rafmacalaba/datause-encoder-data huggingface dataset:
# _8.1. Population Distribution by Social Groups within the States_

Table 9 reports the shares of various social groups in the 17 largest states according to the expenditure survey of 2009-10. We continue to rank the states according to population from the largest to the smallest.

**Table 9** : Shares of different social groups in the state population, 2009-10

|State|ST|SC|OBC|FC|NS|Total<br>(million)|
|---|---|---|---|---|---|---|
|Uttar Pradesh|1|25|51|23|74|175|
|Maharashtra|10|15|33|43|75|97|
|Bihar|2|23|57|18|75|84|
|Andhra Pradesh|5|19|49|27|76|77|
|West Bengal|6|27|7|60|67|75|
|Tamil Nadu|1|19|76|4|79|64|
|Madhya Pradesh|20|20|41|19|60|62|
|Rajasthan|14|21|46|19|65|62|
|Gujarat|17|11|37|35|72|54|
|Karnataka|9|18|45|28|73|53|
|Orissa|22|21|32|25|57|36|
|Kerala|1|9|62|27|90|31|
|Assam|15|12|26|47|73|28|
|Jharkhand|29|18|38|15|53|26|
|Haryana|1|29|30|40|70|23|
|Punjab|1|39|16|44|61|23|
|Chhattisgarh|30|15|41|14|55|22|
|India (17 states)|8|21|43|28|71|993|
|India(all states)|9|20|42|29|71|1043|

Source: Authors’ calculations from the NSSO expenditure survey, 2009-10 Nationally, the Scheduled Tribes constitute 9 percent of the total population of India according to the expenditure survey of 2009-10. In past surveys and the Census 2001, this proportion was 8 percent. The Scheduled Castes form 20 percent of the total population according to the NSSO expenditure surveys, though the Census 2001 placed this proportion at 16 percent. The OBC are not identified as a separate group in the censuses so that their proportion is available from the NSSO surveys only. The figure has varied from 36 to 42 percent across the three quinquennial expenditure surveys since the OBC began to be recorded as a separate group.

The Scheduled Tribes are more unevenly divided across states than the remaining social groups. In so far as the ST were very poor at independence and they happen to be outside the mainstream of the economy, ceteris paribus, states with high proportions of ST population are at a disadvantage in combating poverty relative to the other states. From this perspective, the four southern states enjoy a clear advantage: Kerala and Tamil Nadu have virtually no tribal populations while Andhra Pradesh and Karnataka have 22
**Table 2 (Continued): Plot-Level Characteristics by Gender of Plot Manager**

||**Total**|**Exclusively**<br>**Male**<br>**Managed**|**Exclusively**<br>**Female**<br>**Managed**||
|---|---|---|---|---|
|**Cropping Patterns**|||||
|Plot is Intercropped|32.5%|33.3%|31.8%||
|Proportion of Plot Planted with Roots|27.9%|22.5%|33.3%|***|
|Proportion of Plot Planted with Pulses and Oils|26.1%|23.2%|29.1%|***|
|Proportion of Plot Planted with Cereals|24.0%|25.7%|22.2%|***|
|Proportion of Plot Planted with Banana|12.9%|14.3%|11.5%|***|
|Proportion of Plot Planted with Coffee|3.9%|6.5%|1.3%|***|
|Proportion of Plot Planted with Other Cash Crops|2.4%|4.1%|0.7%|***|
|Proportion of Plot Planted with Vegetables|1.7%|2.3%|1.2%|***|
|Proportion of Plot Planted with Fruits|1.0%|1.3%|0.7%|**|
|Proportion of Plot Planted with Other|0.0%|0.0%|0.0%||
|**Plot Manager Characteristics**|||||
|Age of Plot Manager|42.5|45.3|39.7|***|
|Education Years of Plot Manager|5.4|6.5|4.3|***|
|Plot Manager Received Government (NAADS)|19.9%|22.9%|16.9%|***|
|Extension Services|||||
|Plot Manager Received Other Extension Services|10.0%|13.5%|6.6%|***|
|Number of observations (Plots)|6999|3500|3499||

Stars are significance levels for t-tests of the equality of means between male and female managed plots. * significant at 10%; ** significant at 5%; *** significant at 1%.

17
# **Executive Summary**

The Mexico City Metropolitan Area (Zona Metropolitana del Valle de Mexico (ZMVM)) has witnessed high levels of air pollution in the past few decades. Recent efforts to curb emissions have been reasonably successful, and 1999 had the lowest overall level of air pollution during the last decade. With the exception of lead, carbon monoxide and sulfur dioxide (S02), however, pollution levels are still far above current air quality standards (See Table E.1).

