ai4data/datause-dataset-rechunked huggingface dataset:
**ZUS** - Zakład Ubezpieczeń Społecznych, Polish social security administration, also known as Social Insurance Institution.

44 Chart 1. Poland-Ukraine border movement balance and registered/active PESEL data 07 Chart 2. Ukrainians registered for social insurance 07 Chart 3. Number of Ukrainians registered in Poland for social insurance by sex 08 Chart 4. Age and gender structure of Ukrainian refugees 09 Chart 5. Ukrainian refugee households’ demographic composition 10 Chart 6. Local population shares of Ukrainian refugees 11 Chart 7. Income of Ukrainian refugee households by source 12 Chart 8. Ukrainian refugee household incomes from Poland and Ukraine in 2023 and 2024 12 Chart 9. Ukrainian refugee labour status 15 Chart 10. Ukrainian refugee median net wage 15 Chart 11. Main occupational groups of Ukrainian refugees, pre-war Ukrainians, other foreigners, and Polish citizens registered for social insurance, Q2 2022 and Q2 2024 (civilian, non-agricultural) 16 Chart 12. Ukrainian refugee wages relative to Polish citizens in the same employee-cells 17 Chart 13. Polish citizens and Ukrainian refugees’ employment rates by age group 18 Chart 14. Ukrainian refugee median net wage estimates in Q2 2024 19 Chart 15. Ukrainian refugees wages median net wage by age group 20 Chart 16. Median net wages of Ukrainian refugees median net wage by sector  21 Chart 17. Gross domestic product growth paths with and without Ukrainian refugees 23 Chart 18. Over-qualification rates by citizenship 27 Chart 19. Tertiary education and corresponding occupational groups shares 28 Chart 20. Ukrainian refugees median net wages by educational attainment 28 Chart 21. Share of regulated professions by citizenship and legal status, Q2 2024 29 Chart 22. Econometric model of determinants of Ukrainian refugees’ net wages 32 Chart 23. Average number of months since arrival of a Ukrainian refugee by Polish language fluency 33 Chart 24. What Ukrainian refugee groups have weakest Polish language fluency? 33 Chart 25. Ukrainian refugees’ employment rate in the 18-64 age group
wages 32 Chart 23. Average number of months since arrival of a Ukrainian refugee by Polish language fluency 33 Chart 24. What Ukrainian refugee groups have weakest Polish language fluency? 33 Chart 25. Ukrainian refugees’ employment rate in the 18-64 age group by previous status in Ukraine 34 Chart 26. Polish citizens employment rate  38 Chart 27. Polish citizens unemployment rate 38 Chart 28. Effect of a 1 pp. change in employment share of Ukrainian refugees on 39 Chart 29. Effect of a 1 pp. change in employment share of Ukrainian refugees on gross wage change 41 Chart 30. Polish citizens occupational distribution change by salary bracket, Q2 2022 and Q2 2024 41 Chart 31. Labour market activity rates  42 Chart 32. Average weekly hours in main job 42
Death Rates, Adolescent Mothers, and

Children and Adolescents out of school

using primary data from the MSNA and

secondary data sources.

Calculations were refined using qualita
tive information obtained from experts

during needs assessments validation

workshops conducted in Miranda, Zulia,

Falcón, Bolívar, Delta Amacuro, Sucre,

Amazonas, Apure, Táchira, Lara, and

the Capital District. The overall PIN for

the Protection Cluster is 4,425,466.00,

representing a 5% increase compared

to the calculation for the Humanitarian

Response Plan (HRP) 2022-2023.

Further, throughout October, the national

Protection Cluster participated in the

facilitation of extended OCHA Local

Coordination Fora meetings in the states

of Miranda, Sucre, Bolivar, Delta Ama
curo, Apure, Amazonas, Táchira, Zulia,

Falcón, and Lara. The primary objective

of these workshops was to validate data

collected from the MNSA on the human
itarian situation of the respective state

and, to gather complementary informa

tion to contribute to the narrative of the

HNO. Finally, it helped to validate the

prioritization of municipalities in collabo
ration with Cluster partners and OCHA.

