rafmacalaba/datause-encoder-data huggingface dataset:
# **2 Where do we stand? A brief review of the literature**

The literature on employment quality has evolved significantly in recent decades. In the case of developed countries, Jencks et al. (1988) were the first to develop a job desirability index for the United States, which considered both monetary and non-monetary aspects of jobs. Similarly, Mu ̃noz de Bustillo et al. (2011) comprehensively review different perspectives and approaches to evaluating workers’ wellbeing and job quality, constructing a job quality index for the European region. Additionally, OECD (2015) introduced a Job Quality Framework that considers earnings, labor market security, and the quality of the working environment as key dimensions for job evaluation. Furthermore, the International Labour Organization (ILO) presented a set of statistical indicators related to the Decent Work Agenda in 2008 and 2009. These indicators encompass fundamental dimensions such as labor standards, rights at work, employment opportunities, social protection, and social dialogue. These indicators allow for studying and monitoring labor markets in developing countries, including their informal sectors. However, job quality measurement in developing countries has been more limited due to data availability constraints, which hinder the construction and comparison of job quality indices across different countries.

In the context of developing countries, some studies have utilized the Alkire & Foster (2011) framework to measure job quality. For example, in the case of Latin America, Brummund et al. (2018), Senhbruch et al. (2020), and Apablaza et al. (2021) propose multidimensional measures of job quality based on this method. Brummund et al. (2018) employ the Socio-Economic Database for Latin America and the Caribbean (SEDLAC) for a subset of 15 countries between 1995 and 2012. The authors use income, health insurance, retirement benefits, tenure, job contract, job stability, and job satisfaction components to create a composite job quality index. One of their key findings is that Argentina, Brazil, Chile, Costa Rica, and Uruguay exhibit higher job quality, primarily due to a lower share of their employed population working in the agriculture sector compared to other countries. Additionally, they found that the average job quality in the region increased by approximately 25 percent between 2004 and 2012.

On the other hand, Senhbruch et al. (2020) developed a synthetic indicator of the quality of employment for 10 countries in Latin America using household and labor force surveys from 2015.<sup>4</sup> The authors select three dimensions in their study, each operationalized

> 4The list of countries includes Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, and Uruguay.

4
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- Privatizing non-agricultural land plots, from July 2019

- Guaranteeing the implementation of the right of ownership of land

- Promoting the full and productive employment of the population of large cities

- Applying international experience in managing medium cities, considering the use of the advantages of agglomerations

- Expanding the network of satellite towns, adjacent to a large city within convenient commuting distance

To guide and manage these changes, the decree also stated that a new _Concept for the Development of Urbanization in Uzbekistan until 2030_ would be adopted, alongside a new Agency for Urbanization to be established under the Ministry of Economy and Industry. According to the decree, the mandate of the agency will be to implement a unified state policy in the sphere of urbanization regulation, longterm planning of rates, stages, and results of industrialization policy, and analysis of long-term demographic trends in cities and towns.

If successful, this constellation of reforms will enable a faster pace of urbanization than was previously permitted. But experience in other middle-income counties suggests that rapid urbanization often presents challenges for policy makers. Some segments of the population, especially longtime urban residents, do not always fully embrace sudden development in cities. New construction can also displace people if not accomplished in a fair and consultative manner. Thus, it is important to gauge the extent of any reluctance to support urban growth among the general population. However, the results of this study suggest the contrary is true in the present context: pro-growth urbanization policies are overwhelmingly popular within every major social group studied, and in rural and urban areas alike. However, the results do suggest that this support is vulnerable to concerns regarding fairness. In 2019, several high-profile incidents during which authorities demolished buildings against the wishes of owners were associated with falling enthusiasm for reform among the population, according to the results reported in section V of this paper.

The remainder of the paper is structured as follows: section II describes the data sources used in the analysis. Section III describes the setting, reviews relevant literature, and provides detailed statistics on domestic mobility rates, statistics on propiska registration, cost-of-living indexes, spatial earnings differentials, and measures of housing affordability in Uzbekistan. Section IV provides both descriptive and experimental results on citizens’ preferences for reform. Section V describes the association between highly publicized involuntary demolitions and citizen support for reform. Section VI concludes.

