rafmacalaba/data-use-mentions-extended huggingface dataset:
market creation, in Christensen et al (eds.) Emissions Gap Report 2018, Nairobi: United Nations Environment Programme.

- [42] Moody‘s. (2018): Sovereign green bond market on course for critical mass, but challenges remain. Sector in Depth: Sovereign and Supranational. Press Release 09/07/2018.

- [43] Norberg-Bohm, V. (2000): Creating Incentives for Environmentally Enhancing Technological Change: Lessons From 30 Years of US Energy Technology Policy. Technological Forecasting and Social Change, 65(2), 125-148.

- [44] Nordhaus, W. (2008): A Question of Balance. New Haven: Yale University Press.

- [45] Organisation for Economic Cooperation and Development (2017): Mobilising Bond Markets for a Low-Carbon Transition, Green Finance and Investment. Paris: OECD Publishing

- [46] Organisation for Economic Cooperation and Development (2018): Taxing Energy Use 2018: Companion to the Taxing Energy Use Database. Paris: OECD Publishing.

- [47] Orlov, S., E. Rovenskaya, J. Puaschunder, and W. Semmler (2018): Green Bonds, Transition to a Low-carbon Economy, and Intergenerational Fairness --- Evidence from the DICE Model. . IIASA Working Paper, Laxenburg, Austria.

15
biases in the estimates of incomes, and has particularly serious implications for estimates of agricultural incomes and incomes from other self-employment which are best defined in annual terms. The comparability of consumption, on the other hand, was compromised by the use of a much shorter consumption module in the APIS. 1 " The APIS uses a two-page module identifying only major categories of consumption while a detailed forty-page consumption module is used in the FIES with a detailed coverage of items within categories. A shorter consumption module generally introduces a downward bias in measured consumption levels (see, Jolliffe, 1999, for instance). Thus, _ex post_ it is virtually impossible to separate out how much of any observed decline in consumption would be attributable to a real crisis-related welfare shock versus how much is simply on account of measurement error related to the use of a shorter module.

# _3.2._ **_Direct self-reported measures of shock_**

In view of this problem, we explored other ways of ascertaining welfare impact of the crisis. Fortunately, a separate section of the APIS survey also included some direct questions on the crisis, where households were asked if they were adversely affected by the crisis in different ways. In particular, five potential shocks were identified. Households were asked: "During the past six months, did the following problems affect you and your family?" i) increasing prices of food and other basic commodities, ii) loss of job within the country, iii) loss of job due to retrenchment of migrant/overseas workers of the family, iv) reduced wages, v) drought or "El Ninio".

"There is some limited abbreviation of the income module too in the APIS, but for the most part, the income modules of FIES and APIS are comparable.

7
<mark>WPS3559</mark>

# Decomposing Changes in Income Inequality into Vertical and Horizontal Redistribution and Reranking, with Applications to China and Vietnam

## by

## Adam Wagstaff

Development Research Group and East Asia & Pacific Region, The World Bank, Washington DC, USA

### **Abstract**

It is acknowledged that the lack of any systematic link between growth and income inequality does not necessarily mean that economic growth is not accompanied by major changes in the underlying income distribution. This paper employs a method devised to decompose the redistributive effect of a tax to analyze the extent to which vertical redistribution associated with changing incomes over time is offset or reinforced by horizontal redistribution and reranking. The paper uses panel data from China and Vietnam over a period when both countries grew spectacularly as they transitioned from planned to market economies, and yet experienced smaller annual percentage increases in income inequality. The results suggest that substantial amounts of horizontal redistribution and reranking in both China—and to a lesser extent Vietnam—more than offset pro-poor vertical redistribution. Without the horizontal redistribution and reranking, the Gini coefficient for China might have fallen between 1989 and 1997—substantially so.

**Author’s contact details:** The World Bank, 1818 H Street NW, Washington, D.C. 20433, USA.  Tel. (202) 4730566. Fax (202)-522 1153. Email: awagstaff@worldbank.org.

**Keywords:** Income inequality; vertical redistribution; horizontal redistribution; China; Vietnam.

**Acknowledgements:** My thanks to Will Dow for his income data from the China Health and Nutrition Survey, and to Bert Hofman, Indermit Gill, Peter Lambert, Magnus Lindelöw, Branko Milanovic and Martin Ravallion for comments on earlier drafts.

