rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4529

# **Abstract**

It is routinely assumed that residents of post-socialist countries have a preference for greater income equality, other things being equal, owing to the legacy of socialism. This proposition is examined in the context of Eastern Europe and the former Soviet Union using data from three waves of the _World Values Survey_ . Contrary to expectations, the authors find little evidence of a ‘socialist legacy’ _en bloc_ . Considering the former Soviet Union separately from other post-socialist countries, the analysis finds that as a group these countries display significantly lower preference for moving toward greater income equality than both Eastern Europe and other comparator groups (developed and developing countries). These findings hold up even when controlling for the conventional determinants of attitudes such as income level and employment status of the individual respondent, as well as national factors such as per-capita income and its distribution. Moreover, the preference for greater income inequality appears to have persisted at least since the mid-1990s and possibly since the early 1990s (data difficulties preclude a robust examination of this latter question). The results are consistent with the fairly low levels of public spending on redistribution commonly found in the former Soviet Union.

This paper—a product of the Poverty Reduction and Economic Management Sector and Human Development Sector Units, Europe and Central Asia Region—is part of a larger effort in the department to understand the economic transition in former centrally planned economies. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at mmurthi@worldbank.org and etiongson@worldbank.org.

_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 the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
you felt down, depressed, or hopeless?” (iii) ”Over the last 2 weeks, how often have you had poor appetite or overeating?” (iv) ”Over the last 2 weeks, how often have you had thoughts that you would be better off dead or of hurting yourself in some way?” and (v) ”Did you seek mental health assistance during the last three months? (1=Yes or 0=No)” The Mental Health index is constructed by calculated the average of the five outcome variables explained above. The average is calculated just with the information available for each individual, it excluded from the average the missing answers.

- Labor Market Access: we measured the labor market access dimension using the following variables: (i) Employed is an indicator equal to 1 if the respondent reported to have worked for remuneration for at least one hour in the last 7 days, (ii) Weekly Hours Worked: is the total number of reported hours the respondent spent working in the last 7 days, and (iii) Monthly Wage: is the total amount in somoni that the respondent earned from salary or self-employed profits after taxes in the last 30 days.

The Labor Market Access Index is constructed using the methodology of Kling, Liebman and Katz (2007) with the variables explained above. It implies i) standardizing the variables, ii) averaging, and iii) standardizing the final average again.

- Income and Consumption: we measured the income and consumption dimension using the answer to the following questions: (i) ”Approximately, what was the total household income in the last month? (amount in Somoni)”, and (ii) ”Approximately, how much did your household spend in the last month in total? (amount in Somoni)”. With these two questions, we created the Income and Consumption Index using the methodology of Kling, Liebman and Katz (2007). It implies i) standardizing the variables, ii) averaging, and iii) standardizing the final average again.

# **B.B Treatment Variables**

Treatment variables were constructed using the online data from the Foundation for Defense of Democracies’ Long War Journal (LWJ). They map the districts controlled by Taliban, by U.S. allies, and contested territories between 2014 to 2021, when the Taliban’ took control over the total territory. The classifications are based on open-source information, such as press reports and information provided by government agencies and the Taliban. With this information, they defined the following:

- Taliban and U.S allies controlled districts: places where the Taliban or the U.S. allies

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|Participation<br>Ratio||||0.091***||||0.174***||||0.055***|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||13.05||||11.98||||6.82|
|_cons|157.635***|157.246***|158.781***|152.888***|157.263***|158.611***|159.879***|146.369***|154.198***|152.828***|153.189***|150.478***|
||217.36|173.75|136.42|121.88|144.11|115.96|71.21|57.63|85.21|80.1|69.16|67.23|
|R Adj sq.|0.218|0.239|0.24|0.258|0.355|0.38|0.382|0.422|0.075|0.088|0.089|0.098|
|N|10730|10723|10723|10723|4890|4886|4886|4886|5840|5837|5837|5837|

The regression also includes oblast dummies, dummies for different types of schools and evening schools. The full table can be found in excel tables which can be made available upon request. *** means significant at the 1% level, ** at the 5 % level and * at the 10 percent level.T statistics are based on robust standard errors for the OLS specifications.