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|||_Table 2_<br>Al<br>|_. Cross-Se_<br>l<br>|_ctional Re_<br>|_gression o_<br>(Lo<br>|_f Trajector_<br>Lower-i<br>w- and Mi<br>|_ies with C_<br>ncome<br>ddle-Inco<br>|_ity Chara_<br>me)<br>|_cteristics_<br>|High<br>|-income<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)<br>Initial|(2)|(3)<br>Position|(4)<br> <br>TLL|(5)<br>Initial|(6)|(7)<br>Position|(8)<br> <br>TLL|(9)<br>Initial|(10)|(11)<br>Position|(12)<br> <br>TLL|
||Change|Depth|Dec‘20|in 2020|Change|Depth|Dec‘20|in 2020|Change|Depth|Dec‘20|in 2020|
|lpop|4.353***|<br>5.229***|<br>-0.261|51.50***|4.257**|5.506***|-0.607|50.73***|-1.856|3.221|5.838|46.15|
||(1.663)|(1.616)|(1.688)|(18.23)|(1.716)|(1.676)|(1.755)|(18.83)|(3.443)|(5.195)|(4.580)|(30.23)|
|larea|-8.328***|<br>-4.704**|<br>-1.973|-61.97**|-8.494***|-4.900**|-2.020|-65.08**|4.765|11.93|6.121|91.25|
||(2.382)|(2.242)|(2.654)|(27.66)|(2.466)|(2.327)|(2.754)|(28.81)|(5.988)|(8.189)|<br>(7.090)|(58.23)|
|lgdp|-0.452|-1.305|-2.545*|-20.41|-0.474|-1.135|-2.511|-19.12|1.696|-6.019*|<br>-3.845|-53.41***|
||(1.046)|(1.127)|(1.535)|(16.05)|(1.067)|(1.148)|(1.569)|(16.40)|(2.163)|(3.181)|(3.597)|(19.25)|
|lbuiltup|2.977**|1.118|-0.530|8.807|3.170**|1.056|-0.531|9.293|-7.059**|<br>-0.996|0.312|-4.772|
||(1.422)|(1.222)|(1.381)|(16.37)|(1.437)|(1.238)|(1.401)|(16.58)|(3.509)|(5.259)|<br>(4.548)|(41.22)|
|lroad|-0.122|0.430|3.955**|12.66|-0.245|0.569|4.242**|14.05|3.243|-8.160|-10.23**|<br>-76.82**|
||(1.721)|(1.217)|(1.646)|(16.07)|(1.750)|(1.233)|(1.674)|(16.32)|(3.422)|(5.010)|<br>(4.593)|(35.87)|
|lppr|2.289|3.205|1.664|48.07*|2.629|3.827|2.012|50.70|1.835|0.506|0.193|44.43*|
||(2.077)|(2.323)|(2.682)|(27.13)|(2.369)|(2.629)|(3.060)|(31.10)|(2.633)|(3.491)|(3.510)|(24.17)|
|N|2710|2733|2701|2753|2330|2353|2321|2371|380|380|380|382|
|R-sq|0.388|0.355|0.292|0.415|0.389|0.340|0.288|0.416|0.304|0.409|0.406|0.368|
|Country FE|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|

Note: All columns control for country FE. Robust standard error in parentheses. * p<0.1, ** p<0.05, *** p<0.01 17
Where Is Poverty Concentrated? New Evidence Based on Internationally Consistent Urban and Poverty Measurements<sup>,∗†</sup> Shohei Nakamura Pierre-Philippe Combes Robin Moellerherm Charlotte Robert Mark Roberts Benjamin Stewart Slava Yakubenko Keywords: Global poverty; urban poverty; urbanization; cost of living; Sub-Saharan Africa JEL Codes: I32, R12, O18

> ∗Nakamura: World Bank; snakamura2@worldbank.org. Combes: Sciences Po; pierrephilippe.combes@scie ncespo.fr. Moellerherm: Heidelberg University; robin.moellerherm@awi.uni-heidelberg.de. Robert: Heidelberg University; charlotte.robert@awi.uni-heidelberg.de. Roberts: World Bank; mroberts1@worldbank.org. Stewart: World Bank; bstewart@worldbankgroup.org. Yakubenko: Westminster International University in Tashkent and HSE University; viacheslav.yakubenko@wiwi.uni-goettingen.de.