**II.** **Coordination** **with** **other**

**platforms, subnational Clusters,**

**ICCG, and Partners**

**Coordination with the Response for**

**Venezuela (R4V) continued through-**

**out September and October through**

**bilateral meetings and common work-**
_UNHCR   Discussion   Paper   –15   June   2013_

Also   in   2012   UNHCR   received   authorization   to   open   offices   in   Hpa-­‐an   in   Kayin   State   and
Loikaw   in   Kayah   State,   although   this   is   still   to   be   formalised.   Access   remains   restricted
in   certain   areas   and   is   dependent   on   Government   authorization   and   restrictive   advance
clearance   procedures,   especially   for   international   staff.   However,   there   have   been
incremental   improvements   in   access   in   areas   close   to   the   Thai   border,   particularly   as   the
cease-­‐fire   process   has   become   embedded.   Nonetheless,   humanitarian   access,   and   the
degree   to   which   UNHCR   is   free   to   assess   needs   and   to   effectively   target   its   support   to   the
most   vulnerable   displaced   and   host   communities   varies   considerably   from   state   to   state.

**5.** **LEGAL,   INSTITUTIONAL   AND   POLICY   FRAMEWORKS,   AND   KEY**

**PRINCIPLES**
**time.** In the SEIS UNHCR survey, on
average, Ukrainian refugees who said they were fluent in Polish had stayed in Poland for 29 months, while those with an intermediate level – for only 22 months.
The results are interesting, especially the fact that the average time required to 32 33 progress from advanced to fluent language levels was longer than from intermediate to advanced, or beginner to intermediate (although not from zero to beginner). It is likely that the highest level of fluency, which may be required in some of the most attractive occupations, is also the hardest to achieve, and such language courses are not as readily available.
_UNHCR   Discussion   Paper   –15   June   2013_

livelihoods,   household   and   family   security   issues,   as   well   as   about   past,   present   and
possibly   future   skill-­‐sets   that   will   help   the   refugees   to   return   to   a   normal   life   outside   the
camp.   This   information   will   also   help   in   identifying   both   the   major   return   areas   and   all
other   locations   along   with   indications   of   the   possible   number   of   refugees   intending   to
return   to   those   areas,   and   required   programmatic   interventions   to   help   in   preparing   the
conditions   that   would   support   a   sustainable   voluntary   return   and   reintegration.

**3.2.   Internally   Displaced   Persons**

In   its   October   2012   report,   TBC   estimated   that   a   total   of   about   400,000   individuals   are
still   internally   displaced   in   the   rural   areas   of   36   townships   in   South-­‐East   Myanmar   in
Kayin,   Kayah,   South   and   East   Shan   and   Mon   States,   and   Bago   and   Tanintharyi   Regions.
Of   these,   the   number   located   in   Kayin,   Kayah,   Tanintharyi   and   Mon   combined   is
estimated   at   230,400   individuals   (89,150   or   38.7%   in   Kayin;   71,650   or   31.1%   in
Tanintharyi;   35,000   or   15.1%   in   Mon   and   34,600   or   15%   in   Kayah) [   5] .
increase in cash earning opportunities is estimated at 7.5 to 7.8 percentage points compared

with 5.4 in the baseline estimation.

**6.6 Employment and wages using the GLSS**

The DHS data do not provide detailed information regarding how much an individual earns for

work, or her wage rate, but the GLSS does collect such data. First, we try to replicate the results

estimated with the DHS data. Panel A of table 11 indicates that agriculture becomes less

important in mining communities for women (statistically insignificant), who mainly shift into

services and sales (statistically insignificant, except for strategy 2). Men are more likely to work

as miners (statistically significant across all strategies).

29
THE ROLE OF HOUSING SUPPORT AND EMPLOYMENT FACILITATION IN ECONOMIC VULNERABILITY OF REFUGEES FROM UKRAINE

**SHARE OF YOUNG UKRAINIANS (AGED 15 TO 24) WHO ARE NOT ENGAGED IN EMPLOYMENT, EDUCATION, OR TRAINING**

**(NEET) BY COUNTRY, %** **[1,2,3]**

Host country Refugees, excluding online education Refugees Bulgaria Czechia Hungary Moldova Poland Romania Slovakia Region

1. Data reported by the national statistics service was used as a
reference for Moldova 2. With the exception of Poland and the Czech Republic, the reliability of data by country is hindered by a relatively low number of observations

[3. Host country data is based on indicators reported by the OECD for](https://data.oecd.org/youthinac/youth-not-in-employment-education-or-training-neet.htm)
[2022](https://data.oecd.org/youthinac/youth-not-in-employment-education-or-training-neet.htm)

**UKRAINE REFUGEE YOUTH ACTIVITY BY AGE**

**The gender divide: female – led households are**

**more economically vulnerable than their male –**

**led counterparts**
The share of households that are composed of female only adults with or without children constitute 65% of the total sample across the region. Compared to households with male only adults, the vast majority of which are without children, they are 51% more likely to find themselves below the poverty line.