# **_Table 9b – Male Students, Ukrainian_**

|||To<br>|tal<br>|||Ur<br>|ban<br>|||Ru<br>|ral<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|# students|0.010***|0.009***|0.010***|0.009***|0.009***|0.009***|0.016***|0.009***|0|0|0.009*|0.010*|
||17.25|16.22|6.09|5.38|15.34|14.99|7.9|4.88|0.04|-0.02|1.65|1.73|
|(# students)<sup>2</sup>|||0|0|||-0.000***|0|||-0.000*|-0.000*|
||||-0.78|-0.27|||-3.63|-1.3|||-1.77|-1.83|
|Student/Teacher|-0.201***|-0.206***|-0.479***|-0.503***|-0.048|-0.076|-0.381*|-0.469**|-0.036|-0.071|-0.178|-0.164|
||-3.42|-3.53|-3.18|-3.34|-0.66|-1.04|-1.78|-2.24|-0.34|-0.68|-0.57|-0.53|
|(Stud./Teacher)<sup>2</sup>|||0.014***|0.016***|||0.012*|0.016**|||0.001|0|
||||2.6|2.8|||1.69|2.3|||0.07|0.01|
|Class Size|-0.100***|-0.101***|-0.487***|-0.460***|0.005|0.004|-0.602***|-0.552***|-0.084**|-0.097***|-0.247*|-0.250*|
||-4.55|-4.52|-5.53|-5.22|0.16|0.11|-3.54|-3.3|-2.57|-2.98|-1.92|-1.94|
|(Class Size)<sup>2</sup>|||0.011***|0.010***|||0.014***|0.013***|||0.004|0.004|
||||5.03|4.81|||3.86|3.57|||1.18|1.17|
|Urban|2.099***|2.256***|2.424***|2.087***|||||||||
||6.64|7.02|7.11|6.1|||||||||
|Gender Comp.||-0.024***|-0.028***|-0.025***||-0.038***|-0.041***|-0.040***||-0.021**|-0.021**|-0.022**|
|||-3.04|-3.57|-3.26||-2.95|-3.15|-3.12||-2.17|-2.24|-2.32|
|Unemployment<br>rate 2009||-0.054|-0.064|-0.084||0.023|0.018|-0.028||-0.163|-0.159|-0.153|

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-2-

# 1. Int duct ion

Atter the "adjustment decade" of the 1980s, attention<sup>in the 1990s seems</sup> to be turning once again to longer term issues of<sup>development, particularly of</sup> poverty alleviation. If the publication of major reports<sup>by international</sup> agencies is anything to go by, the shift in attention<sup>is quite marked. Just</sup> as the 1980s were heralded by a series of reports<sup>on adjustment (for example,</sup> the World Bank's report on adjustment in Africa),<sup>1990 has seen the</sup> puJlication of two major reports on poverty: World Development<sup>Report 1990:</sup> Poverty (WDR), by the World Bank, and Human Development<sup>Report 1990 (HDR), by</sup> the UNDP. The World Bank's report, the thirteenth in<sup>a series of annual</sup> reports on different development issues, revisits the<sup>topic of the World</sup> Development Report 1980, while the UNDP's report is the<sup>first in a planned</sup> annual series focussing on human development.

It seems an opportune ti,me, therefore to take stack<sup>of what we know</sup> about poverty and development, where we stand on conceptual<sup>issues and what</sup> experience has taught us about the best policies<sup>for alleviating poverty. The</sup> object of this paper is to attempt such an overview, based<sup>on the t4o major</sup> reports published this year, and on the literature on which<sup>they draw. Ihe</sup> plan of the paper is as follows. Section 2 starts<sup>by posing some basic</sup> questions on the definition and measurement of poverty,<sup>emphasizing the need</sup> for operational methods that have sound methodological basis.<sup>Section 3 moves</sup> on from this to take a look at what has actually happened to poverty<sup>in</sup> developing countries ever the past three decades. Section<sup>4 turns to the most</sup> important question -- what policies help to alleviate poverty?<sup>Section 5</sup> concludes the paper.
Panama). The disability gap in EPR tends to be larger for older age groups (Appendix B3).

The EPR is further analyzed in Figure 4.3, where the relative employment rate is pictured: it is the EPR for persons with functional difficulties divided by the EPR for persons with no difficulty. For instance, in Afghanistan, the relative employment rate is 76%, meaning that the EPR of persons with any difficulty is 76% that of persons with no difficulty. In Figure 4.3, the relative employment rate ranges from 43% in Panama to 100% in Liberia, Vanuatu, and Papua New Guinea. For countries with a graded scale for functional difficulty questions, Figure 4.3 shows that the relative employment rate is consistently lower for persons with at least a lot of difficulty compared to persons with some difficulty.

