rafmacalaba/datause-encoder-data huggingface dataset:
# Part I: The Existing System

# Old-Age Security in the Command Economy

Public pension systems in EECA countries began at about the same time as in the OECD countries - during the first decades of the 20th century. Prior to World War II, most were modest systems, intended to be a funded system of comprehensive 'worker's insurance', based on the German model. Most formal sector workers were covered, but the self- employed, small businesses, and those working in agriculture were in general not covered.

These modest systems changed dramatically after World War II, as central planning was introduced across Centrl and Eastern Europe. Coverage expanded rapidly, as these systems became the third leg of the cradle-to-grave minimum income and living standard security which socialism promised citizenry who played according to the rules of the game.' The first leg was support for families with children: universal family benefits, child care, education, health services, etc. The second leg covered working life, and promised a job (accrding to the individual's skil level), with a modest and basically un-ifferentiated wage, for the active period (18 or school leaving age to _55_ for women, 60 for men), coupled _-1th_ full short-term benefits (sickhn and maternity leave, worker's compensation, etc.).<sup>2</sup> The third leg was a pension, with almost complete salary replacement. Together, the three legs assured income security to households.

The introduction of the command economy changed household behavior dramat;cally over the post-war period. Guaranteed jobs and child benefits, combined with the prevailing political view that both sexes had an obligation to work and the economic difficulty of living on one salary, sentumillions of women-into the labor force, such that today, EECA countris have the highest female labor force participation rates in the world (World Bank, 1993). Indeed, in Russia, women outnumber men in the workforce. At the same time, labor force participation declined among workers over 55 (Mitchell, 1992). Given the high returns to unpaid work under central planning (e.g. standing in food rationing queues, getting permits, etc.), and a rationing system which tended to award consumer durables and housing based on age or time in queue, the older population could live reasonably well on a pension income, small accumulatod assets and housing entitlements. In rural areas, the elderly usually had access to agricultual land for a subsistence garden plot as well. This command economy ' Me-ing, worked directly for the state in state enterprises, or in statsponsored cooperatives; about 9596 of the populan in most countries.

> **2** In countries with a large rual population, a larger eamnings differential was observed in the 1980s. However, this differenil never came close to that commonly observec **i** n non-EECA countries with similr income levels. See Atlinson and Mickelwright, (1993) for this analysis.
|Resolution<br>of<br>the Cabinet of<br>Ministries<br>No - 618|and institutions<br>that are allowed<br>to apply for<br>permanent<br>registration of<br>their<br>employees”|improving the activity of the Chamber of Commerce and Industries of the Republic of Uzbekistan” of 19 June 2017, the Cabinet of Ministries<br>decides:<br>To make an addition by paragraph No 61(1) to the list of the of public authorities, state and economic management bodies, other public institutions<br>of republican significance, whose senior officials may apply for provision of permanent registration for their employees in Tashkent city and Tashkent<br>region approved under the decision of the Cabinet of Ministries No 336 of 7 October 2016:<br>“61(1). Chamber of Commerce and Industries of the Republic of Uzbekistan”<br>Link:http://www.lex.uz/docs/4434432|
|---|---|---|
|August 16,<br>2019<br>Resolution<br>of<br>the Cabinet of<br>Ministries<br>No - 675|“On making<br>amendment to<br>the list of the<br>state authorities<br>and institutions<br>that are allowed<br>to apply for<br>permanent<br>registration of<br>their<br>employees”|Pursuant to Presidential Resolution of the Republic of Uzbekistan No– 4307 “On additional measures for increasing the efficiency of spiritual and<br>educational work” of 3 May 2019, the Cabinet of Ministries decides:<br>To make an addition by paragraph No 61(2) to the list of the of public authorities, state and economic management bodies, other public institutions<br>of republican significance, whose senior officials may apply for provision of permanent registration for their employees in Tashkent city and Tashkent<br>region approved under the decision of the Cabinet of Ministries No 336 of 7 October 2016:<br>“61(2). Centre of Spirituality and Enlightenment”<br>Link:http://www.lex.uz/docs/4476612|
|July 2019<br>Public opinion<br>survey of<br>kun.uz related<br>to propiska<br>legislation||Kun.uz through their channel in telegram conducted a survey of around 4 000 respondents. Respondents were asked to provide their opinion if its<br>time to abolish the system of propiska in Tashkent city and region. 69 % of respondents are against propiska. Majority (31%) consider that propiska<br>hinders the development of the country. For instance it is very hard to open business in Tashkent if a person does not have Tashkent propiska. 26%<br>of respondents indicated that propiska is violation of human rights and freedom. 12 % of respondents are convinced that propiska became a business<br>as a citizen of Uzbekistan that does not have Tashkent propiska by simply purchasing a house/apartment in new building in Tashkent.<br>Link:https://repost.uz/che-tam-po-hatam|

