, and quality of ventilation.

In Bangladesh, middle- and upper-income households in urban areas typically use electricity or relatively clean cooking fuels such as natural gas. However, households in peri-urban and rural areas rely primarily on biomass fuels. These include wood, twigs and leaves, animal dung, and agriculture residues such as straw, rice husks, bagasse, and jute sticks. Seasonal and economic factors may dictate the use of different biomass fuels over the annual cycle.

Given the level of emissions from fuel use, the particulate concentration in a space depends on the length of time the emitted particles remain, as well as the ambient (outdoor) concentration. The extent and duration of smoke in the kitchen, and the amount of smoke leaking from the kitchen into the outdoors or other living spaces, may depend on several structural factors: the location of the kitchen, the extent of ventilation, and the porous nature of materials used to construct the roof and walls of the kitchen.

6
**Figure 7**

# **U.S. Soda Ash Balance**

<!-- Start of picture text --> 12,000,000<br>10,000,000<br>8,000,000<br>Metric Tons<br>Primary production<br>6,000,000 Exports<br>Consumption<br>4,000,000<br>2,000,000<br>0<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Year<br><!-- End of picture text --> Source: USGS Historical Statistics for Mineral and Material Commodities in the United States 2003.

The water in Mexico is relatively hard, making phosphates a desirable ingredient to local consumers. According to the information obtained during firm interviews, most detergents produced in Mexico are phosphate-based and can only be legally exported to those U.S. states that have not enacted prohibitions. Switching production from phosphate-based to phosphate-free detergents and vice versa cannot be done without significant costs and producing both types of detergents at the same facility is not deemed to be commercially viable.

25
regulations. In contrast, the importance of country characteristics for entry and exit suggests that fixed costs and uncertainty vary more at the country level than at the sector level.

**Table 7: ANOVA Decomposition at Country and HS 2-digit Sector Level**

||**Total Exports**|<sup>**Number of**</sup><br>**Exporters**|**Mean**<br>**Exports per**<br>**Exporter**<br>**(mn USD)**|<br>**Median**<br>**Exports per**<br>**Exporter**<br>**(mn USD)**|<br>**Share of Top**<br>**5% Exporters**|**Number of**<br>**Products per**<br>**Exporter**|<br>**Number of**<br>**Destinations**<br>**per Exporter**|**Entry**<br>**Rate**|<br>**Exit**<br>**Rate**|<br>**Survival**<br>**Rate**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|10%|24%|3%|2%|28%|14%|37%|30%|29%|14%|
|**HS 2-digit Sector**|9%|18%|7%|5%|24%|46%|12%|17%|17%|16%|
|**Residual**|81%|58%|90%|93%|54%|41%|51%|55%|55%|70%|

Note: The variance decomposition shown is based on measures in the country-year-product (HS 2-digit) level dataset (file CYH2.dta) averaged across the 2006-2008 period for each country except Kuwait for which averages are across the 2009-2010 period and Portugal for which averages are across the 2003-2005 period.

# **_Stylized Fact 5: There is a tremendous skewness in exporters as total exports of developing_**

# **_countries are largely dominated by multi-product multi-destinations exporters but these account for a very small share of the number of exporters._**

The recent trade literature shows a dominant role for firms that export many products to many destinations in explaining trade flows (see, for example, Bernard, Jensen, Redding, and Schott, 2009 on the US; Eaton, Kortum and Kramarz, 2008 on France; and Amador and Opromolla, 2008 on Portugal). As mentioned in Section 3, a component of the Database is a set of matrix tables showing for each developing country and year the distribution of exporters and of total exports by the number of HS 6-digit products exported and the number of destination markets served that can provide an insight onto the importance of multi-product multidestination exporters across developing countries and over time.

Using the information from developing countries over the period 2006-2008, we calculate for each country the average share of exporters and of total exports accounted for by singleproduct single-destination firms and by firms exporting to more than four countries and to more than four destinations and show them in Table 8. Single-product single-destination firms 31