rafmacalaba/data-use-mentions-extended huggingface dataset:
country. The prevalence rates at the individual and household (HH) levels are calculated using the formulas: the formulas: P P P =<sup>N</sup> where c denotes a country. cc<sup>NPoPPaaNNPPrraa wrrh ooNNnnccrrwwoonnPaawwowwccNNPPrrddcc</sup> PPnnccPP P rrPP cc TToorrP ppooppNNP rrwwoonn H P P P =<sup>N</sup> cc PPnnccPP P rrPP N N P o<sup>NPoHHooNNaaPPhooPPawrrh PPrr PPPaarr oonnPP PPaaNNPPrr wrrh ooNNnnccrrwwoonnPaawwowwccNNPPrrddcc</sup> cc hooNNaaPPhooPPa

# **2.4 Human Development Indicators**

We compare human development indicators across groups by functional difficulty status to establish the size of the gap that may be associated with disability, i.e. the disability gap or inequalities associated with disability. Results are presented in graphs and tables, often in appendix tables. Double disaggregation tables by disability and a demographic characteristic (sex, rural/urban, age group) are available from the authors upon request (Appendices A to C). In tables, the disability gap and its statistical significance is typically noted in a separate column. Statistical significance is based on a t-test (*, **, and *** at the 10%, 5% and 1% levels respectively). As indicators reflect achievements (e.g. employment population ratios, asset ownership) and deprivations (unemployment rate, food insecurity, exposure to shock), positive/negative differences will not consistently reflect a disability gap in achievement/deprivation indicators, respectively. We also use and graph the ratio for a given indicator by dividing the indicator for persons with disability with the indicator for persons with no disability. This is often referred to as a relative rate or as a parity index (e.g. UNESCO 2018, OECD 2003). For instance, for the relative employment rate, a relative employment rate at or near one reflects a similar situation of persons with and without disabilities in terms of the share who are employed.

The indicators under study are
/ budget/ Integrated Survey, Agriculture survey, Labor Force Survey,<br>Health/Demographic survey, Business/establishment survey. Source: NSO websites,<br>World Bank microdata library, ILO microdata library, IHSN microdata library|
||Availability of Civil Registration and Vital Statistics (CRVS) indicator. An ideal<br>indicator would include a score based on the density of administrative data available in<br>sectors of social protection, education, labor, and health. However, social protection,|
|Dimension 4.2:<br>administrative data|<br>education, health, and labor admin data indicators not included because of lack of<br>established methodology. While several promising sources for administrative data from<br>the World Bank’s ASPIRE team, WHO, UNESCO, and ILO have been identified, the<br>were not included due to incomplete coverage across countries. Further research and<br>data collection effort would be needed to fill in this information, so that a more<br>comprehensive picture of administrative data availability can be produced.|
||Geospatial data available at 1st Admin Level. This data source from Open Data Watch<br>|
|Dimension 4.3:<br>geospatial data|focusing on data availability at the sub-national level provides a partial understanding<br>of a country’s ability to produce geospatial data. A research and data collection effort is<br>needed to develop an more comprehensive global database of the availability of key<br>geospatial indicators.|
|Dimension 4.4:<br>Private/citizen<br>generated data|Not included because of lack of established methodology. Currently no comprehensive<br>source exists to measure the use of private and citizen generated data in national<br>statistical systems, and this should be another area where more data collection is<br>needed by the international community.|
||This indicator is
Note. Results from linear probability model. Clustered standard errors at the country-region-size-sector in parentheses. Specifications (1)-(2) and (4)-(5) include country dummies. Specifications (3) and (6) include country-region dummies. All specifications control for the severity of the crisis based on Google mobility (See section 2).<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. ‘Electrical outage (region)’ refers to the share of firms experiencing electrical outages during the previous fiscal year in the same sub-national region. ‘Finance constraint (region)’ refers to the share of firms identifying access to finance as a major constraint in the same sub-national region. ‘Banking account’ refers to having checking or saving account.