**Table E.1: Number of Days Per Year that Ozone and PM10 Concentrations in Mexico City Satisfy Daily Air Quality Standards**

||_1995_|_1996_|_1997_|_1998_|_1999_|
|---|---|---|---|---|---|
|Ozone|41|39|43|45|65|
|PM10|273|186|212|176|345|

_Source:_ **GDF** (2000).

Further efforts to reduce polluting emissions are being developed by the Comision Ambiental Metropolitana (CAM) under the Third Air Quality Program 2001-2010. This study presents an economic valuation of benefits from reducing pollution in the ZMVM, as the main economic rationale for controlling emissions is the welfare gain from improvements in air quality. The current study focuses on the two most important economic impacts of air pollution, namely health impacts and restrictions imposed on economic activities through environmental contingencies _(contingencias ambientales)._ The health hazards associated with ozone and PM1O are studied because these substances are the most important in terms of violating pollution standards. Ozone pollution stems mainly from emissions of NO, and VOCs. Their concentration levels depend on the amount and location of emitted pollutants, geographical characteristics, meteorological conditions, and atmospheric chemistry and transport. The chemistry of ozone formation is complicated and nonlinear: under certain conditions, an increase in NO, emissions could reduce ozone concentrations. PM10 pollution stems mainly from direct emissions of particles, and from reactions of NO,, and S02 with other substances in the atmosphere. Likely emission sources are building and construction (road construction), diesel trucks and buses, forest fires, open-air refuse burning, some manufacturing industries, and resuspension of road dust.

The daily 1-hour maximum air quality standard for ozone is 0.110 ppm. During 199599, the highest concentration observed for ozone-0.349 ppm-was measured at the Pedegral station, in the southwest zone of the ZMVM. The Chapingo station in the northeastern zone was the least polluted, with a daily 1-hour maximum concentration of 0.210 ppm. The daily average air quality standard for PMIO is 150 ,ug/m3 and the annual average standard is 50 igIm3. All stations violated both standards with the exception of the annual average standard at the Pedregal and Coacalco stations. The highest concentrations were in the east of the ZMVM with a daily maximum of 335 jig/m3 at the Netzahualcoyotl station. The highest annual average of 94 ug/m3 was observed at the Xalostoc station. In 1995, over 1.2 million
_Table 2: Service Price increases per Fuel Cost Increase in Kinshasa_

|**Service**|**Base Fare Range**||**Share of Operating**<br>**Ct ttibtd t**|**Explanation**|
|---|---|---|---|---|
||**CDF**|**PPP $ (2018)**|**os arue o**<br>**fuel price**||
|Transco|500-1000|0.61-1.22|30%|Bus Service|
|Esprit de<br>Vie|500-1000|0.61-1.22|30%|Bus Service|
|Esprit de<br>Mort|300-1000|0.37-1.22|40%|Private Minibus Service|
|Taxi<br>Jaune|300-2500|0.27-3.06|30%|4-Seater Sedan Service|
|Moto<br>Taxi|100-2000|0.12-2.44|20%|Motorbike Service|

# 2.2 Methodology

## 2.2.1 Accessibility

For any origin area _i_ , accessibility is calculated as the sum of jobs across destination areas _j_ ={1,2,3..., _n}_ where at least one travel itinerary _k_ satisfies both the travel time and travel cost thresholds (Equation 1). To calculate overall accessibility for an urban area that comprises multiple origin and destination pairs, accessibility in each origin area is weighted by the share of the population in that area. This accessibility measure does not indicate a decision process for commuting but it indicates potential access given selected thresholds and is a useful tool for planners and policy makers.

_Ai_ is the total number of jobs accessible in origin area _i_ ; Oj is the number of jobs in destination area _j_ ; _K_ is the total set of possible itineraries between origin _i_ and destination _j_ ; _tijk_ and _bijk_ represent the travel time and travel cost, respectively, of itinerary _k_ between origin _i_ and destination _j_ ; _T_ and _B_ are travel time and travel cost thresholds, respectively, which define binary functions _f(tijk)_ and _g(bijk)_ .

As part of the assessment of monetary costs, it was felt that a more suitable metric of affordability was a monetary cost relative to an average monthly income or travel budget of different areas. Single trip costs were adjusted assuming two trips a day for 22 business days in a month and then dividing these by the monthly income as shown in Equation 4.

