rafmacalaba/datause-encoder-data huggingface dataset:
16 focus on uncontrolled vehicles, and vehicles without catalysts, versus all other vehicles. For countries without unleaded fuel, new vehicles are probably being produced or imported vith engine modifications to reduce emissions but without catalysts. In this case, substantial reductions in emissions from pre-controlled levels may be already realized.

The foregoing also shows that cold start emissions are quite important, representing 50% of HC exhaust emissions for a 5-mile trip and 40% for a 10-mile trip. Thus, chaining two trips together reduces HCs significantly through the elimination of a cold start, even with total miles driver kept constant. Increases in average vehicle speed appear to be of lesser importance to emissions reductions (but, of course, cost-effectiveness is the ultimate test of importance),9 although expressing speeds in average terms ignores how the car is being driven, i.e., at a steady speed or with much stopping and starting. The latter are likely to produce much higher emissions than the former, as both acceleration and deceleration tax the emissions control system.

Speed effects when considering ozone concentrations are unclear, as NOX tends to increase with speed while HC decreases. The relationships between Mi) emissions and speed and (ii) emissions and volatility are virtually identical across model years and for vehicles of different ages. Thus, these interaction effects can be ignored.

Because of the relatively large proportion of diesel vehicles in developing countries, it is important to compare diesel emissions to those of gasoline vehicles, although no definitive conclusions can be reached. This cannot be done comprehensively using MOBILE4. Table 3 provides data

> 9. Note now, as we do later, that large benefits in time savings are to be had from increasing speeds.
# **MENA Export Performance and Specialization—The Role of Financial Sector Development and Governance**

Christina A. Wood Judy Yang The World Bank<sup>1</sup> _JEL Classification:_ F1, G0, K2, O14, O16 _Key words_ : Exports, Financial development, Governance, External finance, Asset tangibility.

> 1 This analysis was commissioned as background paper for the Exporters in MENA regional study titled “Champions Wanted: Promoting Exports in the Middle East and North Africa” (published October 2015) supported by the MENA Chief Economist’s Office at the World Bank. We thank participants at the Exporters in MENA Workshop, held at the World Bank, for comments. This paper has benefited immensely from comments and input by Olivier Cadot, Ana Margarida Fernandes, Melise Jaud, Bob Rjikers and Murat Seker. We thank them for their time, comments, and for providing data. All errors are our own.
These hypothetical distributions provide the answer to the question of what the distribution of COVID-19 deaths would look like if each country had the mean high-income population age structure but its own observed age-specific COVID mortality rates. The low and middle income countries would have a larger mean share of deaths among people age 70 or older—58 percent rather than 37 percent—but still below the 87 percent share observed in high income countries. Thus, the differences in age structure can only partly account for the younger profile of deaths in developing countries.

**Table 1: Shares of COVID-19 Deaths Over Age 70, Actual vs Hypothetical with HIC Age Structure**

||**Country**|**GDP per capita, 2018**<br>**(current US$)**|**Share of population**<br>**over age 70**|**Actual share of**<br>**reported COVID-19**<br>**deaths over age 70**|**Hypothetical share of**<br>**COVID-19 deaths over age**<br>**70, with HIC age structure**|
|---|---|---|---|---|---|
||Afghanistan|521|2%|19%|54%|
||Pakistan|1,482|3%|24%|45%|
||Philippines|3,103|3%|36%|61%|
|**Low-**|South Africa|6,374|3%|28%|55%|
|**and**|Colombia|6,668|6%|49%|66%|
|**middle-**|Peru|6,941|6%|40%|55%|
|**income**|Brazil|9,001|6%|51%|68%|
||Mexico|9,673|5%|28%|46%|
||Malaysia|11,373|4%|34%|56%|
||Argentina|11,684|8%|62%|70%|
||Chile|15,923|8%|65%|74%|
||Hungary|16,151|13%|78%|81%|
||Portugal|23,403|17%|86%|85%|
||Spain|30,324|15%|86%|85%|
||Republic of Korea|31,380|11%|77%|84%|
||Italy|34,489|17%|85%|82%|
|**High-**|Japan|39,290|22%|83%|76%|
|**income**|France|41,470|15%|81%|80%|
||Canada|46,234|12%|91%|92%|
||Germany|47,616|16%|86%|84%|
||Netherlands|53,022|14%|89%|90%|
||Sweden|54,651|15%|89%|88%|
||Australia|57,396|11%|84%|86%|
||Denmark|61,391|15%|90%|90%|
||Norway|81,734|12%|87%|88%|
||Switzerland|82,829|14%|90%|90%|
||**Mean LMIC**||**4%**|**37%**|**58%**|
||**Mean HIC**||**15%**|**87%**|**86%**|