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**Figure 3. Gini Coefficient for selected Middle East and North African countries (circa 2012)**

<!-- Start of picture text --> 50 44.1<br>45 40.7<br>37.4<br>40 35.8 34.5<br>35 29.7<br>27.6<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Djibouti Iran (2013) Morocco Tunisia West Bank Algeria Egypt (2012)<br>(2013) (2006) (2010) and Gaza (2011)<br>(2009)<br><!-- End of picture text --> Source: All countries but Egypt: World Bank Global Monitoring Database (2017); Egypt own calculations using HIECS 2012/13.

**Table 7** summarizes the implications of the adjustment for Old‐law tenants in the measurement of a series of inequality measures for Egypt.  There is a small decline in inequality after the adjustment as measured by the Gini coefficient and other whole‐distribution indexes. In contrast, the ratio of the 90<sup>th</sup> to 10<sup>th</sup> percentile indicates an increase in inequality after the adjustment. Moreover, the increase seems to be concentrated in the top half with the ratio of the 90<sup>th</sup> to 50<sup>th</sup> percentile increasing, while the ratio 10<sup>th</sup> to 50<sup>th</sup> remaining basically constant. These results may hint at some compositional changes in inequality going in different directions. **Table 8** explores this possibility and presents the estimates for the Atkinson (1) inequality measure by regions. For all the urban regions, we observe a decline in inequality when the welfare aggregate is adjusted for imputed rent. The decline is slightly more pronounced for metropolitan area as compared to other regions, where the estimate drops by 0.017. In fact, it appears that due to the adjustment for rents, within‐group (region) inequality drops from 0.114 to 0.108, while inequality across regions is now higher (from 0.024 to 0.027). The combination of these two forces cancel each other and the changes in overall inequality are small as we saw in **Table 7** . These results are in line with the estimated changes in poverty above: households living in urban areas are now richer on average as a significant share of them are now reflecting higher housing services, whereas households in rural areas remain at (relatively) the same levels of deprivation.

**Table 7. Estimates of inequality measures for Egypt 2012/13, with and without adjustment for the regulatory framework**

||No adjustment|With adjustment|
|---|---|---|
|Gini|29.7|29.5|
|GE(0)|.1464|.1427|
|A(1)|.1362|.133|
|A(2)|.2276|.2247|
|P90/p10|3.251|3.262|
|P90/p50|1.953|1.957|
|P10/p50|.601|.6|

Source: Own calculations using HIECS 2012/13.

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Table 5 Corruption Trends for ECA in Non-BEEPS Sources