Where _bt_ is the travel budget to complete a trip _t_ , _ct_ is the absolute cost of a trip _t_ and _Ii_ is the average income of origin area _i_ .

9
# **Results from Panel Estimations**

Table 5 presents the pooled OLS and fixed effects results for the narrow spread and table 6 for the wide spread measure. As mentioned before, all regressions include yearly and quarterly dummy variables, and we report t-values based on clustered standard errors as well as _R_ 2 with and without bank dummies (since the _R_ 2 of the fixed effects models exclude the cross section variation due to the bank dummies).<sup>12</sup> Except column 1, all regressions use bank- level fixed effects.

There is some strong evidence that higher overhead costs lead to both higher narrow and wider spreads, as banks pass on these additional costs to borrowers. Loan loss provisions have a significantly negative effect on both spread measures in all model specifications. This is not surprising since the spreads are an ex-post measure so increased provisions for bad debts should result in lower interest income on the banks’ income statements. Also, after controlling for overhead costs we do not observe any evidence that larger banks (in terms of market share in deposits) either charge significantly lower or higher spreads.

The effect of liquidity on the narrow spread is mixed and unclear whereas higher banks’ liquid holdings appear to decrease the wide spread, even though this effect is often not significant. Equity does not significantly influence the spreads even though the signs are predominantly negative.

To better capture any possible effects of liquidity and equity in the context of the announcement of new capital requirements and subsequent merger and acquisition activities among Nigerian banks during 2004 and 2005, we interact both liquidity and equity variables with the yearly dummy variables 2004 as well as 2005. Column 3 in table 5 and 6 gives the result for liquidity. For the narrow spread measure in table 5, the effect of both interaction variables is negative but insignificant and of small magnitude whereas for the wide spread in table 6, only the interaction with 2005 is negative and close to being significant but also with a small economic magnitude.

Column 4 in tables 5 and 6 shows the relevant findings for the interaction of equity and the yearly dummy variables in 2004 and 2005. Overall, equity movements in

> 12 As stated before, models that have macroeconomic control factors do not include yearly or quarterly dummy variables in order not to distort the effects of the macroeconomic variables.

16
# Table 8 Average Wage Earning and It's Inequality by Age and Education for Male and Female Workers and Employees: Ukraine 1989

||Male Em|ployes|Femdae<br>Em|ployees|Ratio of Fa|lde-Male|
|---|---|---|---|---|---|---|
|Employees|||||||
|Chwractedstics|Aveage<br>Wage|Gini<br>Index|Avae<br>Wage|Ginl<br>Index|Averag<br>Wge|income<br>tnequdity|
||||Levels of Ed|ucation|||
|Higher Education|236.124542|19.0183462|184.3897645|18.17798|78.1|95.6|
|Secondary Special|216.259565|20.9938104|144.6056877|19.41531|66.9|92.5|
|Secondary General|215.895647|22.5010022|143.6918695|22.29261|66 6|99.1|
|Incomplete Secondary|204.12568|23.9308589|140.6166197|24.30334|68.9|101.6|
|Primary Education|177.860896|23.4948659|129.3665318|24.12899|72.7|102.7|
||||Age of Em|ployees|||
|16-24|176.715836|21.9304696|125.755102|20.0741|71.2|91.5|
|25-29|210.367413|21.08665|139.0529129|19.33895|66.1|91.7|
|30-39|229.616129|21.4136811|154.2772768|20.57662|67.2|96.1|
|40-49|230.484869|20.5815562|161.2882208|21.58169|70|104.9|
|>50|205.076292|22.45246|149.5554106|23.52911|72.9|104.8|
43 Table (4.1) Equilibrium Real Exchange Rate and Overvaluation in Sudan

||Real Exchange Rate|Equilibrium||
|---|---|---|---|
||(EatVPn)|Exchange Rate|Overvaluation|
|1971|100.00|100.00|0.00|
|1972|98.99|99.27|0.28|
|1973|97.01|99.77|2.85|
|1974|93.05|100.46|7.96|
|1975|90.26|98.12|8.71|
|1976|89.63|97.01|8.22|
|1977|84.65|98.82|16.75|
|1978|85.59|98.41|14.97|
|1979|84.22|98.02|16.38|
|1980|84.33|93.54|10.91|
|1981|86.95|89.53|2.97|
|1982|89.13|84.28|-5.44|
|1983|86.03|90.03|4.65|
|1984|84.63|90.40|6.82|
|1985|84.63|86.94|2.73|
|1986|84.92|89.06|4.87|
|1987|83.60|89.48|7.02|
|1988|78.05|90.95|16.52|
|1989|75.89|90.55|19.31|

EatIPn = (Exchange rate for agricultural tradables/Price of non-tradables)
- 3 -

increase drivers’ wages. While drivers over 55 years old account for 34 percent of the total labor force in this sector in Europe, only 7 percent are under 25 years old (Ti et al., 2022).