**DISTRIBUTION OF HOUSEHOLDS BY GENDER OF**

**ADULTS, %**

NEET 15 16 17 18 19 20 21 22 23 24
# **BRIEF** **BRAZIL**

### Operational Context & Analysis

Brazil has in place a progressive and inclusive protection and solutions framework for refugees
and other forcibly displaced individuals, ensuring equal access to rights and services alongside its
national citizens. With an open-border policy, Brazil guarantees the admission, registration, and
documentation of those in need of international protection. Brazil hosts the largest number of
Venezuelans recognized as refugees in Latin America and the Caribbean.

As of July 2024, Brazil has recognized 144.463 refugees and provided alternative protection
pathways to 572.877 persons in need of international protection, the majority of whom are
Venezuelan (474.217) and Haitian (89.455) nationals. Additionally, there are 75,998 pending
asylum applications, primarily from individuals originating from Cuba (26.225), Venezuela (15.065)
and Angola (8.696).
host country weighted average [17] . Excluding such online schooling increases this indicator to 18%. The highest NEET rate by country was reported in Moldova at 26%, which is similar to the local population, and the lowest in Poland at just 11% (3 percentage points below the national metric).
Regionally, the Ukraine refugee NEET rate tends to increase with age, as it becomes more affected by unemployment, which is high for the 15 – 24 cohort (at 17%).
19 The complete rationale for the productivity shock is outlined in the Appendix on modelling strategy, while further detailed results of econometric models are available in the Online Technical Appendix.

20 As outlined in the Appendix on modelling strategy, the total number of refugees was set at 2.6% of the total population, while their share in total employment as growing from 1.5% in 2022 to 2.4% in 2024.
HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES Just like last year, employment rates demonstrated disparity by age group, gender, education level, the presence of a disability, and being in need of MHPSS. Disability was found to be associated with the largest drop in employment, with this factor halving the probability of working in 2024 when compared to the sample overall. Having MHPSS needs and being female were also associated with a lower employment likelihood, although to significantly smaller degrees.

Compared to the host population, the refugee sample demonstrated higher employment rates at lower age brackets (15-19 and 20-24), which, considering the findings on poverty, can be interpreted as a coping strategy associated with low income. Higher age brackets (55-59 and 60-64) saw the highest gap in the employment rate compared to hosts, supporting the hypothesis that older age individuals have more difficulty integrating into the local labor market.

**REGIONAL EMPLOYMENT RATE BY AGE AND POPULATION**

Refugees (2023) Refugees (2024) Hosts (2023) 82% 79% 80% 52% 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 Source: Survey data, ILO

**REGIONAL REFUGEE EMPLOYMENT RATE BY POPULATION**

**GROUP (2024)**

**EMPLOYMENT RATE BY EDUCATION LEVEL**
the bottom 40 percent in four groups: far away, before mine or during mine, and close to mine,

before mine or during mine. The summary statistics for selected main outcomes are presented

in table 10. As the table shows, the bottom 40 percent in mining communities are more likely

engaging in agriculture than the bottom 40 percent elsewhere. This could illustrate that

agricultural workers are overrepresented among the less well-off in mining communities.

However, women in this group still more often work in services than women did before in the

same communities.
The choice of district – rather than cluster – fixed effect is informed by the understanding that

meaningful time-invariant factors - such as mining laws, level of development, local political

institutions, norms regarding environment, women’s participation in the labor market, etc. - that

influence exploitation of the mine happens at the district level. Including district fixed effects,

we control for various institutional and cultural factors at the district level that are stable over

time. Including district fixed effects also ensures that we are not only capturing effects from

transfers or the fiscal system as we compare individuals within the same districts. With this

method we capture the geographic spillover effects in the vicinity of the mine. Moreover, cluster

fixed effects are not possible because of clusters are not repeatedly sampled over time.

