rafmacalaba/datause-encoder-data huggingface dataset:
public sector cannot be adequately described as a simple “wage premium” that is unrelated to productive characteristics.

Finally, we find clear evidence that workers sort according to their comparative advantage θ. As shown in table 5, the means of the distribution of θ are negative for the employees who stay in or who switch to the private sector (m00, m10). The public sector stayers have, on average, higher (positive) values of θ. Our estimation results also suggest that the sorting of workers across sectors responds to the changes in the returns to their comparative advantages. During the 1990s, when the returns to workers' comparative advantage are lower in the public than in the private sector, the average of θ among the “switchers to public” (m01) is much lower than it is for any other group. However, in the second panel, the relative returns are higher (α≥1).

|TA|BLE5. Non-Lin|ear Least Squ<br>1993|ares Estima|tion Results|2006||
|---|---|---|---|---|---|---|
||Coef.|Std. Err.|P>t|Coef.|Std. Err.|P>t|
|Relative returnsα|0.505|0.267|*|1.448|0.725|**|
|Publicpremiumηt|−0.222|0.187||0.010|0.055||
|Publicpremiumηt+1|−0.125|0.188||0.089|0.055||
|Time dummy τt|0.620|0.168|***|1.315|0.047|***|
|Time dummy τt+1|1.103|0.164|***|1.559|0.046|***|
|Public stayersm11|0.309|0.197||0.231|0.079|***|
|Private stayersm00|−0.256|0.164||−0.158|0.046|***|
|Switchers topublicm01|−0.524|0.199|***|−0.108|0.103||
|Switchers toprivatem10|−0.021|0.186||0.061|0.046||
|Sample size|1750|||7786|||

_Source:_ Author's calculations based on VLSS and VHLSS data.

Compared with the other models presented above, our model accounts for any characteristic that contributes to workers' comparative advantage from being in the public sector rather than in the private sector. In this sense, our model allows for a greater effect from workers' selection into one sector or the other. However, we find significant differences in the relative returns to workers' comparative advantage between the public 21
-17-

# SOMALIA

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## FIGURE 2b
# **Table 1.** <u>Illustration of Income Source-Earner Combinations</u>

||Nu|mber of I|ncome Earners|Non-labor income|
|---|---|---|---|---|
|||1|2<br>3||
|ources|1|X|X||
|ncome S|2||X||
|mber of I|3||X||
|Nu<br>Source:<br>**Table 2**:|4<br>Zimbabwe I<br>Percent con|CES 1990<br>tribution|/91 and 1995/96.<br>of different income sources to ov|X<br>erall income of the sample|
|**Income**|**Sources**||**Rural**|**Urban**|
||||1990/91<br>1995/96|1990/91<br>1995/96|
||||(N=9432)<br>(N=10136)|(N=4744)<br>(N=7391)|
|Formal w|age employ|ment|27.2<br>26.5|66.1<br>57.9|
|Informal|wage empl|oyment|4.3<br>7.4|13.2<br>16.8|
|Self-emp|loyment||45.5<br>46.3|8.1<br>10.8|
|Non-labo|r income||22.9<br>19.7|12.8<br>14.4|
|**Total**|||**100**<br>**100**|**100**<br>**100**|

Source: Zimbabwe ICES 1990/91 and 1995/96.

**Table** **<u>3</u>** <u>: Percent distribution of households by number of income sources (N)</u>

|**Number of income**|**Rur**|**al**|**Urba**|**n**|
|---|---|---|---|---|
|**sources**|1990/91|1995/96|1990/91|1995/96|
|1|20.8|35.6|38.0|41.8|
|2|62.1|52.0|40.4|40.7|
|3|16.3|12.0|20.0|15.8|
|4|0.8|0.4|1.5|1.7|
|**_Total_**|**_100_**|**_100_**|**_100_**|**_100_**|