## World Bank Policy Research Working Paper 3559, April 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get
**Figure 18. Simulated percentage change in earning as a result of the minimum wage increase**

<!-- Start of picture text --> 0.24<br>0.12<br>0.06<br>0.03 0.02 0.01 0.00 0.02 0.00<br>0.00<br>250 230 212 180 (137.6) for 35+(34-) 50% of the Median Wage<br>(193.6)<br>Minum wage (Gross, 2018 Euros)<br>Short-run Long-run<br><!-- End of picture text --> _Source: Own estimates using the 2018 Kosovo LFS._

**Figure 19. Simulated percentage point change in unemployment rate as a result of the minimum wage increase**

<!-- Start of picture text --> 0.56<br>0.38<br>0.31<br>0.21<br>0.16<br>0.11<br>0.05 0.07<br>0.00 0.00<br>250 230 212 180 (137.6) for 35+(34-) 50% of the Median Wage<br>(193.6)<br>Minum wage (Gross, 2018 Euros)<br>Short-run Long-run<br><!-- End of picture text --> _Source: Own estimates using the 2018 Kosovo LFS._

# **V. Conclusions and Policy Insights**

**Currently the minimum wage in Kosovo is 130 euros for people under the age of 35 and 170 euros for people 35 and older; this minimum wage is relatively high, when measured as the ratio of the minimum wage to the median wage.** <mark>The ratio of the minimum wage to the median wage in Kosovo is slightly lower
company contacts located outside of India. The first row documents that 39% of ethnic Indians choose India for their initial outsourcing contract. This rate compares to 32% for non-ethnic Indians, and the 7% difference between these shares is statistically significant at the 1% level. The next two rows show a strong contrast when looking at second contracts. Differences across ethnicities no longer link to differences in propensities to choose India; the more critical factor is whether the initial contract outsourced by the company contact went to India. Subsequent contracts have similar properties to the second contract. Thus, with all the caveats that need to be applied to sample averages, these simple descriptives suggest that ethnicity could play an important role in initial contract placements, with path dependency then taking on a larger role.

What drives this strong persistence in geographic choices? A very likely candidate is whether or not the company contact has a good experience on the first contract. Good experiences can create inertia where other options are not considered or adequately tested. Table 3 examines this possibility with linear probability models of the location choice of second contracts. The first column considers the second contracts formed by company contacts that chose India on their initial contracts. The estimating equation takes the form _Outcomei_ =η _tjc_ + δ⋅ _FirstContractSuccessfuli_ + β⋅ _CompanyContactEthnicIndiani_ + χ⋅ _FirstContractSuccessfuli_ ⋅ _CompanyContactEthnicIndiani_ + ε _i_ , where contracts are indexed by _i._ In the first column, the dependent variable is an indicator variable that takes the value of one if the company contact chooses India again. The primary independent variables are an indicator variable for the first project being a success (“good” performance rating or higher on the public feedback score or a successful evaluation in the private post-employment survey), the probability that the company contact is of ethnic Indian origin<sup>13</sup> , and their interaction. To control for many potential confounding factors, regressions include fixed effects for the (year _t_ ) x (job category _j_ ) x (country _c_ ) of
.

- Sun, Changlong, Yongli Zhang, Wenwen Ma, Rong Wu, and Shaojian Wang, “The impacts of urban form on carbon emissions: a comprehensive review,” _Land_ , 2022, _11_ (9), 1430.

- The World Bank, “Global Consumption Database,” 2023.

- , _Thriving: Making Cities Green, Resilient, and Inclusive in a Changing Climate_ , World Bank Publications, 2023.

- UN, _World Urbanization Prospects: The 2018 Revision_ , United Nations, 2017.

- UN Habitat, “World Cities Report 2020: The value of sustainable urbanization,” _United Nations Human Settlements Programme_ , 2020.

- UNEP, “2022 Global Status Report for Buildings and Construction,” 2022.

- United Nations, _World Population Prospects: The 2019 Revision_ , United Nations, 2019.

- World Bank, _Global economic prospects, 2019_ , The World Bank, 2019.