> †We would like to acknowledge the support for data preparation and analysis from Maksym Bondarenko, Clément Gorin, Kengo Igei, Juri Ishimoto, Heba el Laithy, Anirudh Rajashekar, and Alessandro Sorichetta. We are also grateful to Samuel Baah, Judy Baker, Somik Lall, Peter Lanjouw, Yue Li, Daniel Mahler, Megha Mukim, Ambar Narayan, Nistha Sinha, and Nobuo Yoshida for their input. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations or those of the Executive Directors of the World Bank or the governments they represent.
_Low-Income Countries’ Access to Private Debt Markets_

**Table 2 OLS Estimates of equation (1) over the period 2003-07**

|_Capital flow_|β1||β2||R<sup>2</sup>|SER|N|
|---|---|---|---|---|---|---|---|
|Debt and equity|-0.078*|(0.042)|0.471***|(0.072)|0.259|0.959|130|
|Debt|-0.075|(0.048)|0.573***|(0.086)|0.319|1.017|110|
|Equity|-0.132***|(0.047)|0.357***|(0.088)|0.138|1.023|125|
|Bank loans|-0.033|(0.060)|0.379***|(0.087)|0.153|1.076|109|
|Bond issuance|-0.366***|(0.065)|0.375***|(0.136)|0.303|0.989|67|
|FDI|-0.174***|(0.043)|0.368***|(0.084)|0.167|0.985|125|
|Portfolio equity|0.357***|(0.099)|0.279|(0.182)|0.158|1.665|61|
|ODA|-0.505***|(0.039)|-1.033***|(0.069)|0.832|0.777|107|

|||_Common_|_sample peri_|_ods:_||||
|---|---|---|---|---|---|---|---|
|Debt|-0.072|(0.062)|0.566***|(0.083)|0.315|1.019|105|
|Equity|-0.153***|(0.052)|0.280***|(0.070)|0.149|0.859|105|
|Bank loans|0.014|(0.080)|0.359***|(0.123)|0.119|0.977|63|
|Bond issuance|-0.334***|(0.077)|0.407***|(0.118)|0.330|0.933|63|
|FDI|-0.220***|(0.067)|0.133|(0.102)|0.167|0.810|63|

_Notes:_ ***, ** and * denote significant at the 1%, 5% and 10% levels, respectively.

Estimated standard errors shown in parentheses.

SER = standard error of the regression; N = number of countries in sample.

ODA = net ODA (Official Development Assistance) disbursements.

Most low-income countries have accessed syndicated bank loans at least once since the early 1990s, but very few on a frequent basis. The average volume of syndicated bank loan transactions in 2003-07 was less than 0.2 percent of GDP in one half of low-income countries. Moreover, few lowincome countries have ever issued bonds in the international market. Only 7 of 48 low-income countries in our sample have issued an external bond since 1980. Of those, only India and Pakistan have accessed the international bond market on a frequent basis. India has been active since the early 1980s, with bond 10
income levels, urbanization rate, population density, fertility, access to finance, and the rule of law are also critical for accumulating infrastructure stocks. Similar findings are reported by Randolph et al. (1999) when analyzing the factors affecting investment in transportation and communication infrastructure in 27 low and middle-income economies for the period 1980-86. They show that economic development, urbanization rate, trade openness, government expenditure, and labor force participation rate significantly impact the per-capita infrastructure spending. Banerjee et al. (2006) find increasing importance of private finance to accumulate infrastructure stocks. Using panel data from 40 developing countries during the 1990-2000 period, it shows factors that are necessary for attracting private investment (e.g., property rights, strong and fair judicial system, strong regulatory system, better contract enforcement, and a low risk of expropriation of assets) are also instrumental for those infrastructures where the private sector can play a bigger role (e.g., power generation, telecommunication, toll roads). Dao (2008) further explains the complementarity between the private and public investment in infrastructure through a study using infrastructure data from 27 developing countries. It finds that public investment complements private investment. However, public expenditure on education is found to compete with spending on telecommunication and water resources. In low-income countries, however, physical infrastructure with the exception of cellular telephone systems, is primarily provisioned by the public sector (Guarara et al. 2017). Grants and concessional loans from development partners also played an important role in infrastructure funding in such countries. Analyzing the macroeconomic risks associated with infrastructure booms in 105 advanced and emerging economies, Walsh _et al._ (2011) find that economic growth and availability of financing (e.g., developed capital market and institutions), affect infrastructure investments.

