rafmacalaba/data-use-mentions-extended huggingface dataset:
sup>87</sup> These portals provide data on populations of concern at regional and country levels, including time series data, demographics, location and accommodation information.<sup>88</sup> Eurostat compiles and publishes data on asylum (applications and decisions) and managed migration in European Union member countries. Countries and national and international NGOs also publish these statistics, based on sources of various completeness, quality and timeliness (UNSD 2014). There are sometimes substantial inconsistencies between the numbers published by different organizations for the same country, including high-income countries with good statistical systems, usually due to differences in definitions, times and statistical methods, including the mixing of data on flows and stocks (UNSD 2014).

There are several challenges associated with the compilation of data on asylum-seekers and refugees. Significant among these are: (a) the lack of capacity of national statistical agencies in many developing countries to collect robust data on refugees; (b) weak or incomplete monitoring of refugees dispersed within host communities; (c) lack of capacity to maintain up to date information on refugees (reflecting new arrivals,

> 81 General population registers may also provide opportunities for more elaborate analysis of the integration of refugees in asylum countries, as the data could be linked to other administrative registers, for example on labor and education (UNSD 2014).

> 82 UNHCR collects, compiles and publishes data on asylum-seekers, refugees and IDPs protected or assisted by UNHCR, including populations in refugee-like or IDP-like situations.

83 Established in 1863, the ICRC’s mission is to ensure humanitarian protection and assistance for victims of armed conflict and other situations of violence. ICRC’s work is based on the Geneva Conventions of 1949, their Additional Protocols, its Statutes—and those of the International Red Cross and Red Crescent Movement—and the resolutions of the International Conferences of the Red Cross and Red Crescent.

84 WFP is the food assistance branch of the United Nations and the world's largest humanitarian organization addressing hunger and
5**|5.5|6|8|6|5|4|
|<br>General cargo vessel pre-berth waiting<br>time - average (hours)|**2.9**|2.5|48|9.6|36|24||
|General cargo vessel stay (turnaround<br>time) - average (hours)|**2.2**|3|48|48|40.8|60||
|Container crane productivity* (container<br>per hour)|**18**|||13|12|||
|General cargo crane/gang productivity -<br>average (tones per hour)|**16**|16|15|13.5|9||22.5|
|**TARIFFS:**||||||||
|General cargo handling charge, ship to<br>gate - average ($/tonne)|**260**|200|180|168|155|160|220|
|Dry bulk handling charge, ship to gate or<br>rail - average ($/tonne)|**13.5**|10.5|8.5|10|8|15|9|
|Liquid bulk handling charge –average<br>($/tonne)|**5**|4|5|3||5|5|

Source: Mundy and Penfold 2009. Derived from AICD ports database downloadable from http://www.infrastructureafrica.org/aicd/tools/data TEU = 20-foot equivalent units.

# **Challenges**

As traffic in the port of Abidjan increases, expansion, specialization, and modernization will have to be on the port’s agenda. In 2008 the port initiated a project to add major new deepwater container capacity at Île Boulay, thereby
Bank Living Standards Measurement Study (LSMS) team, conducted the third wave of the General Household Survey Panel (GHS-Panel) with over 4,500 nationally-representative households. Data were collected on consumption, behavioral preferences, depressive symptoms, history of conflicts (5 years), deaths of family members (2 years), and economic shocks (2 years), among others. We analyze data for the 4,579 households for which we have complete data sets of consumption expenditures,<sup>6</sup> depression scores, economic activities, educational investment and other variables. Our primary unit of analysis is individuals who responded to the included module on depression (only one individual per household).

Table 1 presents the summary statistics of each variable. Seventy-four percent of respondents to the depression module are household heads (with 21 percent of all households being femaleheaded). Thirty-seven percent of respondents are female, with the average age of the respondents being slightly over 50<sup>7</sup> and the average household size being between five and six. About 74 percent of respondents are married, of which 57 percent are monogamously married, and 17 percent are polygamously married. Some 43 percent are Muslim, while the rest are Christian (with 2 percent having unknown or no religion). On average, 41 percent of the sample lives in an urban

> 6 The methodology implemented when calculating consumption expenditures is included in the survey documentation, available on the World Bank’s Micro Data Catalogue.

