rafmacalaba/data-use-mentions-extended huggingface dataset:
Where _Yijrt_ is, in turn, the primary schooling completion dummy, an indicator for enrollment status, and the number of completed years of education. _aget_ is a set of dummies for the age of the index individual at the time of the survey, _dhs2006_ is a dummy equal to one for 2006 DHS observations and zero for 2001 DHS observations, and _conflict_by_surveyjt_ is the total number of conflict-related deaths at the district level, per 1000 inhabitants, up to the (Nepali) year in which the bulk of the survey data was collected. The inclusion of region and age-specific dummies for the change in outcome between the two surveys controls comprehensively for potential regionspecific, time-varying confounding factors, allowing these to vary by age group. **_Xijrt_** is a vector of household characteristics (binary indicator for rural location, and dummies for the educational attainment of the household head<sup>3</sup> ). In general, the inclusion of covariates that explain a sizeable share of the variation in the outcome variable tends to increase the precision of the estimate of the treatment effect (Duflo et al., 2008). However, the inclusion of socioeconomic household regressors may cloud the interpretation of the effect of conflict intensity because the latter may have an effect on the former. Therefore, I focus my attention on largely predetermined variables.

# **Marriage**

Given the short time elapsed between the start of the conflict and the latest data available (10 years), it is not possible to estimate the impact of conflict during primary school age on long term marital status. For instance, an individual aged 5 at the beginning of the conflict is only 14 or 15 years old at the time of the 2006 DHS survey. It is however possible to estimate the impact of exposure to conflict during primary schooling age on early marriage, e.g., on the probability of being married by the age of 15 years old.

It is also relevant to analyze the effect of exposure to conflict during core marriageable
4 discusses the importance and policy implications of these results. Section 5 concludes.

# **2. Data and Methods**

# _2.1. High-Frequency Phone Surveys_

This paper uses data from a series of high-frequency phone surveys (HFPS) conducted in six SSA countries. The HFPS data used here have been collected primarily by national statistics offices,<sup>1</sup>

> 1 The Ethiopia HFPS was implemented by a private survey firm, not the national statistics office.

3
**Table 5: Indicators/ data sets measuring data openness**

|**Indicators/Data sets**|**Author**|**% of elements focusing on**<br>**data access**|**Number of countries**<br>**covered(year)**|**MENA countries coverage**|
|---|---|---|---|---|
|1. Open Data Inventory (ODIN)<br>-2018/2019 and 2020|Open Data Watch|50% (5 out of 10)|178 (2018/2019)<br>187 (2020)|17 of 20 in 2018/2019 -<br>Algeria, Djibouti, Egypt, Arab<br>Rep., Iraq, Iran, Islamic Rep.,<br>Jordan, Kuwait, Lebanon,<br>Libya, Malta, Morocco,<br>Oman, Qatar, Saudi Arabia,<br>Syrian Arab Republic, Tunisia,<br>United Arab Emirates<br>All 20 MENA countries in<br>2020|
|2. Open Data Barometer (ODB)<br>– The leadership edition|World Wide Web<br>Foundation|Around 33.3 %|30 (2017)<br>Note: Scope of study was<br>reduced to 30 countries<br>in 2017 to include only<br>countries thatpublicly<br>committed to adopt<br>the International Open<br>Data Charter<br>Principles or the<br>equivalent G20 Anti-<br>Corruption Open Data<br>Principles.|1 of 20 – Saudi Arabia|
|3. Global Open Data Index<br>(GODI)|Open Knowledge<br>Foundation|About 50%|94 (2015)|3 of 20- Iran, Islamic Rep.,
they were pushed into operating an HE as they could not find adequate income-earning opportunities in either wage or agricultural employment. Although a combination of both may be at work, analysis of the ILFS data shows that push factors are more common, especially when an HE is a secondary activity (Table 2.5).

**Table ‎2.5:  Main Reason for Having an HE, by Economic Activity**

||_Primary_<br>|_Activity_<br>|_Secondary_<br>|_Activity_<br>|
|---|---|---|---|---|
|**PUSH FACTORS**|_2000_<br>%|_2006_<br>%|_2000_<br>%|_2006_<br>%|
|Can‟t find other work|44.9|37.2|21.2|18.5|
|Released from employment|3.6|1.7|1.1|1.1|
|Retirement from employment|1.1|0.9|1.2|0.5|
|Family need for more income|24.7|31.6|43.5|55.9|
|**PULL FACTORS**|||||
|Good business opportunities|6.6|11.8|8.5|9.0|
|Does not require much capital|7.8|7.4|10.2|7.2|
|Low production cost|0.6|0.4|0.3|0.5|
|Desire for independence|2.1|2.0|0.9|1.4|
|Free choice of work hours and place|2.3|1.6|3.2|1.4|
|Can combine business w/ housework|2.4|3.2|5
</sup> Our evidence also shows that rabi precipitation, relatively minor as it is, has no significant productivity impact, hence its exclusion from all subsequent specifications. Quadratic terms, shown in column 4, are also not significant for seasonal temperature, but there are diminishing marginal returns to kharif season precipitation.