Figure 4.3: Relative employment rates <!-- Start of picture text -->
1.00<br> 0.90<br> 0.80<br> 0.70<br> 0.60<br> 0.50<br> 0.40<br> 0.30<br> 0.20<br> 0.10<br> -<br>Any difficulty Some difficulty At least a lot of difficulty<br><!-- End of picture text -->

Table 4.2 presents other employment related indicators. The unemployment rate could be calculated for countries that had information on job search status. The unemployment rate is significantly higher among persons with any difficulty for six countries (Afghanistan, Dominican Rep., Ethiopia, Mexico, Rwanda (Census), Tanzania (LSMS)) but lower in eight countries (Indonesia, Kiribati, Namibia, Panama, Rwanda (LFSO), South Africa (Census and GHS), Vanuatu and Vietnam. Coupled with often significantly lower employment population ratios described above, this reflects that persons with difficulties are more likely to be both out of work _and_ out of the labor force, and thus not captured in the unemployment rate. Persons with functional difficulties may disproportionately drop out of the labor force by stopping to search for work and hence not be counted as unemployed. The idle rate for youths aged 15 to 24 is significantly higher for youths with functional difficulties in 15 countries, with the largest disability gap in Vietnam. The disability gap is larger for persons with at least a lot of difficulty compared to persons with some difficulty. For Papua New Guinea, results in panel 3 shows that the idle rate for youths is significantly higher for youths with at least a lot of functional difficulty but lower for youths with some difficulty, explaining the lower rate for youth with any difficulty in panel 1.

25
Kose, A., and M. Terrones. 2015. Collapse and Revival: Understanding Global Recessions and Recoveries. Washington, DC: International Monetary Fund.

Kose, M. A., and K.-M. Yi. 2006. “Can the Standard International Business Cycle Model Explain the Relation between Trade and Comovement?” Journal of International Economics 68 (2): 26795.

Martínez-García, E. 2015. “The Global Component of Local Inflation: Revisiting the Empirical Content of the Global Slack Hypothesis with Bayesian Methods.” In Monetary Policy in the Context of the Financial Crisis: New Challenges and Lessons, edited by W. A. Barnett and Fredj Jawadi, 51-112. Bingley, United Kingdom: Emerald Group Publishing Limited.

Monacelli, T., and L. Sala. 2009. “The International Dimension of In fl ation: Evidence from Disaggregated Consumer Price Data.” Journal of Money, Credit, and Banking 41(s1): 101-20.

Mumtaz, H., S. Simonelli, and P. Surico. 2011. “International Comovements, Business Cycle and In fl ation: A Historical Perspective.” Review of Economic Dynamics 14 (1): 176-98.

Mumtaz, H., and P. Surico. 2012. “Evolving International Inflation Dynamics: World and Country-Specific Factors.” Journal of the European Economic Association 10 (4): 716-34.

Neely, C. J., and D. E. Rapach. 2011. “International Comovements in Inflation Rates and Country Characteristics.” Journal of International Money and Finance 30 (7): 1471-90.

Parker, M. 2018. “How Global Is ‘Global Inflation’?” Journal of Macroeconomics 58 (December): 174-97.

Rogoff, K. 2003. “Globalization and Global Disinflation.” Federal Reserve Bank of Kansas City Economic Review 88 (4): 45.

Shambaugh, J. C. 2008. “A New Look at Pass-Through.” Journal of International Money and Finance 27 (4): 560-91.

Wang, P., and Y. Wen. 2007. “In fl ation Dynamics: A Cross-Country Investigation.” Journal of Monetary Economics 54 (7): 2004-31.

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# **Figure 2:**

<!-- Start of picture text --> Duration of regulatory improvement<br>process<br>60<br>50<br>40<br>30 2008<br>2009<br>20<br>10<br>0<br>A B C D<br>Days<br><!-- End of picture text --> Key: Route A: Total approval with final effects Route B: Preliminary approval + Final approval Route C: Adjustments and corrections + Total approval with final effects Route D: Adjustments and corrections + Preliminary approval + Final approval Source: “Redesign of the Regulatory Improvement Process”, COFEMER, July 26, 2010.