However, since the estimation is at individual level, all standard errors are clustered at the DHS

cluster level.

The sample is restricted to individuals living within 100 km of a deposit location ( _mine_ ), so

many parts of Northern Ghana where there are few gold mines are not included in the analysis.

The sample restriction is created by using the time-stable continuous distance measure that we
Due   to   the   size   and   remoteness   of   the   operational   area,   compounded   by   access
limitation   and   sensitivities,   reliable   and   disaggregated   information   on   the   profile   and
needs   of   displaced   populations   remains   scarce.   UNHCR   has   recently   strengthened   its
information   management   capacities   with   a   view   to   working   towards   obtaining   a   more
systematic   understanding   of   the   locations   and   characteristics   of   populations   of   concern,
and   the   protection   risks   affecting   them.   Additionally,   the   Joint   IDP   Profiling   Service
(JIPS),   has   recently   completed   a   scoping   mission   aiming   at   assessing   whether   it   is
feasible   and   desirable   to   conduct   an   IDP   profiling   that   would   inform   the   design   of   an
effective   strategy   for   support   to   IDPs   durable   solutions,   as   well   as   targeting,   improved
advocacy   and   fundraising.

**4.** **UNHCR’S   CURRENT   OPERATIONS   IN   THE   SOUTH-­‐EAST**
**Persons of concern in Southern Africa, Data as of 30 September 2022**

Notes: *'Other' in the location refers to any known location other than camp or settlement sites, covering both urban and rural areas; **self-settled refers to the individuals without available information such as their names and locations, and their locations are categorised to be 'unknown'; those by location in Congo, Democratic Republic of
the Congo and Zimbabwe could be different from the numbers operation report due to inconsistency in proGres v4.
contrast, show the univariate regression coefficients using OLS, highlighting the difference

between the population living close (e.g. Column 2) and far away (e.g. Column 1) before

mining.

In the pre-period, women in communities that are close to mines are less urban, poorer, have

more children and are less likely migrants. In contrast, women are of similar age, have similar

education and occupation (but slightly more likely earning cash). Note that these are raw mean

values not controlling for any regional and individual differences. Overall, these differences are

in line with previous research finding that large-scale mines tend to open in more rural and less

developed communities (Benshaul-Tolonen, 2018; Kotsadam and Tolonen, 2016).

In active mining communities, women are still less likely to live in urban areas (although the

gap between mining and non-mining areas may be smaller) than in non-mining communities,

but more likely to have some education. The difference-in-difference estimation strategy

assumes similar trends over time across the treatment (close to mines) and control group (far

away from mines), in absence of the gold mining expansion. While this assumption cannot be

tested using our dataset, previous analyses have found evidence for parallel pre-trends in infant
CROSS BORDER MOVEMENTS KEY FIGURES

200 61 118 21

REGIONAL BUREAU FOR SOUTHERN AFRICA

**PERSONS OF CONCERN INVOLVED IN CROSS BORDER MOVEMENTS IN SOUTHERN AFRICA**

As of 30 September 2022

MAP OF THE CROSS BORDER MOVEMENTS WHERE THE FLOW INVOLVED 3 POCs AND MORE *

**Inward movements**

**into the region**

**Total cross**

**border**

**movements**

**Intra region**

**movemens**

**Outward**

**movements from the**

**region**

Inward

31%

Outward
movements

34%

Before 2019 2019 2020 2021 2022

exclusion.
table 7). Low weight-for-age is an indicator for acute malnutrition, whereas height-for-age is

an indicator for chronic malnutrition. This could indicate that mining districts are less food

secure. [12] Table 7 shows that there are no effects on illness in the last two weeks.

**6. Distributional effects, mechanisms and robustness**

**6.1 Decomposing results by migration status**

We argue that one source of heterogeneity is to consider when exploring socio-economic

impacts and distributional effects of large-scale mining is migration status. First because mining

may cause inward migration of individuals that are different from the previous local population.

While it has its limitations, disaggregating the effects between nonmigrants and migrants may

shed some light on the effect on the initial population. Second, to understand the distributional

effects of mining we argue that migration status may be an important factor.