5
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

# Annex G O&M cost per unit of consumption

|Utility|2001|2002|2003|2004|2005|Average<br>2001–05|
|---|---|---|---|---|---|---|
|SONEB|—|—|—|0.66|0.70|0.68|
|ONEA|0.49|0.51|0.69|0.74|0.75|0.64|
|SODESI|—|—|—|—|—|—|
|ELECTRA|—|—|—|—|—|—|
|REGIDESO|—|—|—|—|—|—|
|AWSA|0.43|0.51|0.52|0.63|0.63|0.54|
|ADAMA|2.52|0.73|0.77|1.05|0.70|1.15|
|Dire Dawa|—|—|0.16|0.18|0.32|0.22|
|GWCL|0.22|0.20|0.25|0.21|0.32|0.24|
|NWASCO|—|—|—|0.21|0.18|0.19|
|KIWASCO|—|—|—|—|—|—|
|WASA|—|—|—|1.05|1.13|1.09|
|JIRAMA|—|—|0.20|0.25|0.49|0.31|
|ADeM Beira|—|—|—|0.26|0.16|0.21|
|Adem Maputo|—|—|0.50|0.62|0.70|0.61|
|AdeM Nampula|—|—|—|—|—|—.|
|AdeM Pemba|0.16|0.40|0.62|0.48|0.57|0.45|
|AdeM Quelimane|0.41|0.39|0.45|0.41|0.41|0.41|
|LWB|—|—|0.19|0.14|0.23|0.19|
|BWB|—|0.31|0.32|0.41|0.51|0.39|
|CRWB|—|0.48|0.73|0.58|0.73|0.63|
|Walvis Bay|—|0.22|0.26|0.36|0.35|0.30|
|Windhoek|—|0.69|0.32|0.36|0.53|0.47|
|Oshakati|—|0.50|0.26|0.38|0.42|0.39|
|SEEN|0.82|0.71|1.49|1.46|1.44|1.18|
|FCT WB|—|—|—|—|—|—|
|Kaduna WB|0.90|0.79|1.07|1.45|2.08|1.26|
|Katsina WB|0.17|0.36|0.47|0.43|0.46|0.38|
|Electrogaz|—|—|—|—|—|—|
|NWC Khartoum|—|—|—|—|—|—|
|NWC South Darfur|—|—|—|—|—|—|
|NWC Upper Nile|—|0.11|0.14|0.29|0.06|0.15|
|SDE|—|0.19|0.24|0.25|0.26|0.23|
|STEE|—|—|0.18|0.20|—|0.19|
|DAEASCO|—|0.18|0.19|0.34|0.51|0.31|
|DUWASA|—|—|—|—|—|—|
|MWSA|0.65|0.64|0.80|0.85|0.85|0.76|
|NWSC|0.46|0.44|0.83|0.98|1.21|0.78|
|Drakenstein|—|—|0.45|0.60|0.70|0.58|
|Tygerberg|—|—|1.09|1.32|1.56|1.32|
|eThekwini|0.91|0.94|1.24|1.35|1.50|1.19|
|Johannesburg|—|—|—|—|0.28|0.28|

38
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<!-- Start of picture text --> a) Phone survey (late May/early June 2021) b) Online experiment (late June/July 2021)<br>18.2% 18.4%<br>28.5%<br>55.5%<br>26.0%<br>53.3%<br>Planning on being vaccinated Planning on being vaccinated<br>Not planning on being vaccinated Not planning on being vaccinated<br>Unsure about being vaccinated Unsure about being vaccinated<br><!-- End of picture text --> Note: These charts are based on answers to question 3 and 34 (control group only) in Appendix B.