Table 2: Factors associated with increasing in online sales A second hypothesis is related to access to finance and the fact that many firms were financially constrained to invest in digital technologies to respond to the pandemic. While the data set has 21
any rate, assumes that this return is constant across districts. The resulting estimates (first column) indicates that, on average, an irrigated plot is 37% more productive than a rainfed plot within the same district. For the second stage, we regress the 542 district fixed effects on district-specific determinants of land productivity, weighting by the number of plot observations per district. Standard errors are calculated using Conley’s (1999) spatial GMM estimator with bandwidth set at 8 degrees lat./long. ( _≈_ 800 km).

The second stage of Model I does not control for any geographic variables apart from climate, specifically average annual temperature and precipitation (see Table 1 for descriptive statistics). In addition, we allow weather variability, in the form of the coefficient of vari-

> 16Aside from irrigated and unirrigated land devoted to seasonal crops, the only other category of agricultural land specified in the survey is ”orchards and plantations (including forests),” which constitutes 4 percent of agricultural land area. Due to its heterogeneous nature, we exclude this category from our analysis. Privately held grassland dedicated exclusively to pasture also appears to be rare in India, as livestock are typically fed fodder or grazed in fallow fields or village common areas.

> 17Districts with high land values correspond fairly closely to those with high median net revenues per hectare in the 2002-03 crop year based on farm-level data from the 59th round of the NSS (The district-level correlation coefficient is 0.48). As noted, however, net revenues are affected by weather realizations and in 2002-03 India experienced an extremely poor monsoon, though rainfall in the northeast was close to normal.

11
of the national health system regarding both access and use of available services and

12 In the literature, this is known as the Lambert conundrum (Lambert 2001; Lustig & Higgins, 2018). Taxes, for instance, can be regressive according to the Kakwani index but when combined with transfers make the system more equalizing than without the regressive taxes. For a thorough discussion see Enami, Lustig, and Aranda (2018). 13 See Higgins and Lustig (2018).

14 Survey data for 2020 and 2021 was available when this paper was written. However, there are several reasons related to the unusual COVID-19 conditions in Brazil that make conducting a fiscal incidence analysis in those years less desirable. First, the years 2020 and 2021 are not considered ‘typical years’ as the pandemic had major disruptions on the economy and the labor income. The employment rate of the working-age population dropped by 5.4 percentage points between 2019 and 2020. At the same time, the government put in place emergency measures including a cash transfer program called “Auxilio Emergencial” (AE) that reached over 67 million people (circa 42% of the country’s adult population) with a generous payment of R$600 (55% of the minimum wage). Second, in 2021, AE was substantially reduced in terms of coverage and benefits, while other emergency measures expired. The labor force participation (at 60.6 percent) was far below its pre-pandemic levels of 63.9% and unemployment rates were at its highest point (14 percent) in a comparable series since 2012. Moreover, the social distancing measures established to contain the virus spread challenged the data collection by the National Statistical Office of Brazil (IBGE). Consequently, the response rates in both 2020 and 2021 were much lower than in previous years and IBGE undertook a series of mitigation measures to preserve the data (IBGE 2021, IBGE 2022). The effects of these atypical situations over income, poverty and inequality estimates were documented in Lara Ibarra
the country and city level), of city governance, and of city performance (access and quality of service delivery). This city database integrates existing data with new data gathered specifically for this research project.<sup>3</sup> The paper is organized as follows. Section 2 provides a discussion of conceptual and definitional issues on city globalization, providing a partial survey of the literature. In Section 3 we discuss the issue of urban governance in the context of globalization and review some of the relevant literature to capture the dynamics of city performance. Section 4 provides simplified hypotheses on the globalization and governance nexus in explaining city-level performance in terms of service delivery, includes a working definition of urban governance, and presents a reduced-form model specification to test key hypotheses. Section 4 also describes the extended city level dataset we have compiled, comprising data on 412 cities in 134 countries, from which we make use of over 35 variables.<sup>4</sup> In this section we also present in brief the results, both in terms of simple summary charts synthesizing the uncontrolled correlations, as well as the econometric specifications and the synthesis of the econometric results of the determinants of city performance. Given the extensive set of variables in the dataset, and multiple econometric specifications, we present synthesis tables and graphs summarizing the main econometric results.