In the analysis, we distinguish between nonmigrants (where the woman respondent report being

born in the locality) and migrants (born elsewhere). We note several caveats with this analysis,

the first being that we cannot follow migrant households before the migration decision.

Therefore, we cannot make any causal claims on changes in this group over time. We compare

---
[12] In table 5 we saw very small insignificant changes in nutritional status.
3 As of December 2023, according to UNHCR, based on governmental sources [Situation Ukraine Refugee Situation (unhcr.org)](https://data.unhcr.org/en/situations/ukraine) 4 According to the active PESEL UKR database.
5 According to the active PESEL UKR database in October 2023.
6 Deloitte calculations based on Multi-Sector Needs Assessment Poland 2023 survey data provided by UNHCR.

06
1. A household classified as above or below the poverty line if household members are individually located above or below the poverty line

2. Income shares represent the average for each group 11 Defined in the survey as working for an employer full- or part-time on a regular basis, opposed to temporary work or selfemployment 12 Here defined as the share of individuals aged 15+ who are either employed or unemployed. Unemployed is understood as having searched for work over the past 30 days and willing to start if an opportunity presents itself over the next 2 weeks 13 Ratio of people younger than 15 and older than 64 to the 15-64 cohort 14 The share of individuals aged 15+ that are either employed or unemployed (have looked for work over the past 30 days and are willing to start if an opportunity presents itself over the next 2 weeks)
However,   two   serious   internal   conflicts   still   remain.   Following   two   waves   of   inter-­‐
communal   violence   in   the   western   state   of   Rakhine   in   2012,   over   150   persons   were
killed   and   over   115,000   people   were   displaced.   In   the   northern   states   of   Kachin   and
Shan,   the   conflict   with   the   Kachin   Independence   Army   continues;   fighting   flared   up   in
June   2011   breaking   a   cease-­‐fire   that   held   for   17   years.   Despite   repeated   attempts   to
negotiate   a   new   cease-­‐fire   agreement,   no   peace   solution   has   been   reached   between   the
parties   and   the   fighting   has   so   far   resulted   in   the   displacement   of   an   estimated   of   about
85,000   persons   scattered   in   more   than   194   locations.

Although   the   cease-­‐fire   agreements   have   not   led   yet   to   durable   peace   accords,   the
situation   in   the   South-­‐East,   where   the   number   of   Internally   Displaced   People   (IDPs)   in
UNHCR’s   area   of   operation [1] is   estimated   to   be   about   230,400   people,   has   started   seeing
changing   dynamics.   From   the   moment   when   the   new   civilian   Government   started   to

---
[1] UNHCR’s   area   of   operation   in   the   South-­‐East   currently   consists   of   Kayin,   Kayah,   and   Mon   States,   and
During the covered period, a National Development Plan spanning 2022-2026 has been prepared, to
supplant the National Development Plan document (2019-2021) which has lapsed, but it has not been
adopted. The updated plan maintains provisions for establishing consensual mechanisms for the peaceful
resolution of conflicts. This involves reinforcing the legal framework to foster trust between communities and
security forces, facilitating socio-security dialogue, promoting peaceful coexistence and fostering respect
for differences.

2 R E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D**
Deloitte D.Climate, [32 33] is a general equilibrium model that uses consumer and producer optimisation to calculate changes in the economy in response to shocks. This enables the impact of shocks to be assessed by considering supply and demand channels as well as connections between different sectors of the economy. It provides information on the total impact of shocks on various aspects of the economy, including the labour market, government revenue, as well as key economic aggregates. It is the most appropriate tool for accounting for the multi-layered impact of Ukrainian refugees. A counterfactual analysis was performed using the latest data on the Polish economy to account for the effects of other shocks in the economy. The model enabled a counterfactual analysis to be conducted by isolating the refugee influx from all other economic shocks, e.g. the other macroeconomic consequences of the war in Ukraine. The results were calculated for 2022, 2023 and 2024 with an additional long-term analysis up to 2030 to assess the economy’s long-term adaptation (assuming no new shocks, including no countershocks [34] ).

32 Economics of climate change | Deloitte Australia

**Chart 32. Average weekly hours in main job**

Women in 15-64 age group 68.4% 2023-Q2

---
[34] E.g. refugees keep having lower productivity rather than adapt to level of natives.