# <u>5.1.4 Reasons for vaccine hesitancy (based on both the phone survey and online experiment)</u>

In both the phone survey and online experiment, the main reason people stated they did not want to be (or were unsure about being) vaccinated was that they were worried about side effects from the COVID19 vaccine (see Figure 7). The same options were provided in both surveys. However, in the online experiment respondents were asked to select the main reason they were not prepared to get vaccinated, whereas in the phone survey respondents could select all that applied (see Figures 5a and 5b). Around four-fifths (78%) of respondents selected worried about side effects as a reason for hesitancy in the phone survey and around half (48%) selected this as the main reason in the online experiment.

**Figure 7 – Top three reasons for vaccine hesitancy in PNG**

<!-- Start of picture text --> a) Phone survey (late May/early June 2021) b) Online experiment (late June/July 2021)<br>.8 0.78 .5 0.48<br>.4<br>.6<br>0.53<br>.3<br>.4<br>.2<br>0.23<br>.2 0.10 0.12<br>.1<br>0 0<br>Worried about side effects Don't trust vaccines Don't think it will work Worried about side effects Don't trust vaccines Against religion<br>Share of respondents that selected this answer Share of respondents that selected this answer<br><!-- End of picture text --> Note: These charts are based on answers to question 4 and 35 (control group only) in Appendix B.

<u>5.1.5 Who could reduce vaccine hesitancy? (based on both the phone survey and online experiment)</u> 17
# **Appendix A: Constructing separate asset indices for urban and rural areas**

Tables A1 presents basic descriptive statistics for the ownership of selected assets in urban and rural areas of Azerbaijan. Given that there are marked differences in the extent of assets ownership between urban and rural areas, it could be argued that it is more appropriate to derive separate asset indices for each locality rather than a single index for the country as a whole. The drawback of doing so is that such an approach precludes any national analysis. However the advantage is that separate indices may provide better rankings of welfare _within_ urban and rural areas than a simple national index. Tables A2 and A3 therefore present the results of the PCA within each area and these weights are used to construct two separate assets indices, one for urban households and another for rural households.

**Table A1: Ownership of assets and household characteristics in urban and rural areas, Azerbaijan 1999 Census**

|||**_Urban_**|**_R_**|**_ural_**|
|---|---|---|---|---|
||Mean|Standard<br>Deviation|Mean|Standard<br>Deviation|
|1. Gas Line|0.792|0.405|0.310|0.462|
|2. Gas Cylinder|0.139|0.346|0.303|0.459|
|3. Electric Oven|0.188|0.390|0.321|0.467|
|4. Heating system (private, public)|0.110|0.313|0.063|0.244|
|5. Stove|0.171|0.376|0.681|0.465|
|6. Water|0.835|0.370|0.261|0.439|
|7. Hot water|0.068|0.252|0.002|0.053|
|8. Sewage System|0.695|0.460|0.037|0.190|
|9. Bathroom|0.625|0.483|0.068|0.252|
|10. Telephone|0.414|0.492|0.076|0.265|
|11. Total living area (square meters)|57.82|46.99|75.07|47.38|
|12. Number of rooms|2.37|1.55|2.37|1.42|
|13. Dwelling Structure(separate house, no)|0.440|0.496|0.899|0.301|
|14. Ownershipof dwelling|0.594|0.491|0.931|0.252|

A key question is how these alternative welfare rankings compare with those derived from the ‘inputed welfare method’. Figures A1 and A2 present scatter diagrams of the welfare indicators at the rayon level by the two different methodologies, along with the spearman rank correlations. Interestingly, using separate indices for urban and rural areas as opposed to a single national index results in a change from a weak positive to a weak negative correlation between the asset index and material poverty in rural areas. This highlights the problem of using a single 65
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(weighted) EU-SILC data, the Gini index equals only 0.2510. Regarding the imputed tax income in the EU-SILC data set, the Gini index is nearly 50% higher and equals 0.3699. The underestimation of inequality is exceptionally high at the top. The top 10% income share equals 30.3% in the imputed EUSILC and only 20.67% in the EU-SILC. The top 1% income share increases from 2.9% to 7.2%.<sup>14</sup> The income inequality using the imputed tax income in the EU-SILC is comparable but slightly lower than in the tax data. The slightly higher inequality in tax data may reflect a better representation of top-income earners (having different characteristics than EU-SILC respondents) in the tax dataset. These outcomes show that survey data severely underestimate Romania's true income inequality level.