3. Load factor is an important determinant of freight rates. In Europe, the share of road freight kilometers carried by empty vehicles has been increasing especially for international freight ( **Figure 4** ). From the efficiency point of view, holding everything else constant, freight rates may be higher when there are more empty containers. Freight rates are also determined by the road condition. The higher quality of roads generally leads to lower freight rates. In theory, road user costs, including vehicle operating and maintenance costs and drivers’ time costs, can be reduced if roads are well maintained. Road roughness is negatively associated with driving speed (e.g., Alessandroni et al., 2017). However, the relationship among road conditions, traffic speed and freight rates may be complicated by other factors involved, such as the trucking industry’s market structure. Given all these factors, it remains open to argument whether overall freight rates increased or declined, and why.

4. The current paper recasts light on the question of how road freight transport costs are determined, using large sample shipping data from Eastern European and Central Asian countries. Since the early 2010s, the region has made significant infrastructure investments to improve interregional connectivity between Europe and East Asia. Despite our general belief that the enhanced connectivity would facilitate regional integration and trade (e.g., ADB, 2020; Kalyuzhnova and Holzhacker, 2021), there is little empirical evidence to the best of my knowledge. The paper takes advantage of unique micro data, which comprises stop-by-stop shipping data in the Central Asia Regional Economic Cooperation (CAREC) member countries, including Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan, collected by the Asian Development Bank (ADB).<sup>2</sup> The data is focused on longdistance truck shipments and covers a long period of time from 2010 to 2020, which allows us to evaluate the impacts of long-term infrastructure investment made in the region on freight costs.

> 2 See ADB (2014, 2020) for more details. I would like to express my special thanks to the CAREC Corridor Performance Measurement and Monitoring (CPMM) team for sharing relevant raw data.
under-invest in the experimentation necessary to discover new exports, and consequently the process of productive diversification might be hampered by this market failure.

However, the market failure hypothesis has not been tested. And it is entirely plausible that both the intensive and extensive margins of trade are hampered by barriers to entry by firms or by barriers to exports. In fact, we know little about the relationship between the emergence of new export products and economic development in general. This paper attempts to fill this void by focusing mostly on testing the market failure hypothesis.

We develop an identification strategy for the existence of market failures affecting export discoveries, using disaggregated product-level export data available for a large sample of countries. In addition, we develop a metric of export discovery activity and establish empirical regularities that serve to deepen our understanding of the discovery process and its relationship with diversification and economic development. The evidence provides some support for the market failure hypothesis: barriers to entry and exporting (which are expected to delay the entrance of imitators) appear to enhance the effect of the growth of the intensive margin of exports (relative to non-export economic activities) on the number of export discoveries within countries and industries. However, this magnification effect of barriers to entry on export discovery counts is less significant when the dependent variable is the total number of export discoveries at the country level. This evidence might be consistent with the existence of inter-industry, within-country knowledge spillovers affecting export discoveries.

The rest of the paper is organized as follows. Section 2 discusses the data and the methodology for identifying episodes of export discoveries. Section 3 proceeds in stages.

3
# **<mark>Intergenerational Impact of Population Shocks on Children’s Health:</mark>**

# **<mark>Evidence from the 1993-2001 Refugee Crisis in Tanzania</mark>**

_<mark>Soazic Elise Wang Sonne</mark>_<sup>_1_</sup> _<mark>and Paolo Verme</mark>_<sup>_2_</sup>