Results in column 5 of Table 4 allow for interactions of seasonal weather variables with the proportion area irrigated in the district.<sup>24</sup> Consonant with the cross-sectional findings, the

> 23Denoting gross annual revenue by _Y_ , revenue per hectare by _y_ , and temperature by _T_ , one can show that

where subscripts denote kharif ( _K_ ) and rabi ( _R_ ) specific values of the relevant variable. Our estimate of _YK/YR_ comes from the 59th round of the NSS, which gives gross revenue by season at the household level for the 2002-03 crop-year. Since the left-hand side is around two, as is _YK/YR_ , _d_ log( _yK_ ) _/dTK_ must be close to _d_ log( _yR_ ) _/dTR._

> 24This variable is calculated from the 59th round of the NSS, as these data have the most complete district-wise coverage and are measured at the midpoint of the relevant period. The correlation between this irrigation ratio and a corresponding one calculated from Ministry of Agriculture data on the subset of districts for which the latter is available is about 0.8.

14
to measure refugee QOL. The first dimension is the overall experience of life, or "life satisfaction", and the second is the material living conditions.<sup>3</sup> The "life satisfaction" indicator assesses people's subjective well-being. It is popular for use in studies of refugees because it requires respondents to reflect on, and make an overall assessment of, their life happiness, including wealth, security, and hopes for the future. "Life satisfaction" tends to be an overall reflective evaluation that uses a Likert response scale of between 0 to 10, where 0 means not satisfied and 10 completely satisfied. However, it is left for the respondent to define what "satisfaction" means and the scaling of his/her satisfaction; thus, it is a subjective indicator of well-being.

"Material living condition" captures households' objective living conditions and opportunities, including material deprivations and housing conditions that directly affect their QOL. Material deprivations refer to the level at which households are able to have the consumption goods and services needed in a society at a given time. Several indicators could measure material deprivations. One typical indicator is the ability or inability for households to meet basic food needs at above the national abject poverty level. Other measures of deprivations may include counting the number of household assets, such as beds, air conditioners, and cooking utensils. The availability of housing and housing conditions can also be captured by calculating overcrowding and satisfaction with accommodation services, such as water and electricity.

# 4. Data and Identification Methodology

## 4.1. <mark>The survey</mark>

<mark>The World Bank Development Economics Data group conducted the Syrian Refugees and Host Communities Survey (S</mark> RHCS <mark>) in 2015 by surveying registered and unregistered refugees (Krishnan, Munoz, Riva, Sharma, & Vishwanath, 2019). The survey was designed to produce comparable findings on living conditions and quality of life of Syrian refugees and host
<mark>how these two types of crises interact and their effect on poverty – Potentially whether there is sequencing or if inflation, for example, is the channel through which much of the social costs are borne. Furthermore, while we look at differential impacts depending on the length of default, there are other characteristics of a default episode that can also affect the results, such as the size of the haircut and the number of restructurings. It is our suspicion as well that with a more granular study controlling for war, as opposed to excluding incidents of war, the results would show interesting heterogeneity – though it is hard to imagine household survey data being available for war years. Lastly, the decision to default is always a political decision. It would be useful to understand how different political regimes fare in case of default and how the social costs of default differ depending on the political regime.</mark> 33
. For all these reasons, inevitable gaps in the data emerge (see table 2).

Three conventions are adopted for constructing missing data. The one that is ultimately used depends on how many data are missing and for which decades these data are missing relative to the decades for which data are available.

MISSING IN-BETWEEN DECADES. Where data are missing for a particular decade but are available for the decade before and after, a linear trend is assumed between the earlier and later bilateral data. In total, 86 country-years of data were interpolated using this method.

MISSING BEGINNING OR END DECADES. Where the data are missing at the beginning or the end of the time period, the destination country is assumed to have the same bilateral migrant composition as in the decade closest to the missing period. The bilateral shares from the closest decade for which data are available are applied to the destination country‘s total number of migrants for the missing decade. The information comes from one of two sources. In some cases, the census provides the total number of migrants without any bilateral information. If these data are not available, the total from the closest decade is taken and adjusted for growth in migration. The growth rates are taken from Trends in International Migrant Stock, which details total migrant stocks for 13
, including Fiji, Kiribati, Marshall Islands, etc. The list includes seven microstates, economies with fewer than 1,000 residents, and 49 hectares of land, such as Nauru, Tuvalu, Liechtenstein, etc. Many of these countries have severe data constraints that would have made estimation difficult with more elaborate formulations and data requirements.<sup>18</sup> Table 1: Sample size distribution in the estimation

|Sample Size|No. of countries|Percent distribution|
|---|---|---|
|49(full sample)|126|66.0%|
|40 - 48|8|4.2|
|30 - 39|19|9.9|
|20 - 29|38|19.9|
|Total|191|100.0%|

Source: Authors' calculation. Note: Full sample = 1970-2018.