Source: Zimbabwe ICES 1990/91 and 1995/96 17
# **Table 8:  Ordered Probit regressions of determinants of young people's (aged 15-29) satisfaction with opportunities to improve one's social status in the future**

|Dependent Variable: 1= highly dissatisfied;|**All indi**|**viduals**|**Females**|**Males**|
|---|---|---|---|---|
|2=dissatisfied; 3=satisfied; 4= highlysatisfied|(1)|(2)|(3)|(4)|
|Employed|0.324***||||
||(0.0639)||||
|Individual Relative Labor Deprivation||-5,465***|||
|(reference: all individuals)||<br>(968.4)|||
|Individual Relative Labor Deprivation|||-3,280**|-1,964***|
|(reference: individuals of same gender)|||(1,597)|(352.3)|
|Primary education|0.0987|0.182***|0.140|0.0920|
||(0.0687)|(0.0696)|(0.0874)|(0.120)|
|Low secondary education|0.217***|0.297***|0.145|0.300**|
||(0.0770)|(0.0781)|(0.104)|(0.126)|
|Secondary education|0.339***|0.431***|0.418***|0.360**|
||(0.0955)|(0.0966)|(0.139)|(0.150)|
|Tertiary education|0.600***|0.749***|0.741***|0.759***|
||(0.137)|(0.137)|(0.193)|(0.208)|
|Married|0.317***|0.317***|0.393***|0.0886|
||(0.0609)|(0.0609)|(0.0758)|(0.113)|
|Youth aged (20-24)|0.00640|0.0439|-0.119|0.164|
||(0.0658)|(0.0656)|(0.0896)|(0.100)|
|Youth aged (25-29)|-0.0541|0.0136|-0.0966|0.0542|
||(0.0708)|(0.0701)|(0.0982)|(0.105)|
|Urban|0.258***|0.220***|0.332***|0.157*|
||(0.0581)|(0.0583)|(0.0815)|(0.0867)|
|Male|-0.156**|-0.169***|||
||(0.0619)|(0.0614)|||
|Region dummies|Yes|Yes|Yes|Yes|
|Constant|||||
|cut1|-0.0574|-0.283|-0.129|-0.436|
||(0.184)|(0.189)|(0.224)|(0.355)|
|cut2|0.843***|0.619***|0.802***|0.485|
||(0.184)|(0.190)|(0.225)|(0.356)|
|cut3|1.719***|1.497***|1.759***|1.322***|
||(0.187)|(0.192)|(0.229)|(0.357)|
|Observations|2,128|2,128|1,155|973|
|PseudoR2|0.0463|0.0476|0.0513|0.0820|

Source: MHYS 2009-2010. Note: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; The reference groups is rural unmarried youth aged 15-19 without any education (for model 1 and 2 add female) Education and marriage are factors associated with greater satisfaction with potential to improve social status as it is also known in the happiness literature. Higher levels of education and marriage are typically associated with greater life satisfaction and our results are in line with such findings. In models 1 and 2, it is also apparent that young males have lower levels of satisfaction than young women, which 20
Table 3: Exogenously Calibrated Parameters

|Parameter|Value|Target/Reference|
|---|---|---|
|_σ_|5|Simonovska and Waugh(2014)|
|_{βa_,_βm_,_βs}_|_{_0.92, 0.86, 0.95_}_|Share of Energy in Production, 2010|
|_{ωa_,_ωm_,_ωs}_|_{_0.204, 0.272, 0.524_}_|Value Added Shares, 2010|
|_ν_|0.93|Expenditure Share in Electricity, 2008-2009|
|_θ_|_−_0.30|Expenditure Share in Housing, 2002-2009|
|_γ_|0.47|Literature (see the text)|

Calibrated values correspond to Brazilian data, except when taken from the literature.

**Construction of the Road Network of Brazil.** The road network is defined as _R ≡_ ( _E_ , _V_ ) where _E_ is a set of edges or roads, and _V_ is a set of vertices or road junctions. Each microregion is associated with a point in the plane corresponding to its centroid, which we express as the set _M_ . The set of road junctions _V_ and the set of microregions centroids _M_ do not need to coincide, so to construct an adjacency matrix of the network joining _V_ and _M_ we choose to shift points in _M_ to the closest point in _V_ that is still within the microregion. That is, for each _mi_ ( _xi_ , _yi_ ) _∈M_ , we associate a _vj_ ( _xj_ , _yj_ ) _∈V_ such that

**Definition of Microregion Centroids.** Instead of using geographical centroids, we opt to choose a point of economic significance. To do so, we use data from the Brazilian Institute of Geography and Statistics, IBGE ( _Instituto Brasileiro de Geografia e Estatistica_ ) on the registration of selected locations, which offers georeferenced data on cities. A location is considered a city if it has a city hall. It consists of the urban area of the headquarters district and is bounded by the urban perimeter by municipal law. This dataset contains a total of 5,565 cities. If a microregion has several cities, we choose the one with the largest population based on the IBGE Census data. If a microregion has no urban areas, we likewise select the rural center with the largest population. Figure 4a shows a zoomed-in view of a specific section of the observed network with the locations of the centroids. Figure 4b shows the discretized network with shifted centroids for the same section.<sup>14</sup>

> mates in Couture et al. (2018) imply _γ_ = 0.1, which is the one used by Fajgelbaum and Schaal (2020). We choose _γ_ = 0.47, which is in the middle of the interval [0.1, 0.84].