<mark>In the case of Latin American countries (LACs), Cerra et al. (2016) f</mark> ind a large variation in infrastructure development. Some of the factors contributing to the variations are different levels of public investment in infrastructure, different degrees of private sector participation, interdependence among types of infrastructure, large gaps in per capita income, rate of urbanization, the disparity in access to finance and institutional capacities to enforce rules and regulation. They also find that fiscal policies and institutions play a critical role in improving the infrastructure network. Similarly, Lora (2007) assesses the impact of the state of public finances on public infrastructure investment (PII) in seven LACs. The results indicate that increase in public debt leads to increases in PII, while exogenous debt (exogenous is 5
data on the voting records of U.S. congresspersons to provide persuasive evidence that congresspersons with female children tend to vote in favor of reproductive rights issues, such as teen access to contraceptives. Furthermore, Washington (2008) argues for broader results: congresspersons support a range of measures that favor women, such as flexibility for working families and tax-free education. Oswald and Powdthavee (2010) model the idea that daughters increase parents’ left-wing tendencies. Their model incorporates the presence of pay discrimination and the fact that women derive greater utility from public goods than men do. In this scenario, women prefer a larger supply of the public good and a greater tax rate on income because their marginal utility from the former is relatively high, and the tax penalty they face as a result of the latter is relatively low. Because men have female children, however, they shift their political stance and become more sympathetic to the female desire for a steeper income tax schedule and a larger amount of the public good, so they become more left wing. Similarly, a mother with many sons becomes sympathetic to the male case for lower taxes and a smaller supply of public goods and becomes more right wing. Their theory is supported by German and U.K. data.

Although it seems that the gender of offspring affects an individual’s attitudes, previous evidence has only examined political views. It would be interesting to investigate whether the gender of offspring has implications for practices linked to the labor market, such as employers’ hiring decisions and wage-setting structures.

Overall, previous evidence indicates that men’s behaviors and attitudes toward women are likely to be affected by a range of factors (i.e., technological progress, media exposure, wage gaps, sex ratio, and offspring’s gender). Given the considerable influence that men exert on the economic development of women, these factors should be considered in the design of public policies intended to improve women’s well-being.

# **<<A>>The Role of Men in the Socioeconomic and Health Outcomes of Women**

This section reviews recent studies that identify an important role of fathers and husbands for the well-being of their wives and daughters. It focuses on the access to education and health institutions, as well as on public and private arrangements that influence women's economic and social outcomes.

**<<B>>** _Reproductive Health and Sexual Behavior_

8
- Premchander, S., Bloesch, U., Tuladhar, B., Raghunandan, D., 2011. External Review of Clean Building Technologies for Nepal: VSBK-CESEF Project 2008-2011. Government of Nepal (GON) and Swiss Agency for Development and Cooperation (SDC), Kathmandu.

- PSCST, 2015. Utilization of Paddy Straw as Fuel in Brick Kilns. Punjab State Council for Science & Technology (PSCST), Chandigarh.

- Rajarathnam, U., Athalye, V., Ragavan, S., Maithel, S., Lalchandani, D., Kumar, S., Baum, E., Weyant, C., Bond, T., 2014. Assessment of air pollutant emissions from brick kilns. Atmospheric Environment 98, 549-553.

- Rawal, R., Maithel, S., Shukla, Y., Rana, S., Gowri, G., Patel, J., Kumar, S., 2020. Thermal Performance of Walling Material and Wall Technology, Part-1. Shakti Sustainable Energy Foundation, Greentech Knowledge Solutions Pvt. Ltd., Centre for Advanced Research in Building Science and Energy, New Delhi and Ahmedabad.

- Raza, A., Ali, Z., 2021. Impact of air pollution generated by brick kilns on the pulmonary health of workers. Journal of Health Pollution 11, 210906.