**Table 4. Income inequality in Romania (gross labor income, only employees).**

|**Indicator**|**Survey data**<br>**(EU – SILC)**|**Tax admin data**<br>**(July 2020 * 12)**|**Imputed tax income**<br>**(imputed EU-SILC)**|
|---|---|---|---|
|**Gini index**|0.2510|0.4321|0.3699|
|**Theil index**|0.1010|0.3679|0.2728|
|**Botom 50% income share**|32.79%|21.87%|25.76%|
|**Middle 40% income share**|46.55%|45.38%|43.94%|
|**Top 10% income share**|20.66%|32.75%|30.30%|
|**Top 1% income share**|2.91%|8.28%|7.19%.|

_Source: own estimation based on the EU-SILC and tax data._

> 14 To get a sense of magnitudes, Atkinson et al. (2011) and Burkhauser et al. (2012) have analyzed income data for the United States and found that the survey-based estimates of the top 1% income share are lower by several percentage points compared to the estimates based on tax return data.

22
Table 1 Percent Changes in Prices from Benchmark due to Mode 1 Services Trade in India (2001‐ 2006)

||**Percent**<br>**Change **|
|---|---|
|**Factor**||
|Skilled Labor|39.48|
|Unskilled Labor|6.83|
|Capital(Agriculture)|‐12.00|
|Capital(Manufacturing)|‐28.81|
|Capital(Services)|36.87|
|**Output**||
|Agriculture|0.01|
|Manufactures|‐0.05|
|Services|18.84|

Source: Author’s Simulations Note: Numeraire is composite global consumer price index During the time period under consideration, technological improvements also decrease the import cost of Mode 1 services into India enough to allow its imports to rise by 280 percent. The increased Mode 1 services imports, complements to skilled labor, contribute to increases in M1Sk composite quantities. With more skilled labor drawn in from other industries, and with the cheaper and more abundant Mode 1 service imports there is thus an increase in the quantities of the M1Sk composite used. Table 2 illustrates this.

Table 2 Percent Change due to Mode 1 Services Trade in Input‐Output Quantity Ratios by Input and Industry in India (2001‐2006)

||**Agriculture**|**Manufacturing**|**Services**|
|---|---|---|---|
||**I**|**II**|**III**|
|**Skilled Labor**|‐3.39|‐4.13|‐0.30|
|**Unskilled Labor**|‐1.06|‐2.14|1.49|
|**Capital**|0.88|1.91|‐0.99|
|**Mode 1 Services**|81.43|81.20|88.45|
|**Composite**|0.74|4.47|12.30|

Source: Author’s Simulations 18
Taken together, the seasonal results in Tables 4 and 8 suggest that children’s fish intake, and therefore animal-source food intake, increase with the pulse in fish supply during September - December.

And, as Tables 6 and 7 show, this increase is particularly important for poor households. Figures 6 and 7 highlight two important facets of these results: the impact of the peak-season fish catch (Figure 6) and the joint impact of economic status and mother’s education (Figure 7).

**Table 8:  Fractional logit results - share of fish in animal-source foods**<sup>**a**</sup>

|**Mother's**<br>**Education**<br>**(0 Excluded)**|**Parameter**<br>**Estimates**|**Month**<br>**(June Excluded)**|**Parameter**<br>**Estimates**|
|---|---|---|---|
|**Primary**|**-0.319**|**July**|**1.385**|
||**(2.71)****||**(3.14)****|
|**Middle**|**-0.518**|**August**|**1.815**|
||**(4.19)****||**(4.15)****|
|**Secondary**|**-0.726**|**September**|**1.777**|
||**(5.23)****||**(4.04)****|
|**Post-Secondary**|**-1.053**|**October**|**1.899**|
||**(5.28)****||**(4.24)****|
|||**November**|**1.565**|
|**Asset Quintile**<br>**(Q1 Excluded)**|||**(3.48)****|
|**Q2 **|**-0.666**|**December**|**1.48**|
||**(2.14)* **||**(3.22)****|
|**Q3 **|**-0.707**|||
||**(2.25)* **|**Constant**|**-0.308**|
|**Q4 **|**-1.081**||**(0.46)**|
||**(3.38)****|||
|**Q5 **|**-1.175**|||
||**(3.54)****|||
|**Year**<br>**(2011 Excluded)**||||
|**2014**|**-0.203**|||
||**(2.59)****|||