> 14To compute to which region each centroid belongs to, we use the function `inpoly2` that substantially reduces computation times (Engwirda, 2021).

19
|||**Table 4: Base**|**linequantil**|**e decomposi**|**tion results**|||||
|---|---|---|---|---|---|---|---|---|---|
|Dependent variable: Sales decline(%)|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
||**10th**<br>**quantile**|**20th**<br>**quantile**|**30th**<br>**quantile**|**40th**<br>**quantile**|**50th**<br>**quantile**|**60th**<br>**quantile**|**70th**<br>**quantile**|**80th**<br>**quantile**|**90th**<br>**quantile**|
|||||**Panel A: T**|**otal gap (un**|**conditional)**||||
|SMEs|2.392***|11.163***|18.058***|30.776***|35.634***|47.300***|56.919***|66.328***|84.028***|
||(0.707)|(0.707)|(2.529)|(2.283)|(2.669)|(2.677)|(2.542)|(2.995)|(2.431)|
|Large firms|-4.690***|4.911***|10.368***|11.853***|23.501***|32.331***|36.359***|51.088***|66.107***|
||(0.959)|(0.891)|(0.891)|(2.407)|(2.769)|(2.836)|(3.162)|(4.380)|(5.354)|
|Difference (SME-Large firms)|7.082***|6.252***|7.690***|18.923***|12.134***|14.968***|20.561***|15.240***|17.921***|
||(0.838)|(0.804)|(2.319)|(2.283)|(2.620)|(2.702)|(2.508)|(3.435)|(4.565)|
||||**Panel B:**|**Aggregate**|**endowment**|**and structur**|**al effects**|||
|Aggregate endowment effect|2.443**|2.270**|2.270**|9.958***|10.668***|9.397***|8.713***|12.042***|19.370***|
||(1.062)|(0.905)|(0.905)|(2.370)|(2.594)|(2.686)|(2.828)|(3.763)|(4.805)|
|Aggregate structural effect|4.639***|3.983***|5.420**|8.966***|1.466|5.571**|11.847***|3.199|-1.449|
||(1.092)|(0.894)|(2.227)|(1.883)|(2.116)|(2.202)|(1.991)|(3.087)|(4.860)|
|||**Panel C: Ag**|**gregate end**|**owment and**|**structural e**|**ffects as a pe**|**rcentage of t**|**he total gap**||
|Aggregate endowment effect|34.5%|36.3%|29.5%|52.6%|87.9%|62.8%|42.4%|79.0%|108.1%|
|Aggregate structural effect|65.5%|63.7%|70.5%|47.4%|12.1%|37.2%|57.6%|21.0%|-8.1%|
|Huber-White robust standard errors clust|ered on countr|y-industry pa|irs. *** (1%)|, ** (5%), *|(10%).|||||

56
mapped occupations are unlikely to change the overall findings of our analysis.

# _Data_

Four data sets are used in our analysis. These are the OECD’s Programme for the International Assessment of Adult Competencies (PIAAC), World Bank’s Skills Towards Employability and Productivity (STEP), the Integrated Public Use Microdata Series (IPUMS-I) by the Minnesota Population Center (2017), and the OECD’s Programme for International Student Assessment (PISA).

Both the PIAAC and STEP data are household surveys that collect data on individuals’ occupations (coded using ISCO-08) and measure individuals’ literacy achievement. STEP also measures non-cognitive skills including the “Big 5” personality traits of extraversion, conscientiousness, openness to experience, emotional stability and agreeableness. Both data sets also include data on educational attainment, gender and age. We apply the Frey and Osborne (2013) automation probabilities to the occupation coding as described above using the ISCO-08 occupation classifications included in the data set. These data are used to test the underlying assumption of the model that educational attainment and skills are positively associated with non-automation-prone occupations and to understand how educational attainment, cognitive skills (as measured by literacy achievement) and non-cognitive skills associate with being in a non-automation-prone occupation. For PIAAC data, post-secondary education is labeled as vocational if it is in the ISCED B track (whose graduates are normally destined for direct entry to the labor market). Also, those in ISCED levels 4A and 4B are grouped with general upper secondary while those in 4C are grouped with vocational upper secondary. For STEP data, a variable denoting ISCED level and orientation is used to define vocational and general programs for each level of education. STEP’s non-cognitive skill variables are standardized to mean 0 and standard deviation of 1. PIAAC data sets for 21 countries and STEP data sets for 7 countries have the necessary variables for our analysis. PIAAC surveys were conducted between 2008 and 2016 while STEP surveys were collected between 2011 and 2013.