> 17 The terms – countries, territories, and economies – are used interchangeably. We include any that have national accounts data in the WDI and UN sources.

> 18 Even so, it was not possible to estimate elasticities of some countries. A few of these are countries that have experienced severe conflicts – such as Afghanistan, Somalia, Sudan, and South Sudan. For some small countries - like Cura ç ao, Monaco, New Caledonia, Palau, Nepal etc., flat relative prices are registered for an extended or entire period. In those instances, the countries employed exactly the same implicit price indices for GDP, exports, imports, and (therefore) domestic goods. Hence, the estimated elasticities were essentially zeros. These countries were excluded.

14
The analysis is also based on information from the Mexican Federal Competition Commission and assumes that the mobile telecommunication market behaves as an oligopoly (OLI-NASH) and that corn markets mimic a partial collusive oligopoly (PCO). Then, using WELCOM, data from ENIGH, and assumed parameters of these two markets from recent literature, the analysis shows that expanding competition from 4 to 12 firms in the mobile telecommunications industry and reducing the market share of the dominant oligopoly in corn products from 31.2 percent to 7.8 percent would produce a combined reduction of 0.8 percentage points in the poverty rate and of 0.31 points in the Gini coefficient. The contribution of each market to poverty reduction would be: 0.11 percentage points with mobile telecommunication and 0.66 percentage points with corn products.<sup>11</sup> The paper is organized as follows. Section 2 briefly describes the theoretical models adapted and included in the simulation approach. It also describes the options available for estimating the effects on household welfare. Section 3 introduces the simulation technique and describes the data requirements. Section 4 presents the rationale of selecting Mexico as a relevant case study, as well as some stylized facts on the degree of concentration of the industries selected, that is, mobile telecommunication and corn products, and some descriptive statistics, including data on household expenditures on categories linked to the selected markets. Section 5 discusses the parameters used in the analysis, the main results, and the limitations of the analysis. Section 6 concludes and lays out potential extensions for the simulation approach.

# **2. Conceptual framework**

This section presents an overview of some of the early theoretical approaches to studying the distributional effects of market concentration. Then, it discusses three main theoretical models used in WELCOM to simulate alternative market conditions: (a) the monopolistic structure, (b) the oligopolistic structure (Nash equilibrium), and (c) the PCO structure. These models are the theoretical foundations of the simulation method presented
**Figure 1: Average Per Hectare Tax Rates, Rural Land Use Fee and Agricultural Income Tax, Amhara, Oromia, and SNNP Regions**

<!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br><0.5 0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4 4-4.5 4.5-5<br>Total land area (ha)<br>Amhara - non PSNP kebeles Amhara - PSNP kebeles<br>Oromia rain and irrigation farmers Oromia rain dependent farmers<br>SNNP high value crops SNNP others<br>Per hectare tax rate (Birr/ha)<br><!-- End of picture text --> _Source_ : Amhara Regional State, Proclamation (No.161/2001), Proclamation to Amend Rural Land Use Payment and Agricultural Income Tax of Oromia Regional State’s Proclamation (No.99/2005), SNNP Regional State: A Revised Proclamation to provide for rural land use fee and agricultural activities income tax (No. 122/2008). _Note_ : The average birr per hectare tax rate is calculated by dividing the total tax by the mid-point of the landholding classes; this is similar to the method used in Hill et al. (2017). See Appendix 1 for the tax schedules for the three regions.

The Bureau of Agriculture and Rural Development provides farmers’ names and area of rural landholdings in the land registry to the Revenue Bureau (ORS 2005). The Revenue Bureau or the chairman of the _kebele_ (ward) peasant association delegated by the Revenue Bureau collects the taxes from the farmers, who have until April 30 E.C. to make the payment (ORS 2005). Farmers are required to notify any changes in
, David Simon, and Wendy Holmes** , “Male involvement in reproductive, maternal and child health: a qualitative study of policymaker and practitioner perspectives in the Pacific,” _Reproductive Health_ , July 2016, _13_ (1), 81.

- **Desai, Sonalde and Reeve Vanneman** , “India Human Development Survey-II (IHDS-II), 2011-12: Version 6,” Technical Report, ICPSR - Interuniversity Consortium for Political and Social Research, New Delhi, India 2015.

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- **Field, Erica, Seema Jayachandran, Rohini Pande, and Natalia Rigol** , “Friendship at Work: Can Peer Effects Catalyze Female Entrepreneurship?,” _American Economic Journal: Economic Policy_ , May 2016, _8_ (2), 125–153.