In Section 5 we present the results of the preliminary empirical investigation of the potential determinants of city governance. Future research questions are dealt with in the concluding section, where we also present emerging policy implications from this analysis.

# **2 City Performance in the Context of Globalization**

There are two main threads in the literature that treat the subject of globalization of cities, which mostly refer to the term “world city” to describe the phenomenon. We take the theoretical underpinnings of this definition from the work of Doel and Hubbard (2002), who clarify key definitions and develop the conceptualization of globalization of cities. The two conceptualizations of world cities that we
<br>0.06||||
|**II. Village-level Characteristics in 1987-88 (****_N_= 82)**||||
|**Inequality of Per Capita Household Income:**||||
|Gini Coefficient<br>0.20|0.14|0.19|0.28|
|Mean Log Deviation<br>0.07|0.03|0.06|0.12|
|90-10 Ratio<br>2.56|1.91|2.46|3.39|
|**Other Characteristics:**||||
|Household Education<br>5.53|4.00|5.66|7.13|
|Share of Income from Agriculture<br>0.54|0.25|0.59|0.74|
|Share of Income from Wages<br>0.20|0.07|0.16|0.37|
|Share of Income from Family Enterprises<br>0.15|0.05|0.13|0.30|
|Village Total Tax Revenue Per Capita<br>0.46|0.06|0.31|0.92|
|<br>Village Total Public Expenditure Per Capita<br>0.46|0.06|0.30|0.90|
|<br>Mountainous Terrain<br>0.26||||
|Hilly Terrain<br>0.39||||
|Near a City<br>0.06<br>_Notes:_<br>1) Household-level statistics are calculated over the 3,424 panel house<br>statistics are calculated over all available households in the 82 villages|holds, while<br>in 1987-88|the village-<br>(4,847)|level|
three phases: the demobilization, the reinsertion and the reintegration. The demobilization phase started with disarmament, followed by the transfer of ex-combatants to a demobilization center. Ex-combatants spent eight days in the center, attending training on economic strategies and opportunities, HIV/AIDS, civic responsibility, as well as peace and reconciliation.

As part of the reinsertion phase, demobilized combatants received a cash allowance worth 18-month salary, paid in four installments over a period of eighteen months. Demobilized combatants received the first reinsertion payment when leaving the demobilization center. Called the Transitional Subsistence Allowance (TSA) by the World Bank, the reinsertion money was dedicated to _“enable the ex-combatants to return to their community and to sustain themselves and their families for a limited period following demobilization”_ (The World Bank Group, 2004). The total amount of the cash allowance increased with military rank, starting from a minimum of FBU 566,000 (US$ 515).<sup>6</sup> The average benefit amounted to FBU 644,000 per demobilized combatant (US$ 586). Data from our survey in 2010 shows that civilian households consumed on average about FBU 190,000 (US$ 173) per adult equivalent per year, which is about one third of the minimum cash allocation to FNL rebels.

Finally, the reintegration phase consisted of a one-off in-kind transfer worth FBU 600,000 (US$ 545). The ex-combatants could choose from a range of options, including vocational training, support for agro-pastoral activity, and start-up material for a small business or a construction project. This phase was launched in September 2006 for the CNDD+.<sup>7</sup> Of the 23,000 CNDD+ beneficiaries, 85% had received the reintegration support by December 2008. This phase was just starting for the FNL ex-combatants at the time of our 2010 survey.

Some rebels had ties with the factions, but did not benefit from the DDR program. A list of criteria was established in
45</sup> Again, we did not find any systematic 44 As robustness, we also tried using the level of the market power variable instead of the indicator. We tried both the level and the log of the inverse export supply elasticity (and their changes due to the customs union), but overall the variable was less significant or had the wrong sign in some cases (and we also tried winsorizing the elasticities by setting the extreme values at the values at the 5th and 95th percentiles of the distribution). However, our results regarding the effect of preferential liberalization and the effects of including TTBs in the estimations remain robust. The results are also robust to working with the elasticities at the 4-digit HS level from the estimates from Nicita, Olarreaga and Silva (2015), concorded similarly, but overall the market power variable was less significant in some cases. Furthermore, we also tried adding the interaction of the market power variable (in levels, logs or the indicator) with the preferential liberalization variable, but the interaction was not significant.