From the IPUMS data collection, we select any country that has data available from 1990 onward with ISCO occupation classifications, and, of these countries, we apply the Frey and 21
- Mauro, P., N. Sussman, and Y. Yafeh. 2002. “Emerging Market spreads:Then versus now.” _The Quarterly Journal of Economics_ 117:695–733.

- Miranda, M.J., and P.L. Fackler. 2002. _Applied computational economics and finance_ . MIT Press.

- Moore, J., S. Nam, M. Suh, and A. Tepper. 2013. “Estimating the Impacts of the U.S. LSAPs on Emerging Market Economies’ Local Currency Bond Markets.” Federal Reserve Bank of New York Staff Reports 595, Federal Reserve Bank of New York.

- Nakamura, K., and T. Yagi. 2017. “Fiscal Conditions and Long-term Interest Rates.” _Monetary and Economic Studies,Institute for Monetary and Economic Studies,Bank of Japan_ 35:59–88.

- Ongena, S., A. Popov, and N. Van Horen. 2019. “The Invisible Hand of the Government: Moral Suasion during the European Sovereign Debt Crisis.” _American Economic Journal: Macroeconomics_ 4:346–379.

- Ottonello, P., and D. Perez. 2019. “The Currency Composition of Sovereign Debt.” _American Economic Journal: Macroeconomics_ 11:174–208.

- Peiris, S.J. 2013. “Foreign Participation in Local Currency Bond Markets of Emerging Economies.” _Journal of International Commerce, Economics and Policy_ 04:1–15.

- Reinhart, C.M., K. Rogoff, and M. Savastano. 2003. “Debt Intolerance.” _Brookings Papers on Economic Activity_ 34:1–74.

- Reinhart, C.M., and C. Trebesch. 2015. “The pitfalls of external dependence: Greece, 1829-2015.” _Brookings Papers on Economic Activity_ 46:307–328.

- Sakuragawa, M., and Y. Sakuragawa. 2016. “Absence of safe assets and fiscal crisis.” _Journal of the Japanese and International Economies_ 40:59–76.

- Schmitt-Grohe, S., and M. Uribe. 2003. “Closing small open economy models.” _Journal of International Economies_ 61:163–185.

- Shiller, R.J., J.Y. Campbell, and K.L. Schoenholtz. 1983. “Forward Rates and Future Policy: Interpreting the Term Structure of Interest Rates.” _Brookings Papers on Economic Activity_ 14:173–224.

25
business recovery seems generalizable, as does the change in demand and supply chain disruption leading to faster recovery for retail than manufacturing.

We interpret the data as supporting the use of cash grants in disaster recovery, but only in limited cases. Grants to firms in retail trade stimulate more rapid recovery of these enterprises. The data also support a greater use of cash aid in household recovery. The spending by households in local shops provides a stimulus that is lacking with inkind aid. Finally, we believe the experiment has demonstrated the ability of random grants to generate knowledge about how to increase the speed with which small enterprises recover. If global warming leads to more frequent and more severe waterrelated disasters, as climate change experts predict, this knowledge will be increasingly valuable in hastening recovery from the growing devastation.

# **References**

Asian Development Bank, Japan Bank for International Cooperation, and World Bank, 2005, “Tsunami Recovery in Sri Lanka: Damage and Needs Assessment,” World Bank Report.

Banerjee, Abhijit and Esther Duflo. 2005. “Growth Through the Lens of Development Economics”, in Philippe Aghion and Stephen Durlauf (eds), _Handbook of Economic Growth, Volume 1_ , pp. 473-552. Amsterdam: Elsevier.

Blanchard, Olivier and Michael Kremer, 1997, “Disorganization,” _Quarterly Journal of Economics_ , Vol. 112(4), pp. 1091-1126.

Carter, Michael, Peter Little, Tewodaj Mogues and Workneh Negatu. 2007. “Poverty Traps and Natural Disasters in Ethiopia and Honduras”, _World Development_ 35(5): 835856.