As in the U.S., the RIA process establishes a set of standard criteria to determine if the regulation is justified:

- an explanation of the underlying situation and why the government needs to be involved;

- a justification of the specific obligations imposed on private agents; an analysis of the potential effects of the regulations; and

- the identification of requirements created, modified or eliminated by the rule.

Until recently, any regulation that had expected annual costs greater than MEX$800 million was routinely considered high impact. If a regulation specifically affected a particular industry, area of commerce, or geographic population it also may be considered 22
Figure 7: Financing to the Private Sector by Source (% GDP) Figure 8: Composition of Financing by Public Sector (% GDP) <!-- Start of picture text --> 30% 3.0%<br>25% 2.5%<br>5.2% 0.5%<br>3.8%<br>20% 1.5% 2.6% 2.0%<br>15%<br>1.5% 0.1%<br>10.8% 12.9% 1.6%<br>10%<br>1.0% 0.7%<br>5%<br>7.4% 5.4% 0.5%<br>0.7%<br>0.5%<br>0%<br>0.0%<br>4Q00 3Q05<br>4Q00 3Q05<br>Foreign Private domestic Public Infonavit First tier Second tier Guarantees<br>% of GDP % of GDP<br><!-- End of picture text -->

## Source: BOM and SHCP.

Note: FIRA and Financiera Rural lending, certain public sector retirement programs financing mortgages (e.g. FOVISSSTE) and mortgage/asset-backed securities are excluded. Only performing loans are included in the analysis. Foreign financing consists of cross-border bank lending and bond issuance abroad. Private domestic financing consists of lending by non-banks, domestic bond issuance by non-financial private sector firms, as well as that part of lending by commercial banks and Sofoles that is not related to public sector support (i.e. it excludes second-tier development bank lending and credit guarantees).

# **_Financing Sources outside the Financial System_**

Financing sources outside the formal financial system might explain the relatively low level of private sector credit to GDP, but data are scarce. As can be seen in figures 9 and 10, Mexico’s domestic private sector credit as a percentage of GDP in 2004 was roughly at par with the level of the late 1970s<sup>12</sup> . This level is low when compared to countries of similar per capita income and economic size, which might suggest that financing outside the formal financial system is an important source of funding for the private sector. Examples of this type of financing include the reinvestment of retained earnings<sup>13</sup> , supplier credit (see section IV) and the numerous providers of unofficial household financing<sup>14</sup> , but there is a paucity of data to substantiate this hypothesis. In the case of Mexico, foreign financing is also important – according to the BOM, foreign bond issuance and cross-border bank lending amounted to around 5.4 percent of GDP as of 2005Q3.

> 12 2004 figures are used in the analysis because of the unavailability of 2005 figures for all countries. 13 An interesting hypothesis, which cannot be tested due to lack of available data, is that Mexican SMEs tend to rely more on retained earnings than their foreign counterparts and would hence have relatively lower leverage ratios.

> 14 These include _tiendas comerciales_ , _casas de empeño_ , _agiotistas_ , _familiares_ , _tandas_ , _autofinancieras_ and _armadoras_ . According to a recent exercise undertaken by BOM using published financial statements, consumer credit by non-financial publicly listed companies (e.g. department stores) amounted to MXP 30.7 billion, or 12 percent of total household financing.

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# **2.1 Demand-side incentives**

There is a growing empirical literature on the impact of conditional cash transfers, of which scholarships are one form, in low- and middle-income countries (Baird et al. 2014; Barham et al. 2013; García and Saavedra 2017; Snilstveit et al. 2015).<sup>5</sup> Three studies are of particular relevance given their similar designs and scope. First, Friedman et al. (2011) and Jakiela et al. (2015) present experimental evidence on the effects of a Kenyan merit-scholarship program for sixth-grade girls, nine years postintervention. The studies find that short-term impacts on educational attainment and cognitive skill (initially reported in Kremer et al. (2009)) result in greater female empowerment, improved political knowledge and attitudes in young adulthood (finding weaker evidence for effects on political behaviors). Second, Barham et al. (2013) evaluate the long-run effects of a conditional cash transfer program targeted to poor families in Nicaragua. They find that boys who were 9-12 at the time of the program attained about half a year more schooling when they were 19-22 than boys in a comparison group, and subsequently had better labor market outcomes (the difference for girls was not statistically different from zero between the treatment and comparison groups). Third, Duflo et al. (2017) evaluate the long-term effects of a secondary school scholarship program, in Ghana. This randomized evaluation finds that the program delayed fertility and marriage, improved educational attainment, cognitive skill, and reproductive and health behaviors, and had heterogeneous impacts on earnings. Our study builds on these: The Kenyan study does not report impacts on earnings, and the Ghana and Nicaragua studies investigate few impacts on socioemotional outcomes. Our paper includes indicators for both types of outcomes, and allows for a contrast of targeting approaches (building on Barrera-Osorio and Filmer (2016)). Together, these evaluations inform the degree to which individual findings from specific contexts might have broader external validity (Vivalt 2017).