> 7 Note that six of the respondents are under the age of 15. In addition, the average age of the household head is 53.

7
- 21 -

Countries which discovered and exploited oil resources (The Congo, Gabon, Cameroon, and Nigeria) are likely to have enjoyed higher growth rates through 1982, conditional on factor inputs and tax policy. Political instability can disrupt economic growth both through the destruction of property and capital, the flight of skilled workers, and the loss of new investment (Schneider and Frey, 1985; Stewart and Venieris, 1985). A variable measuring the number of "successful" coups during the period is included (Griffiths, 1984). Although coups are potentially endogenous (declining economic fortanes spur coup attempts), Wheeler (1984) finds that political disruption Granger-causes output changes, but not conversely. In the next section, sources of data are described, and regression results are reported.

# IV. Egirical Results

The data set described below will be used to estimate both the output growth model developed in Section III, and also to estimate investment demand equations. The data come from national accounts and government financial statistics. To abstract from short-term fluctuations, income growth is averaged over 9 years, 1965-73 and 1974-82.9 The period 1973-74 represents a significant transition for many countries from relatively stable growth to uneven development as rising oil prices and worldwide recessions led to declining export prices and increased debt. Although some export prices rose later in the 1970s, the second oil price increase in 1979-80, subsequent economic slumps, and increasing debt burdens all led to increasing stress on government tax collection efforts. Despite these downturns, government investment during 1974-82 was high relative to the previous period (Shalizi, Ghandi, and Ehdaie, 1985). Overall tax effort increased for Sub-saharan African countries during this 2/ In a few countries, 1974-81, growth rates were calculated; for Somalia, 1974-79 rates were measured.
# **Welfare Effects of Introducing Competition in the Telecom Sector in Djibouti***

Xavier Decoster, Gabriel Lara Ibarra, Vibhuti Mendiratta and Marco Santacroce

**_Key words:_** Digital Economy, telecom, market structure, welfare **_JEL codes:_** D60, D40

*We are grateful to Carlos Rodriguez Castelan, Eduardo Malasquez and Axel Rifon Perez for their insightful comments. The authors also acknowledge the collaboration with the Directorate for Statistics and Demographic Studies in Djibouti that made the microdata of the EDAM 2017 survey available for this work. Contact the authors at: <u>glaraibarra@worldbank.org</u> and vmendiratta@worldbank.org.
# **6. Conclusion**

In many tropical developing countries poverty is believed to be the main driving force behind the degradation of marine and coastal environment. There is also concern that if policies adopted to address degradation of these resources do not take into consideration the poverty issues, they will be less likely to be effective and lead to greater poverty and marginalized livelihoods for those that depend on these resources. Using household survey data from coastal areas of Tanzania, this paper examines the linkages between poverty, MPA management activities and household use of destructive fishing gear. Our goal is to understand whether poverty contributes to the use of destructive fishing gear and how MPA activities impact household choice of fishing gear and household welfare.

While we cannot establish a direct link between income poverty and the use of destructive fishing gear, we do find that some aspects of poverty contribute to the use of destructive fishing gear. Poverty has many different dimensions, and hence we examine a number of measures of poverty in the analysis to understand how different aspects of poverty affect household fishing gear use. We find that food insecure households and female headed households are more likely to use destructive fishing gear. Higher levels of male education and higher asset wealth, on the other hand, decrease the probability that households use destructive fishing gear.