Dacy, Douglass, and Howard Kunreuther, 1969, _The Economics of Natural Disasters: Implications for Federal Policy_ , New York: The Free Press.

De Mel, Suresh, David McKenzie and Christopher Woodruff, 2008 “Returns to Capital in Microenterprises: Evidence from a Field Experiment”, _Quarterly Journal of Economics,_ 123(4): 1329-72.

30
19 §2?

(MFA) and other textile quotas; "vw untary" export restraints; product specific charges like antidumping and countervailing duties; restrictive licensing requirements; and variable impert levies or "flexible"import fees. These tabulations were made for the same product groups used to analyze tariffs in order to indicate where the two types of restrictions are generally applied jointly (like foodstuffs), or separately (like wood furniture).13 With the exception of the United States, foods and animal feeds is one of the most NTB ridden product groups in OECD markets. Over three-quarters of FSU exports to Finland and Sweden encounter nontariff barriers, as do almost 70% of food exports to Japan. European Community NTBs are applied to 80 percent of FSU meat and sugar exports, and to slightly less than half of all fresh and preserved fruit products. The importance of these NTB statistics is accented by related studies showing they often reflect very high levels of nominal protection against foreign suppliers. For example, the UN Food and Agricultural Organization estimates the average level of protection for cereals, dairy, and sugar products in the EC and Japan ranges from 100 to 300 percent, while Laird and Yeats (1990, Chapter 5) indicate that variable import levies in Switzerland and Sweden, which are applied extensively to agricultural imports, often have ad valorem equivalents of over 100 percent.

As far as manufactures trade is concerned, the highest NTB coverage indices are recorded by the EC, Switzerland, and Sweden. Almost one-fifth of FSU exports to the EC face NTBs, with these restrictions largely concentrated in five sectors: leather and leather goods; textile yarn and fabrics; ferrous - metals; clothing; and footwear. As was the case with foodstuffs, related studies affirm that very high c__ levels of nominal protection are associated with these nontariff measures. A Laird and Yeats (1990, C=hapter _5)_ survey suBLests the (NTB-induced) level of EC protectinn fnr textiles and clothing liae between 30 and 50 percent, while that for ferrous metals is in the range of 20 to 30 percent. Hamilton 35An important problem associated with the analysis of NTBs is that these measures take very different forms from country-to-country and their trade effects or nominal equivalents are often very difficul **t** o estimate (see Laird and Yeats, 1990 Chapter 2 for a detailed discussion of this point). As suwh, economists often rely on indices (like those presented in Table 9) that show product sectors within which nontariff barriers are most prevalent. See UNCTAD (1988) for a discussion of problems in the use and interpretation of NTB inventory data.

* '\&L 'N '

. .

A ,.es,
Navicke, J., & Lazutka, R. (2015). Work INcentives at the bottom of the income distribution and for model families in Lithuania. _EUROMOD Working paper Series_ (EM14/15).

OECD. (2008). _2008 Employment Output._ Paris: OECD Publishing.

Packard, T. K. (2012). _In From the Shadow: Integrating Europe's Informal Labor._ Washington, DC: World Bank.

Pages, C. (2017). Do Payroll tax cuts boost formal jobs in developing countries? _IZA World of Labor_ .

Paulus, A., Lelkes, O., Mitja, C., Kump, N., Hegedus, P., Vork, A., . . . Kralik, S. (2009, April). Flat tax reform in Eastern Europe: comparative analysis of alternative scenarios in Estonia, Hungary, and Slovenia, using EUROMOD. _EUROMDO Working Paper Series_ .

- Perry, G., Maloney, W., Arias, O., Fajnzylber, P., Mason, A., & Saavedra-Chanduvi, J. (2007). _Informality: Exit and Exclusion._ Washington DC: The World Bank.

Popova, D. (2012). Constructing the tax-benefit microsimulation for Russia- RUSMOD. _EUROMOD Working paper_ (EM7/12).

Popova, D. (n.d.). Impact Assessment of alternative reforms of Chil Allowances using RUSMOD, the static tax-benefit microsimulation model for Russia. _International Journal of Microsimulation, 6_ (1), 122-156.

Randelovic, S., & Zarkovic Rakic, J. (2013). Improving work incentives in Serbia: exaluation of a tax policy reform using SRMOD. _International Journal of Microsimulation, 6_ (1), 157-176.

- Rice, R. W. (1980). The job-satisfaction/life-satisfaction relationship: a review of empirical research. _Basic and Applied Social Psychology_ .