**JEL Codes:** O10; O12; O13; O15; F22; R23; R12.

**Keywords:** Early childhood development; refugees; forced displacement, health outcomes, children.

> 1 World Bank and United Nations University-Maastricht Economics and social Research Institute and Training Center on Innovation and Technology (UNU-MERIT). 2 World Bank. This work is part of the program ``Building the Evidence on Protracted Forced Displacement: A MultiStakeholder Partnership''. The program is funded by UK aid from the United Kingdom's Department for International Development (DFID), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of DFID, the WBG or UNHCR. We are also deeply indebted to participants to the Fall 2018 UC Berkeley Development Economics Lunch seminar series, the 2018 World Development - University of Michigan Sustainable Development Conference, the 2018 UC Berkeley Geo4Dev Conference and the 2018 UNHCR internal seminar for their excellent comments and very insightful suggestions. We also thank Jean-Francois Maystadt for kindly sharing his geo-coded data on the 13 refugee camps in Tanzania and Kathleen Beegle and Emanuela Galasso at the World bank for peer reviewing the paper. All remaining errors and omissions are our own responsibility.
telecommunications networks, but is robustly positively correlated with the conceptually preferable measures over the sample for which these are available.<sup>17</sup> All three variables are rescaled to lie between zero and one in such a way that higher values indicate better quality of infrastructure services. Using the weights obtained from the principal components procedure, the synthetic index of infrastructure quality can be expressed as: The index **IQ** captures approximately 60 percent of the total variation of the three underlying indicators.

Our indicators of quantity and quality of infrastructure share a good deal of common information — _i.e._ , their correlation ranges from 0.63 to 0.73 depending on the specific quantity index considered. In the individual infrastructure sectors the respective stocks and quality measures are also positively correlated (0.59 for telecommunication, 0.46 for electricity, and 0.54 for roads). In addition, we should also note that the indices of quantity and quality may also capture trends in access. Calderón and Servén (2010) show that households’ access to infrastructure is typically broader in countries with greater availability and quality of infrastructure services.<sup>18</sup> Our aggregate indicators of infrastructure quantity and quality are strongly and positively associated with economic growth (see Figures 2.1 and 2.2). Long-term growth, as measured by per capita GDP growth over 1960-2005, is higher in countries with larger infrastructure networks. The correlation is equal to 0.34 when quantity is proxied by the index IK1. On the other hand, countries with higher quality of infrastructure services also tend to exhibit higher per capita growth rates. The correlation equals 0.42 when quality is measured by the index **IQ** .<sup>19</sup> Finally, Figures 2.3 and 2.4

> 17 In any case, dropping from the list of variables the indicator of waiting time for the installation of main telephone lines ( _Q_ 1) leads to a synthetic index highly correlated with the one described in the text, and to regression estimates of the growth contribution of infrastructure very similar to those reported below. Those estimates are not presented here to save space.

> 18 However, access is not distributed evenly across the population. Households in the upper percentiles of the income distribution typically enjoy much better access than poorer households do (World Bank 2006) Nevertheless, expanding service coverage is typically associated with improved access by the poor.

> 19 The correlation between growth and the alternative index of infrastructure quantity **IK** 2 is equal to 0.34. Across infrastructure sectors, growth is positively correlated with telecommunication stocks (0.24 for total phone lines, and 0.21 for main phone lines), electricity generating capacity (0.15) and the length of the road

16
||credibility of the government's commitment<br>to such policies.||
|---|---|---|
|_POL STAB_|Perception of the likelihood that the government<br>will be destabilized or overthrown by un-<br>constitutional or violent means, including<br>politically-motivated violence and terrorism.|Kaufmann et al. (2008)|
|_REG QAL_|Perception of the ability of the government<br>to formulate and implement sound policies and<br>regulations that permit and promote private<br>sector development.|Kaufmann et al. (2008)|
|_RULE LAW_|perceptions of the extent to which agents have<br>confidence in and abide by the rules of society,<br>and in particular the quality of contract<br>enforcement, property rights, the police, and the<br>courts, as well as the likelihood of crime and violence.|Kaufmann et al. (2008)|
|_VOICE ACC_|Perception of the extent to which a<br>country's citizens are able to participate<br>in selecting their government, as well as<br>freedom of expression, freedom of association,<br>and a free media.|Kaufmann et al. (2008)|
|_DEMOCRACY_|Proxy for the political system in a country.<br>Include information on competitiveness and openness<br>of executive recruitment, constraints on chief executive, <br>regulation and competitiveness of participation.|Marshall & Jaggers<br>(2005)<br> <br>|
|_COLONY_|Colonial Background<br>Dummy variable that switches to one if the<br>country has ever been a colony|Correlates of War<br>2 Project (2008)<br>|
|_AFFINITY USA_|Extended Gartzke index on voting patterns<br>in the UN General Assembly. Takes a value between -1<br>(USA and recipient never voted the<br>Same) and 1 (USA and recipient always voted the<br>same).|Voeten and<br>Merdzandovic<br>(2009)|
|_OIL RESOURCES_|Proved oil reserves in 1000 tons|US Energy<br>Information<br>Administration|
|_GAS PROD_|Annual production of natural gas in terajoules|US Energy<br>Information<br>Administration|

41