**a Grid location results excluded for brevity**

**Observations:  3,201 Absolute value of t statistics in parentheses [* significant at 5%; ** significant at 1%]**

17
sector choice that includes experience, experience squared, **and schooling. Since these are often the** factors included in Mincerian earnings equations, the initial period real wage is included to ensure that it is not simply the wage effect that is reflected, The coefficients in tables 4a and b are those from the standard exponential form where the vector **fj** measures the degree to which an increase in worker characteristic X increases the probability of a worker going to sector j relative to the probability of staying in sector i. In the second half of each panel are presented the dummy and interactive effects of involuntary separation from the previous job, tabulated in the relatively infrequent case of a spell of unemployment between jobs. In only four cases does the likelihood ratio confirm the significance of these terms as a block (at the 10% level), but in no case does their inclusion cause a substantial alteration of the initial parameters. Again, with few exceptions, the results for the two samples are very similar.

Together, these first offer a view of overall labor force dynamics and then of how the four sectors interact.

# _I. Overview of the Labor Market_

Three important general findings are immediately apparent. First, the matrices reveal high levels of mobility with turnover rates (and implicitly the length of tenure at 5.21-5.7 years) in the formal sector similar to those in the U.S..'<sup>7</sup> There appears little evidence of the rigidities that the

> _17_ MediantenureforaU workers over 16 years old in 1991 was 5.1 years. _BLS NEWS,_ USDL 92-386. The implicit tenure based on turnove **r** ates on mean Juily/August 1994 median separation rate of 1. 1%/month was 7.6 years. Bureau of National Affairs, _Bulletin to Management,_ Dec. 8, 1994. Mean tenure calculated as

13
It is important to first define the organized manufacturing sector of the Indian economy. The organized manufacturing sector is comprised of establishments with more than ten workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, comprised of establishments which fall outside the scope of the Factories Act. The organized sector accounts for over 80% of India’s manufacturing output, while the unorganized sector accounts for a high share of plants and employment (Ghani et al. 2012). We focus on the organized sector in this study.

The organized manufacturing sector is surveyed by the Central Statistical Organization through the Annual Survey of Industries (ASI). Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. We use the provided sample weights to construct populationlevel estimates of organized manufacturing activity at the district and two-digit NIC level. Districts are administrative subdivisions of Indian states or union territories. As we discuss further below, we use district variation to provide more granular distances from the various highway networks.

ASI surveys record several economic characteristics of plants like employment, output, capital, raw materials, and land and building value. For measures of total manufacturing activity in locations, we aggregate the activity of plants up to the district or district-industry level. We also develop measures of labor productivity and TFP. Labor productivity is measured through output per employee at the plant level, with an average then taken across plants for a district. TFP is calculated through a residual regression approach. For every two-digit NIC industry and year, we regress log value-added (output minus raw materials) of plants on their log employment and log capital. The residual from this regression for each plant is taken as its TFP. We then take the average of these residuals across plants for a district.

As our data are repeated cross-sections, rather than panels with unique plant identifiers, there are limits with respect to some of our analyses. Perhaps most notably, we do not have accurate measures of exiting plants. Our data do, however, allow us to measure and study new entrants. Plants are distinguished by whether or not they are less than four years old. We will use the term “young” plant or new entrant to describe the activity of plants that are less than four years old. We aggregate young plant activity at the district level, similar to metrics of total activity.

Our core sample contains 312 districts. This sample is roughly half of the total number of districts in India of 630, but it accounts for over 90% of plants, employment, and output in the manufacturing sector 9
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