We also find that ownership of fishing gear matters. Households that own destructive fishing gear are virtually certain to use such gear. However, more than two-thirds of fishing households do not own the fishing gear they use. These households are only slightly more likely to use destructive fishing gear than not. Most of the fishing gear we classified as destructive also happens to be illegal. Enforcement of existing regulations banning the use of the illegal fishing gear is clearly important, but so is perhaps a policy that confiscates illegal gear. Fines and other types of punishment may do little to deter the use of illegal gear, particularly when the illegal fishing gear can be rented out. For other destructive fishing gear that is not illegal, a
)||||(0_._011)||
|Employed in Industry|||0_._283<sup>_∗∗∗_</sup>||||0_._272<sup>_∗∗∗_</sup>||
||||(0_._012)||||(0_._012)||
|Employed in Services|||0_._349<sup>_∗∗∗_</sup>||||0_._336<sup>_∗∗∗_</sup>||
||||(0_._009)||||(0_._009)||
|Country FE|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|
|Adjusted R2<br>|0_._523<br>|0_._655<br>|0_._668<br>|0_._699<br>|0_._531<br>|0_._658<br>|0_._670<br>|0_._705<br>|
|Nr. of countries<br>Nr. of hh<br>1|16<br>34630<br>1|16<br>34630<br>1|16<br>34630|16<br>88450<br>1|16<br>34630<br>1|16<br>34630<br>1|16<br>34630|16<br>88450|

Source: International Urban Poverty Database.

Note: *** p < 0.01, ** p < 0.05, *** p < 0.1 . Robust standard errors are in parentheses. WorldPop 250m is used for the DB methods. The dependent variable is the log of per capita consumption expenditures, expressed in PPP and not spatially deflated
The ND readiness index shows an upward trend since 1995 based on improvements in economic, governance, and social readiness. This observation is particularly evident for the ECA region, followed by MENA (Figure 6D and E). In contrast, countries in the most vulnerable region, EAP, do not seem, on average, to get more resilient to extreme climate shocks, as their ND readiness indicator remained flat for the period under study.

# _Transition risks – high carbon intensity_

Countries with a large carbon footprint face significant transition risks arising from the move towards low‐ carbon economies. We use CO2 emissions intensity (kg per kg of oil equivalent energy use) from the World Development Indicators to track countries’ exposure to transition risks.

Countries’ exposure to transition risks, as measured by their CO2 emissions intensity, has hardly changed since 1995. The data on CO2 emissions show that between 1995 and 2014, median CO2 intensity across countries has been fairly stable (Figure 7A and B). Moreover, MENA appears to be the most exposed, but its CO2 intensity declined over time. In particular, Jordan and Morocco face high transition risk. Many economies in the region try to diversify their economies, even though they are still heavily dependent on hydrocarbons.

Whereas countries in the MENA and in ECA reduced their carbon footprint in the past 20 years, those in Asia (both Eastern and Southern) increased their CO2 emissions (Figure 7B). Yet, for some countries in ECA, such as Poland and Kazakhstan, transition risk has increased over time.

# _Transition risks – fossil fuel exporters_

Fossil fuel export dependent economies face particularly significant transition risks. Global efforts to reduce GHG emissions and fossil fuel consumption will affect demand and prices of fossil fuels. Revenue from fossil fuels is the main source of revenue for many fossil fuel exporters which thus face significant fiscal risks. The MENA region is the most exposed while dependence is rising for some countries in the ECA region (Figure 7D). Diversification away from fuel exports will become
.253|0.265|0.270|0.243|
|Municipalities|1,169|1,169|1,169|1,169|1,169|1,169|1,169|1,169|1,169|1,169|1,169|

_Note:_ Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants. The average income corresponds to the average per capita household income in each municipality in logarithms. The deciles of income correspond to the cut-off points of dividing in each municipality the households into ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Gini refers to the Gini coefficient. Fixed effects model by year and municipality for the years 2005, 2010, and 2014. Include 1169 municipalities with fewer than 15000 inhabitants.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 35
if her household has been victim of an insecurity event with “physical” consequences or if she lives in a district where at least a quarter of the households have been victim of an insecurity event with “physical” consequences. In turn, an individual who is not extremely security deprived is moderately security deprived if her household has been victim of an insecurity event with “material” consequences or if she lives in a district where at least a quarter of the households have been victim of an insecurity event with “material” consequences. In the housing dimension, we consider whether an individual lives in a household that has access to electricity, limited-standard drinking water, limited-standard sanitation and whose dwelling is made of appropriate materials. An individual is extremely housing deprived if her household is deprived in the four housing indicators and moderately housing deprived if her household is deprived in three housing indicators.