- Zhou, Yuyu, Xuecao Li, Wei Chen, Lin Meng, Qiusheng Wu, Peng Gong, and Karen C Seto, “Satellite mapping of urban built-up heights reveals extreme infrastructure gaps and inequalities in the Global South,” _Proceedings of the National Academy of Sciences_ , 2022, _119_ (46), e2214813119.

- Zink, Manfred, Markus Bachmann, Benjamin Brautigam, Thomas Fritz, Irena Hajnsek, Alberto Moreira, Birgit Wessel, and Gerhard Krieger, “TanDEM-X: The new global DEM takes shape,” _IEEE Geoscience and Remote Sensing Magazine_ , 2014, _2_ (2), 8–23.
suggests that the commodity boom may have translated into earnings growth among workers in rural areas who were specialized in the tradable sectors (including primary activities, mining, and manufacturing). These countries, plus Costa Rica, El Salvador, Nicaragua, and Panama, showed the largest narrowing in the urbanrural wage gap after 2003 (figure 12, panel b).

Figure 14. The average gender and urban-rural wage gaps, Latin America, 1993–2013 a. Gender labor earnings gap b. Urban-rural labor earnings gap _Source:_ Seventeen Latin American countries: SEDLAC database; Russia: the Russia Longitudinal Monitoring Survey; Post Apartheid Labor Market Series: South Africa; Turkey: I2D2-LFS; the United States: I2D2-IPUMS. See annexes A.1 and A.2 for details. _Note:_ The underlying data represent the change in wage inequality and the change in the earnings premiums for each country and circa period. The years selected for each country-circa combination may be different depending on the survey availability and to assure, to the extent possible, within country comparability for each period; see annex B.1 for details. In the case of the y-axis, the growth rate of the gender and area of residence gap is plotted. For the x-axis, the growth rate of the labor income Gini is plotted. The reference category of the gender gap is a woman, and the reference category of the residence earnings gap is rural areas. See section 3 and equation 1 for details. The sample covers full-time, wage, and self-employed workers 15–64 years of age. The values of the 1st and 100th percentiles of the earnings distribution were trimmed by each gender-education cell. The size of the bubbles represent the population of the country.

# **Summary and correlations: Earnings inequality and observed labor market structure**

Table 1 presents a summary of the main findings on the dynamics of labor market returns in Latin America over the last two decades. We can conclude that the four measures of earnings gaps that we have analyzed
previous decade. Although the government responded to deterioration in the education pro fi le with an investment program at the primary and “basic” level in 2000, a continuing problem has been the inability of the government to hire and retain teachers in schools.

An exacerbating factor is the HIV/AIDS epidemic. A recent report (Grassly and others 2003) calculates that the number of teachers lost to HIV/AIDS has increased from 2 per day in 1996 to 4 or more a day in 1998, representing two-thirds of each year’s output of newly trained teachers. Not surprisingly, teacher attrition has received a lot of attention, both in the popular press and in institutional reports (our data and that from the census of schools in 2002 corroborates the high rates of attrition (Das and others 2004b)). Further, absenteeism rates are high, primarily due to illness and funerals. Grassly and others (2003) for instance, fi nd that absenteeism arising from illness-related reasons will lead to the loss of 12,450 teacher-years over the next decade. The resulting teacher-shortage has led to class sizes above the 40 children per teacher norm (particularly in rural areas), teachers teaching double shifts, and limited possibilities for substitutions when teachers are absent.

In 2002 we surveyed 182 schools in four provinces of the country.<sup>9</sup> The choice of schools was based on a probability-proportional-to-size sampling scheme, where each of 35 districts in the four provinces was surveyed and schools were randomly chosen within districts with probability weights determined by grade 5 enrollment in the school year 2001. Thus, every enrolled student in grade 5 in the district had an equal probability of being in a school that participated in the survey. As part of the survey, questionnaires were administered to teachers and head-teachers with information on a host of topics including their demographics, personal characteristics, absenteeism, outside options and classroom conditions. In addition, we also collected information at the level of the school including fi nancing and the receipts of
floods on mobility is more prominent and discrepant. The average share of the road network that is flooded by at least 30 cm is 3.64% (or 24.84%) under the 5-year (or 1000-year) return period–yet 11.58% (or 65.67%) of simulated trips fail in the same scenario.