- **Gallup** , “Americans Satisfied With Number of Friends, Closeness of Friendships,” March 2004.

- **Godlonton, Susan and Rebecca Thornton** , “Peer effects in learning HIV results,” _Journal of Development Economics_ , January 2012, _97_ (1), 118–129.

- **GoI** , “National Family Health Survey, 2015-16,” Technical Report, Ministry of Health and Family Welfare, Mumbai 2017.

28
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Policy Research Working Paper 10404

# **Abstract**

There is increasing recognition that deficiencies in the public transport system impact men and women differently. While transport systems have been shown to play a significant role in women’s participation in the labor force globally, this topic has been little explored in the Middle East and North Africa. This paper examines the effect of the spatial accessibility, availability, and safety of public transportation on women’s labor market outcomes in three capital cities in the Middle East and North Africa—Amman in Jordan, Beirut in Lebanon, and Cairo in the Arab Republic of Egypt. The analysis uses three types of data collected for each city in 2022, namely, household mobility surveys, transit network data, and built environment audits. The paper investigates how the spatial accessibility of jobs in each city, the availability of public transportation close to residential locations, and the safety of public transit stops affect the labor force participation of women and their likelihood of employment. The main findings are that: (a) accessibility, availability, and safety appear to impact women’s labor force participation differentially in each city, and these impacts also vary by income level; and (b) although accessibility, availability, and safety appear to impact women’s labor force participation, they have overall little impact on women’s employment probability. The paper takes these two findings to imply that: (a) a one-size-fits-all-women solution is not appropriate when designing public transport systems; and (b) although public transport plays a critical role in improving women’s access to employment opportunities, complementary actions are needed to translate these gains into gainful employment.

This paper is a product of the Transport Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors
**Figure 16. Components of government revenue, upper‐middle income countries (% of GDP)**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Indirect Taxes Social security contributions Direct taxes Other taxes Non tax revenue<br><!-- End of picture text --> Source: Authors based on CEQ Data Center. Notes: Data for Brazil 2019 covers only Central Government.

Brazil's comparative results regarding middle-income countries show that it is a country that reduces inequality the most when including all taxes and transfers and the education and health benefits (Figure 17). Brazil in 2009 was less successful in reducing inequality, excluding education and health, even when the country's results were above the average. In contrast, the drop in the Gini coefficient in 2019 is lower when including only direct taxes, indirect taxes, and direct transfers; still, it is among the three highest.

**Figure 17. Inequality reduction, upper‐middle income countries (Gini coefficient points) From Market income plus pensions to Consumable From Market Income plus pensions to Final**

<!-- Start of picture text --> 12.0<br>10.0<br>Brazil (2019),<br>6.4<br>8.0<br>6.0 Brazil (2009),<br>3.1<br>4.0 3.3<br>2.0<br>0.0<br>consumble income (Gini points)<br>Gini coefficient reduction from Market Income plus pensions to Fiji (2019)<br>Turkey (2016) Paraguay (2014) Guatemala (2014) Peru (2011) Armenia (2011) Russia (2014) Moldova (2017
# **I. Introduction**

Understanding the determinants of social distancing is central to addressing both the medical and economic aspects of COVID-19.<sup>4</sup> On the one hand, reducing interactions among people is critical to reducing the propagation and a variety of non-pharmaceutical interventions (NPIs), such as closure of nonessential businesses, stay-at-home orders, or school closings, have been put in place to this end, with some success.<sup>5</sup> While there is controversy around whether this should be the goal in developing countries as well (BarnettHowell and Mobarak 2020, Loayza 2020), there is also concern about whether such measures would work: government capabilities to enforce may be weaker, and resistance may be higher since the trade-off with livelihood is harsher. At the other extreme of the cycle - where the debate is when to loosen NPIs as it is in several advanced countries – preliminary evidence from Wuhan suggests that when opened, mobility and economic activity may not respond quickly.<sup>6</sup> Similarly, recent polls suggesting that 58% of Americans are concerned that restrictions will be lifted too soon raise the question of how much of an impact opening will have in practice and hence the shape of the recovery, whether V or U.<sup>7</sup> This paper uses Google mobility data to explore which factors are proving important during the 2020 COVID19 outbreak in the United States and globally. In all but the poorest countries, it confirms that NPIs can be effective, but that voluntary de-mobilization on the part of the population is much more important,-driven by fear or perhaps a sense of social responsibility. This suggests that much social distancing will happen regardless of the presence of restrictions and that NPIs may often function more like a coordinating device among increasingly predisposed individuals than repressive measures per se. We also confirm a more direct link of this voluntary effect using data on restaurant reservations in the United States and movie releases and revenues