45 Since we cannot use an IV-ordered probit when the endogenous regressor is not continuous, we do not employ the medium-high inverse export supply elasticity indicator. Instead, we use the level of the inverse export supply elasticity, and we set the top 5 percent of the elasticity data to the value at the 95<sup>th</sup> percentile of the distribution, and the bottom 5 percent to the value at the 5<sup>th</sup> percentile of the distribution, to deal with extreme values of the estimated elasticities.

27
174|0.065|5051|
||(1.000)|(0.044)<sup>_∗∗∗_</sup>|(0.057)<sup>_∗∗∗_</sup>|(0.050)||
|||[0.026]<sup>_∗∗_</sup>|[0.010]<sup>_∗∗∗_</sup>|[0.404]||
|(3) Dwelling materials index|0.000|0.053|0.020|0.087|5052|
||(1.000)|(0.053)|(0.060)|(0.058)||
|||[0.875]|[1.000]|[0.404]||
|(4) Total consumption expenditure|0.000|0.019|_−_0.027|0.066|5053|
||(1.000)|(0.045)|(0.050)|(0.055)||
|||[1.000]|[1.000]|[0.404]||

_Notes:_ Variables are control-group normalized at endline (z-scored). Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A27
more persistent in the Far East and Siberian regions, which border China and Central Asia (Figure 21); but GRP per capita is not the lowest, suggesting that poverty in the Far East and Siberia coexists with opportunity. In other words, the distribution of income in the Far East and Siberia seems to be more unequal; in the North Caucasus it seems to be more equal but lower.

As expected, the relationship between the poverty rate and real per capita GRP is negative (Figure 22). Only two outliers are identified, corresponding to areas in the Southern and Siberia federal regions where the poverty rate is considerably higher than would be expected for the value of real per capita GRP.

# **INBOUND FDI IN RUSSIA’S REGIONS**

The regional distribution of FDI is highly concentrated, although it has recently become more even. Driving the concentration are both the geographical concentration of oil and gas resources and the concentration of business activity in Moscow and St. Petersburg. As measured by the HHI,<sup>9</sup> the distribution of FDI across Russia’s 83 regions is significantly more concentrated than the distribution of GDP or population, though not as concentrated as the distribution of mineral exports (Figure 23).The share of the top three federal regions in Russia’s FDI inflows fell from 69 percent in 2002 to 48 percent in 2011. In both years, the largest FDI flows were to Moscow city and Moscow oblast in the Central District and Sakhalin Oblast in the Far East District.

> 8 There are no evident outliers at the bottom of the distribution, except for the two poorest, Ingushetia and Chechnya.

> 9 The Herfindahl index is calculated as the sum of squared market shares of the variable of interest and has a maximum value of 100 if the distribution is completely concentrated in one region.

12
Policy Research Working Paper 10126

# **Abstract**

This paper examines the Arab Republic of Egypt’s labor market transition dynamics post–Arab Spring based on the two most recent rounds of the Egypt Labor Market Panel Survey conducted in 2012 and 2018. In addition to providing disaggregated-level analysis by examining labor market transitions by gender, education, and age groups, the paper provides a cross-country, cross-regional perspective by comparing Egypt’s labor market transitions with Mexico’s, relying on data from the Encuesta Nacional de Ocupación y Empleo. To match the span of Mexico’s transitions (which are measured over a one-year period) and Egypt’s (which are measured over six years), the analysis uses Monte Carlo simulations of repeated discrete-time Markov chains. Based on these results, the Egyptian labor market appears to be highly rigid compared to the Mexican labor market, which instead shows a large degree of dynamism regardless of individual initial labor market states at baseline. Auxiliary regression analyses focusing on transitions to and from the dominant absorbing labor market states in Egypt —public sector employment for both genders, nonparticipation for women, and the informal sector for men—show that having a post-secondary education is associated with a lower probability of remaining out of the labor force for women who were already out of the labor force at baseline, while being married at baseline is found to be a significant predictor for women to stay out of the labor force if they were already so. Among men, the better educated are found to be more likely to secure formal employment, be it in the public or private sector, and are more likely to keep their public formal jobs once they secure them.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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:/
||0.9992|||0.9985|||