> 5Scholarships may also be designed as incentive mechanisms, where payments are made based on future performance. See Fryer Jr (2011) for related evidence from the US. See Berry (2015), Blimpo (2014), and Li et al. (2014) for examples of related research from India, Benin, and China, respectively. We study scholarships whose payout is not (or arguably, only weakly) incentivized. See Section 3, below, for a description of the scholarship program.

6
covered by the Enterprise Surveys.<sup>2</sup> Back-up generation is particularly common in low-income countries, as they are more prone to experiencing unreliable electricity supply through the central grid Figure 8.

Figure 7: Generator ownership and usage relative to firm size (based on World Bank Enterprise Surveys) <!-- Start of picture text --> 35% 40%<br>30% 35%<br>30%<br>25%<br>25%<br>20%<br>20%<br>15%<br>15%<br>10%<br>10%<br>5% 5%<br>0% 0%<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Number of employees (percentiles) Number of employees (percentiles)<br>Figure 8: Self-generated electricity relative to wealth and power network reliability (based on World Bank<br>Enterprise Surveys)<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>0 10,000 20,000 30,000 40,000 0 500 1,000 1,500 2,000 2,500<br>GDP per capita Outage hours per year<br>generator<br>total electricity use<br>Share of firms owning a<br>Self-generation as share of<br>electricity (%) electricity (%)<br>Share of self-generated Share of self-generated<br><!-- End of picture text --> Various studies have shown that such self-generation is significantly more expensive than conventional grid supply (Adenikinju, 2003; Foster & Steinbuks, 2009). For instance, for 25 African countries, Foster & Steinbuks (2009) show that self-generation is on average three times as expensive as national electricity tariffs. Despite these extra costs, firms from the 137 countries covered by the World Bank’s Enterprise Surveys generate on average 22.3% of their electricity needs using back-up generators. In the top 20 countries the self-generation share is as high as 40%. These figures suggest that self-generation impose significant costs on firms around the world and cannot be disregarded.

By estimating the installed self-generation capacity and the total annual self-generation amount, it is possible to estimate the costs of back-up electricity generation in the industrial sectors of 129 countries. For this purpose both the annualized up-front investment costs and the operation costs are taken into account.

2 Information on generation is not available in Sweden, which was excluded from the analysis.

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**_<u>________________________________________________________</u>_**

|**INFORM**<br>|**ATION AN**<br>|**D COMMUNIC**<br>|**ATION TECHNOLOGY**<br>|
|---|---|---|---|
|The WTO Services<br>Database|Member<br>states|Up to 2004|schedules of commitments and lists of<br>Article II (MFN) exemptions of WTO<br>Members|
|World Bank : ICT at a<br>Glance|184|2001|ICT infrastructure and access, Computer<br>and internet information, ICD business<br>and government environment data.|
|McConnell International<br>Report|53|2001-2|E-readiness|
|International<br>Telecommunications<br>Union : World<br>Telecommunications<br>Indicators Database|200|1960, 1965,<br>1970, 1975-<br>2003|80 telecommunication indicators<br>(Telephone mainlines per 1000 people;<br>Waiting List in thousands; International<br>telecommunications, outgoing traffic;<br>Number of personal computers; Internet<br>Users;InternetHosts)|
|World Economic Forum<br>: Global Information<br>Technology Report The<br>Networked Readiness<br>Index(NRI)|104|annual|Networked Readiness Index; Broadband<br>internet access availability; Local<br>specialized IT services availability;|
|OECD : Key ICT<br>indicators|80|Current|Number of websites, number of internet<br>hosts per 1000 inhabitants, number of<br>businesses with internet access, internet<br>penetration by activity, businesses using<br>internetforpurchasing and selling etc.|
|Bridges: E-Assessment<br>Reports||2000-2002|Competition in ISPs; Government<br>online services availability|
|NUA International||current|Numberofpeople online (million)|

65 ____________________________________________________________________________________________________________