- Ryanzantsev, S. (2016, August 31). _Labor Migration from Central Asia to Russia in the context of the_

- _Economic Crisis._ Retrieved from Russia in Global Affaris:

http://eng.globalaffairs.ru/valday/Labour-Migration-from-Central-Asia-to-Russia-in-the-Contextof-the-Economic-Crisis-18334

- Sasin, M., Matytsin, M., & Popova, D. (2017). RUSMOD: A tool for Distributional Analysis in the Russian Federation. _unpublished manuscript_ .

Slonimczyk, F. (2013). Informality as a Stepping Stone. _HSE Working Paper WP3/2013/05_ .

Slonimczyk, F., & Gimpelson, V. (2013). Informality and Mobility: Evidence from Russian Panel Data. _IZA Discussion Paper_ (7703).

Timmer, M., & Voskoboynikov, I. (2014). Is Mining fuelling Long-run Growth in Russia? Indsutry Productivity Prowth Trends Since 1995. _The Review of Income and Wealth, 60_ (Supplement Issue), S398-S422.

- United Nations Department of Economic and Social Affairs. (2010). _System of National Accounts._ New York: United Nations.

Vanek, J., Chen, M., Carré, F., Heintz, J., & Hausmanns, R. (2014, April). Statistics on the Informal Economy: Definitions, Reional Estimates and Challenges. _WIEGO Working Paper (Statistics), 2_ . World Bank. (2016). _Russian Federation, Systematic Country Diagnostic: Pathwqays to Inclusive Growth._ Washington DC: World Bank Group.

World Bank. (2018). _Labor Regulations in Russia: An Overview._ Washington DC: World Bank.

Zajacova, A. a. (2011). Reliability of self-rated health in US adults. _American Journal of Epidemiology_ .

30 Informal Employment and Worker’s Wellbeing in Russia / June 2019
**LATIN AMERICA AND THE CARIBBEAN**

# **1. Estimates** and Projections

|**Tw**|**Pepiatim**|**.Gro**<br>ratio|**wth**<br>**Peiod**|**rate**|**cm**|CD|TFt|**0c**<br>**Nitm**|Fnlm|lo|**x100O**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1965|400015|72.7|1985-89|2.03|28.5|7.3|3.56|64.0|69.5|55|70|
|1990|"2670|68.0|1990-94|1.86|26.1|6.7|3.13|65.3|71.4|48|60|
|1995|**485S51**|63.9|1995-99|1.63|23.1|6.1|2.76|66.7|73.2|41|51|
|2000|527030|58.7|2000-04|1.43|20.6|5.8|2.45|68.1|75.0|34|42|
|2005|S66011|53.7|2005-09|1.30|19.2|5.8|2.29|69.2|76.1|29|36|
|2010|604103|49.8|2010-14|1.22|18.3|5.9|2.22|70.4|77.2|24|29|
|2015|**64222X**|48.1|2014-19|1.13|17.4|6.0|2.17|71.6|78.4|18|23|
|2020|679399|48.3|2020-24|1.00|16.2|6.1|2.12|72.8|79.6|13|17|
|2025|714215|49.1|2025-49|0 67|14.1|7.4|2.08|75.7|82.4|6|8|
|2050|845000|59.0|2050-74|0.26|12.6|10.0|2.07|78.8|85.4|3|5|
|2075|901675|67.4|2075-99|0.10|12.1|11.1|2.06|80.5|87.2|3|4|
|2100|924514|70.9|2100-24|0.06|11.8|11.3|2.06|81.7|88.6|2|3|
|2125<br>2150|937966<br>944567|73.0<br>73.9|2125-49|0.03|11.7|11.4|2.06|82.2|89.1|2|3|

# **2. Age structure (percent): 1990 and 2025**

<!-- Start of picture text --> 75+<br>; S 1 ~~~70-74 3<br>E B | BO~~8-69 § E<br>;3 2 1 ~~~55-59 2 SA<br>60-54<br>45-49<br>40-44<br>35-39<br>3% .34<br>25-29<br>20-24<br>Ir<3ggEEg2 15-19<br>FXXXRRRX SggaSBBBU 10-14<br>5-9<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0<br><!-- End of picture text -->

<!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# **3. Distribution of population (percent): 1990 and 2025**

<!-- Start of picture text --> ,wiEa c's<br>LAC C6I 31)<br><!-- End of picture text --> <!-- Start of picture text --> LC CS, 51<br><!-- End of picture text --> 10