_Note: *** significant at 1% level, ** significant at 5% level, * significant at 10% level. RE - random effects generalized least squares estimation method, POOLED IV 2SLS - pooled two stage least squares procedure with instrumental variables on panel data; robust standard errors utilized. The detailed description of all the models applied in our analyses is given in Annex 4._ _Source: OECD, Eurostat, World Bank, Heritage Foundation, IZA, own calculations._

# **_Table 6. Results of Model 4 – dependent variable SELFEMPL_**

||S<br>|1 – Mo<br>|del 4.1<br>||S2 – Model 4.2<br>||<br>|S3 – Model<br> <br>|4.3<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||Tota||Old Europe|Total|Old Europe|NMS|<br>Total|<br>Old Euro|pe|NMS|
|EPL|-0.5499||0.9708 *|83.5359 **|-339.8728 **|902.4133|***<br>2.1267|*<br>1.7783|**|2.0232|
|MW|-0.0558||0.2647|-0.7486|0.3260|1.9215|<br>0.0258|<br>-0.1448||1.2082|
|TU|-0.0967|**|-0.0381||||0.0723|*<br>
. In response, ReproSalud organized workshops for women and men on gender issues, carried out community awareness campaigns and established a microcredit program for women. By 2002, ReproSalud had reached over 123,000 women and 66,000 men. Qualitative and quantitative evaluation data suggest that the community-based, PLA approach had a positive impact on attitudes and behaviors related to GBV (Rogow and Bruce 2000; Ferrando, Serrano, and Pure, 2002, cited in Boender et al., 2004). The quantitative evaluation (using community based surveys) was complicated by the fact that project coincided with a time of strong investment by the Ministry of Health, which made it difficult to isolate the project’s impact. Perhaps as a result, gender-equitable attitudes and practices increased significantly in both intervention and control communities, though improvements in program sites were slightly higher. The qualitative data suggested a much greater difference in intervention and control sites and documented dramatic changes in social relations and men's behavior. Respondents spoke at length about decreased alcohol consumption, domestic violence, and forced sex in all intervention villages studied. In the words of one 35 year-old woman, " _Before, they brutally forced sex. They hit, especially when they were drunk. Now no more"_ (Rogow and Bruce, 2000, page 20).

# **2.3.6   Individual behavior change strategies**

Many other programs have attempted to produce individual behavior change (rather than change community-level norms) by working with individual men and boys. White, Greene
# **3.2. Methodology and step-wise computational process**

To estimate the number of people who are exposed to unsafe air pollution levels, this study follows a computational process in four main steps, which are outlined in this section. For further details, refer to Rentschler and Malhab (2020), who offer a detailed description of the stepwise computational process that is replicated in this study.

_Resample the PM2.5 data:_ The air pollution map is resampled to ensure that pixels align with the gridded population density map.

_Define air pollution risk categories:_ While the air pollution map offers average annual PM2.5 concentration levels along a continuous scale, the values are aggregated into six risk categories as summarized in Table 1. These risk categories are defined in line with the air quality guideline (AQG) level by the WHO (2021). The WHO recommends an annual PM2.5 AQG level of up to 5 μg/m. For countries with PM2.5 concentrations above this threshold, it recommends interim targets at 10, 15, 25, and 35 μg/m<sup>3</sup> , which correspond to a linearly increasing mortality rate (Table 1). At higher concentrations, the concentration-response function of mortality may no longer be linear. The epidemiological evidence underpinning these AQG thresholds for PM2.5 is discussed in a systematic review by Chen & Hoek (2020). Each 1 degree cell of each country is assigned one of the six risk categories. This is repeated for the world’s landmass of 149 million square kilometers, which implies the processing of about 300 million data points.

**Table 1. PM2.5 concentration thresholds**

|**Risk**|**classification**|**PM2.5 concentration**|**Mortality**|
|---|---|---|---|
|**Safe**|**No/minor risk