Past case studies on city or regional road networks proposed a positive correlation between road network resilience and graph-theoretic metrics, such as network density. Our study at the global scale, examining a total number of 2,564 road networks shows that network density helps partially understand network resilience to floods. The degree to which the variance of network resilience can be explained by network density alone varies case by case. When combining network density with flood exposure patterns, we show an increase in the overall explainability of network resilience. Furthermore, the proportion of the variance for percentage failed routes that can be explained by flood exposure and network density increases with flood return period. Nevertheless, it should be noted that the underlying governing factors that influence network resilience are inherently complex and vary across space. In locations where the political, socioeconomic, environmental, and cultural conditions for road infrastructure development are complex, additional factors need to be taken into the equation to fully understand road network resilience to floods.

Considering the scale of this study, we made several generalizations and assumptions when modeling the impact of floods on the mobility within settlement clusters. Due to limitations in data availability, we are unable to fully consider traffic congestion, local travel behavior patterns, or road conditions in the disruption simulation process. In addition, the exclusion of tertiary and residential road networks is able to reduce but not exclude selection bias that stems from the incompleteness of the OSM data set at the global scale. In locations where primary and secondary road network data is incomplete, our approach will likely overestimate the adverse impact of flood hazard on mobility.

# **References**

1. Koks, E. E. _et
.00|(0.00)|1.22<sup>***</sup>|
|Observations|4|30|7|6|506|

Notes: Sample is obtained from IFLS 3 (2000) conditional on enrollment in indicated public or private preschools. Means are reported in IDR 10,000 increments and are adjusted for inflation using national CPI with 2010 base year (FRED). The exchange rate in 2010 was 1 USD for 9,090 IDR (FRED).

77
concern in this case could be that some IDPs who register in non-conflict areas may be using these locations as stops en route to living abroad (i.e., transit migration), potentially decreasing the effectiveness of exclusion of foreign regions. To alleviate such concerns, we reproduce our estimations by (i) excluding Ukrainian regions on the country’s western border (which could provide a natural gateway into Europe), in addition to (ii), excluding regions with airports serving more than 100,000 passengers annually plus regions in

> 9Regional GDP numbers produced by the Ukrainian Statistics Office (UkrStat) excluded the non-government controlled areas (NGCAs) in Donetsk and Luhansk oblasts starting from 2014. World Bank, 2021 uses nightlight emission levels as crude proxies for economic activity in the Donbas region. Estimates show a 7.2 percent reduction in Luhansk GCA, 42.9 percent reduction in Luhansk NGCA, 20.2 reduction in Donetsk GCA, and 28.1 percent reduction in Donetsk NGCA.

19
|
|**1980-1984**|<br>n.a.|||n.a.|||n.a.|||0.0676|0.0692|0.0658|0.0381|0.0382|0.0378|
|||||||||||[0.0026]|[0.0037]|[0.0038]|[0.0021]|[0.0029]|[0.0030]|
|||||||||||<br>8739|4532|4187|8136|4208|3909|
|||||||||||||||**Not yet b**|**orn**|
|**1985-1989**|* Age ran<br>|ge at the b<br>|eginning o<br>|f the five<br>|year perio<br>|d|||||||0.0676<br>|<br><br>0.0705<br>|<br>0.06485<br>|
||Standard|errors in b|rackets, w|ith sampl|e sizes.||||||||[0.0028]|[0.0041]|[0.0039]|
||n.a.|||n.a.|||n.a.|||n.a.|||7703|3795|3894|

_Source_ : Siblings' mortality data. Cambodia Demographic and Health Survey (2000).

36
|(0.492)||(0.033)||(0.046)|
|_P-value_, total effect||0.29||0.22||0.0055||0.091|
|Region FEs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Sample mean|0.31|0.31|1.33|1.33|0.39|0.39|0.37|0.37|
|Observations|565|565|562|562|784|784|679|679|