The deprivation mapping for the preferred design is the same as that of the baseline design, except that the former additionally considers a moderate monetary deprivation status and the education dimension. An individual is moderately monetary deprived if her consumption is above the national poverty line but below the WB’s societal poverty line in her state.<sup>14</sup> An individual is extremely education deprived if she is a child not enrolled in school. An individual is moderately education deprived if she lives in a household where no adult has completed primary education and no adult can read or write.

14 The WB societal poverty line is a (weakly) relative monetary line whose monetary threshold increases with median income but is never smaller than the extreme poverty line (Jolliffe and Prydz, 2021). Considering individuals whose income is between the extreme and societal poverty lines as moderately monetary deprived is consistent with the view that being absolutely income poor is worse than being only relatively income poor (Decerf, 2017; Decerf and Ferrando, 2022).

> 1 Data-constraints imply that we don’t have information on underweight men. Therefore, our indicator, which we
967|1.000|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed author was given full credit for a publication in the case of a co-authored publication. An _h_ -index of 10 means that the author has 10 publications to his or her name each of which has been cited at least 10 times. The quadratic influence function is defined in eqn (1). The measure _I_<sup>3</sup> is defined in eqn (2). All authors were included in calculating these (rank) correlations however few publications in health economics they had to their name.
Figure 1: Gross enrollment for Lower and Upper secondary grades, in 2015 <!-- Start of picture text --> 60%<br>50.6%<br>50%<br>48.3%<br>42.6%<br>40%<br>33.6%<br>30%<br>27.8%<br>20%<br>10%<br>6.8%<br>0%<br>Lower secondary Upper secondary<br>Sub-Saharan Africa Tanzania Low-income countries<br><!-- End of picture text --> Source: World Development Indicators (accessed on Oct 3, 2019) This low number hides further disparities. Only 6 percent of children from the poorest quintile were attending secondary school (DHS 2016). Further, approximately 29 percent of girls and 34 percent of boys are estimated to drop out of lower secondary school before they complete. Over the last four years, approximately 21 percent of boys and only 16 percent of girls that completed lower secondary schooling went on to upper secondary (Samarrai and Tamagnan 2019).

Figure 2: Percentage gaps in net secondary school enrollment, by wealth quintile, residence, and gender in Tanzania <!-- Start of picture text --> Richest-Poorest<br>Urban-Rural<br>Male-Female<br>-10 0 10 20 30 40 50<br>2004/2005 2010 2015/2016<br><!-- End of picture text --> Source: 2004/2005, 2010 and 2015/2016 Tanzania Demographic and Health Surveys.

3
# **7. Conclusions**

In this paper, we have assessed whether the Turkish Green Card program had a protective impact on beneficiary households during the 2000s – and this time period includes clearly ̳good‘ times for Turkey (from 2003 to 2008) as well as ̳bad‘ times when the country was impacted by the international financial crisis. While the available data sources do not allow us to examine the protective impact both through financial protection and safeguarding health care utilization in parallel, we were able to tease out these impacts separately for different time periods.

The analysis of household surveys indicates that in the period 2003-2008, the rapid roll-out and successful targeting of the Green Card scheme has increased the health insurance coverage of Turkey‘s poor. In the same time period, out-of-pocket health expenditures have also declined slightly, reducing the incidence of catastrophic health expenditures. However, observed small reductions in levels of out-of-pocket expenditures are not causally attributable to the expansion of the Green Card. Similarly, we found a very slight – and statistically not significant – decline in the impoverishing impact of health expenditures in connection with the roll-out of the program.