- Raza, A., Qamer, M.F., Afsheen, S., Adnan, M., Naeem, S., Atiq, M., 2014. Particulate Matter Associated Lung Function Decline in Brick Kiln Workers of Jalalpur Jattan, Pakistan. Pak. J. Zool. 46, 237-243.

- Sadavarte, P., Rupakheti, M., Bhave, P., Shakya, K., Lawrence, M., 2019. Nepal emission inventory – Part I: Technologies and combustion sources (NEEMI-Tech) for 2001–2016. Atmos. Chem. Phys. 19, 12953-12973.

- Sah, D.P., Chaudhary, S., Shakya, R., Sah, P.K., Mishra, A.K., 2019. Status of Brick Kilns Stack Emission in Kathmandu Valley of Nepal. Journal of Advanced Research in Civil and Environmental Engineering 6, 1-9.

- Sanjel, S., Khanal, S.N., Thygerson, S.M., Carter, W.S., Johnston, J.D., Joshi, S.K., 2017. Respiratory symptoms and illnesses related to the concentration of airborne particulate matter among brick kiln workers in Kathmandu valley, Nepal. Ann. Occup. Environ. Med. 29, 12.

- Seay, B., Adetona, A., Sadoff, N., Sarofim, M.C., Kolian, M., 2021. Impact of South Asian brick kiln emission mitigation strategies on select pollutants and near-term Arctic temperature responses. Environmental Research Communications 3, 061004.

- Shrestha, S., 2019. A case study of brick properties manufacture in Bhaktapur. Journal of Science and Engineering 7, 27-33.

- Shriraam, V., Srihari, R., Gayathri, T., Murali, L., 2020. Active case finding for Tuberculosis among migrant brick kiln workers in South India. Indian Journal of Tuberculosis 67, 38-42.

- Siraz, M.M.M., Rakib, M.D.A., Alam, M.S., Al Mahmud, J., Rashid, M.B., Khandaker, M.U., Islam, M.S., Yeasmin, S., 2023. Assessment of radionuclides from coal-fired brick kilns on the outskirts of Dhaka city and the consequent hazards on human health and the environment. Nuclear Engineering and Technology.

40
2020; Azlor et al., 2020; Fasani et al., 2022; Mart ́en et al., 2019; Edin et al., 2003). Similarly, living in camps is associated with higher inactivity in T ̈urkiye and lower earnings in Jordan and Iraq (which are offset by subsidies offered in camps), as well as the quality of life in Jordan beyond income (Ginn, 2020; Kayaoglu and Erdogan, 2019; Obi, 2021).

# **5 Longer-term outcomes**

In the longer term, refugees either return to their origin countries or settle in their host communities (integrating locally, resettling to a third country, or staying in so-called protracted situations).<sup>6</sup> The median duration of exile was five years as of 2018, Devictor and Do (2017) find. The number of returns of IDPs is higher than for refugees, but the count for IDPs is less reliable. The outcomes of those who return are discussed first, followed by the outcomes of those forcibly displaced and their children who stay in host communities.

## **5.1 Returning to the origin community**

Studies on factors influencing return decisions show the importance of the security situation, economic opportunities, property rights, networks, and services in the origin community, and the quality of information about these factors (Arias et al., 2014; Camarena and Hagerdal, 2020; Stefanovic et al., 2015; Weber and Hartman, 2021). Recent investigations find that conditions in the origin country are the main factor for intentions to return and actual returns of Syrian refugees in neighboring countries as well as Germany (Alrababa’h et al., 2020; Beaman et al., 2022; Kaya and Orchard, 2020). While some previous studies find that a better situation at the destination is associated with lower return intentions (Arias et al., 2014; Balcilar and Nugent, 2019; Stefanovic et al., 2015), more recent studies show that the situation at the destination does not play a role or is even positively correlated with return (Alrababa’h et al., 2020; Beaman et al., 2022). When the forcibly displaced are better off at their destination they have more to lose when returning but at the same time, they have the resources to actually return. This explanation is in line with findings showing that those

> 6Local integration is not yet measured across different countries. Naturalization is used as a proxy but also faces challenges in terms of data availability and coverage. In 2020, data were available from 28 host countries.

15