_Notes_ : This table reports OLS estimates of each outcome on two binary variables: _incumbent runs_ , indicating whether the incumbent village head competed in the most recent election and _incumbent wins_ , indicating whether the incumbent won that election, respectively. Units of observation are villages in columns 1-6 and village heads in columns 7-8. The dependent variable is: in columns 1 and 2, a standardized index of local public service provision constructed using the 2021 Podes data. The index has the following 10 components: drinking water, sewage, garbage collection, street lighting, kindergartens, primary schools, village maternities (polindes), community health centers (puskesmas), paved roads, and public transit. In columns 3 and 4, the growth in service quality between the 2014 and 2021 Podes waves. In columns 5 and 6, the rate of bureaucratic turnover at the village level since the last election, defined as the fraction of new bureaucrats appointed to their current position since the last election. In columns 7 and 8, a dummy equal to 1 if relatives of the village head are employed in the village government. Regressions include region fixed effects. The main regions in our sample are Java, Sulawesi, Sumatra, Kalimantan, and NTB-Bali. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_
72.15%|85.38%|
|Undercoverage|59.59%|26.33%|14.13%|60.79%|27.85%|14.62%|
|Leakage|6.26%|18.76%|27.15%|6.47%|18.65%|28.19%|
|_Notes:_The logistic model is estimated<br>poverty line. For comparisons purpos<br>National Post-LASSO OLS model. Both<br>out-of-sample prediction on a random|using observe<br>es, the PMT cut<br>models includ<br>sample of 25|d poverty statu<br>off was selecte<br>e the same vari<br>% of household|s in the NHBPS<br>d to identify a<br>ables presente<br>s.|2014/2015 ba<br>similar share o<br>d in Table 3. T|sed on the na<br>f households<br>he results are|tional<br>as the<br>based on|

Table 9 presents the results of the Sudan national PMT with the inclusion of a complete set of state and rural and urban indicator variables. The inclusion of state and area indicator variables improves model accuracy by less than 2% with the largest gains observed when targeting the bottom of the consumption distribution. Similarly, poverty accuracy, undercoverage, and leakage all show modest improvements with

> 19 The regression results are available upon request.

19
rural areas of the district, where consumption poverty is both high and significantly worse than the national average of 24.5 percent. Poverty rates outside the district capital of Cox’s Bazar Sadar are higher than both the national and district averages, with the primary hosting subdistricts of Teknaf and Ukhia reporting small-area poverty estimates of 30 percent and 40 percent respectively. In 2016, only 55 percent of adults over 18 in Cox’s Bazar reported being literate. In addition, only half of all households reported access to electricity, and less than 3 percent reported access to piped water. According to the 2011 census, infrastructure and social indicators are also significantly worse outside the district capital and notably in the primary hosting sub-districts.

> 8 The CBPS is a partnership between the Yale Macmillan Center Program on Refugees, Forced Displacement, and Humanitarian Responses (Yale Macmillan PRFDHR), the Gender & Adolescence: Global Evidence (GAGE) program, and the Poverty and Equity Global Practice (GPVDR) of the World Bank.

3
**Source:** Estimates calculated using data from UN Comtrade database, the World Integrated Trade Solutions (WITS), and the World trade Organization (WTO). Plotted estimates are incidental-biascorrected coefficients and standard errors following Correia et al. (2019).  The solid vertical line indicates the estimated value of the “average” agreement as described in equation 1. The estimates of the “average” agreement are basically identical across most agreements because Morocco’s individual agreements have little influence on the average estimate of the other 262 agreements (Morocco is not one of the largest trading countries).

Two main findings emerge from Figure 3.1. The first is that individual trade agreements are associated with a wide range of estimated changes in trade flows. This heterogeneity has been noted in several earlier studies, but few studies focus on the variation individual countries experience across agreements. Second, the changes in trade Morocco experienced after its trade agreements are slightly larger than, but not statistically different from, the change in trade following the “average” agreement. This suggests that Moroccan trade agreements are having positive effects, but are not necessarily more productive than the average agreement in terms of increasing trade flows.

Notably, the results for the Global System of Trade Preferences among Developing Countries (GSTP) are negative, meaning a decrease of around 9 percent in trade flows after entry into force relative to other agreements. The trade agreement between the European Union, which accounts for most of Moroccan exports share, is positive and has a positive effect of around 18 percent in trade flows, slightly larger than, but not statistically different from, the average effect of around 14 percent for all other agreements. It is also important that trade between trade agreement signatory countries geographically closest to Morocco has doubled (Agadir Agreement and PAFTA) and even tripled (Türkiye – Morocco) the tradeincreasing estimate of the “average” trade agreement.

Figure 3.2 provides a graphical benchmarking these results by illustrating the estimated effects of 35 other agreements including