To assess the protective impact during the ̳bad‘ time, we use a unique household survey fielded during the financial crisis in Turkey to assess the impact of the Green Card in times of economic hardship. Three different methodologies are used to establish the impact of the Green Card on health care utilization during the crisis. Both non-parametric and parametric estimates using the Turkey Welfare Monitoring Survey establish that the Green Card program was an effective and functional safety net, protecting the health care utilization of the poor during the crisis period. Propensity score matching estimates and robustness checks using four different matching techniques – as well as trimming to increase common support -- confirm that access to the Green Card was associated with a positive and significant impact on protecting the health care utilization of poor households through the beginning of the financial crisis.

20
exposure.<sup>3</sup> Both variables are used in the analysis to proxy exposure to flood risk by using two binary variables: 1) if the household reports having been directly affected by floods, and 2) if the household perceives the risk of future flood exposure as high or very high. Appendix Table A.1 provides the precise questions and the definitions of the flood exposure variables for each city. Self-reported disaster data have two main advantages. First, they capture actual household experiences and expectations rather than modeled risk estimations. Flood impacts can differ from street to street depending on elevation, housing quality, access to, and topography, etc., and flood models do a generally poor job of capturing localized flood risk, especially in urban areas.<sup>4</sup> Second, in the context where information on flood risk as defined in flood maps is not readily available, up-to-date or well-known to the public, past experiences of flood and the perception of risk can be better predictors of rent values. The use of self-reported proxies for flood risk may therefore increase the accuracy of hedonic models when used in contexts where buyers (or renters) are not fully informed.

In hedonic analyses, house-transaction prices are ideal to define housing value (Bishop et al., 2020). However, they can be difficult to access even in developed countries. Bishop et al. (2020) provide a list of the countries where microdata on housing transactions is available: it includes several European countries, the United States, Australia, Chile, China, Japan, and the Republic of Korea. Examples of studies that use transaction data in middle-income countries are Argentina (Rabassa and Zoloa, 2016) and Indonesia (Alvarez<sup> ́</sup> and Resosudarmo, 2019). Limited data availability explains why, while the hedonic approach to estimate households’ willingness to pay for a change in an environmental amenity is used extensively by researchers, it is difficult to find studies of
is employed, all measures of her empowerment would be higher, with an increase in empowerment index ranging from 1.5 percentage points in reproductive freedom to 9.5 percentage points in financial autonomy. The second most important explanatory variable is women’s education. Each additional year of education achieved is associated with a 1.8 percentage point increase in mobility, 1.1 percentage point increase in financial autonomy, 0.6 percentage point increase in social participation, and 0.9 percentage point increase in overall empowerment. Regular listening to radio is found to slightly increase reproductive and financial autonomy, while regular TV watching is associated with an increase in financial autonomy and social participation. Being sick during the last 30 days is negatively correlated with mobility but does not have any statistically significant impact on other empowerment indexes, possibly because it may be an imperfect approximation of a woman’s general health.

# **VI. Conclusion**

This paper investigates the causal link between access to electricity and women’s empowerment using a large gender-disaggregated data set from the India Human Development Survey. To measure the multidimensional aspects of empowerment, we use factor analysis to combine an array of information on women’s intrahousehold decision-making and resource allocation into five indicators of empowerment: decision-making ability, mobility, financial autonomy, reproductive freedom, and social participation. We also construct an overall empowerment index based on the five factors.

The analysis shows that getting access to electricity enhances women’s positions on all five dimensions of empowerment and the overall empowerment measure. However, the magnitude of improvement is small for women’s decision-making ability and reproductive freedom. Gaining access to electricity is associated with a 4.6 percentage point increase in women’s decision-making ability on intra-household resource allocation and 2.7 percentage point increase in their reproductive freedom. Women’s bargaining power, primarily involving her own well-being (such as traveling alone, having a bank account, and participating in social groups), increases by 6.9–10 percentage