|Source|N|Scale|Mean|value|Mean|rank|
|---|---|---|---|---|---|---|
||ECA,all||2002|2005|2002|2005|
|CPIAQ16|27,134|1 –6|3.11|3.30|64|61|
|Nationsin Transit*|27,27|1 –7|4.85|4.80|--|--|
|ICRG|21,140|0–6|2.07|2.19|82|76|
|WEF|14, 79|1 – 7|||||
|Favoritism indecisions|||2.91|2.79|52|53|
|Diversionofpublicfunds|||3.29|3.33|48|48|
|Business costs ofcorruption|||4.51|4.07|45|51|
|Financial honesty ofpoliticians|||2.11|2.17|50|50|
|Irregularpaymentsin...|||||||
|...exports &imports|||4.44|4.77|49|45|
|...public utilities|||5.06|5.38|46|42|
|...taxcollection|||4.89|5.29|44|41|
|...public contracts|||3.78|3.90|49|48|
|...judicial decisions|||4.80|4.42|51|49|
|TIcorruptionperceptions|28,152|0-10|3.25|3.29|92|88|
|WBIcontrolofcorruption|28,184|0+/-sd|-0.41|-0.37|111|106|

*NIT is the only source for which larger values indicate more corruption.  Median value for NIT was 5.25 in 2002, improving to 5 in 2005.  Average change in CPIA Q16, and in WBI control of corruption, are for 2002-2004.

Table 6 Changes in CPIA Q16, 2002-2004, by region

||Change2002-4|2004|2002|
|---|---|---|---|
|EAP(18)|+0.32|3.12|2.79|
|ECA(27)|+0.19|3.30|3.11|
|AFR(45)|+0.12|2.95|2.83|
|LAC (28)|+0.09|3.54|3.45|
|SAR(7)|+0.00|3.21|3.21|
|MNA(9)|-0.06|2.89|2.95|
|All(134)|+0.14|3.18|3.04|

Table 7: correlations in changes from 2002 to 2005

||NIT|ICRG|CPIA|
|---|---|---|---|
|ICRG|-.34|--|--|
|CPIA|-.34|_-.17_|--|
|EIU|+.31|-.02|_+.02_|

Higher values indicate more corruption in the NIT and EIU scales, but less corruption on ICRG and CPIA. Correlations in the “wrong” direction are shown in italics.

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**Appendix Table A1: Statistics from the Assistance Intervention**

|||Fi|rst Session||||Se|cond Ses|sion||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(1)|(2)|(3)|(4)|(5)|
||Record<br>Keeping|Profit<br>Calculation|Stocking<br>up|Attracting<br>Customers|Joint<br>Decisions|Record<br>Keeping|Profit<br>Calculation|Stocking<br>up|Attracting<br>Customers|Joint<br>Decisions|
|Interest in Implementing Practices (1=Most Interest - 5=Least interest)|2.992|2.995|2.605|2.551|3.731|---|---|---|---|---|
||(1.525)|(1.330)|(1.262)|(1.355)|(1.313)|---|---|---|---|---|
|Started Reading Chapter (Yes/No)|0.475|0.419|0.410|0.412|0.388|0.602|0.569|0.554|0.542|0.535|
||(0.500)|(0.494)|(0.492)|(0.493)|(0.488)|(0.490)|(0.496)|(0.498)|(0.499)|(0.499)|
|Any Practice Implemented (Yes/No)|0.473|0.373|0.387|0.533|0.515|0.635|0.540|0.581|0.694|0.727|
||(0.500)|(0.484)|(0.487)|(0.499)|(0.500)|(0.482)|(0.499)|(0.494)|(0.461)|(0.446)|
|Any Problems Encountered (Yes/No)|0.425|0.288|0.319|0.329|0.294|0.471|0.358|0.356|0.352|0.360|
||(0.495)|(0.453)|(0.467)|(0.470)|(0.456)|(0.500)|(0.480)|(0.479)|(0.478)|(0.480)|

**Notes:** This table presents data collected by the facilitators during the first and second session of the assistance visits. The variables include the interest shown by the retailer in the practices described in each chapter of the handbook, whether they started engaging with the material of the chapters and implementing its practices, as well as whether they encountered problems during the assistance. Data were collected by the facilitators during the first and second session of the assistance visits.
<!-- Start of picture text --> Model 1<br>Model 2<br>1.0<br>0.8<br>0.6<br>PIP<br>0.4<br>0.2<br>0.0<br>GDP60 SubSahara Confucian Mining LifeExp EquipInv Buddha BlMktPm RFEXDist RuleofLaw LatAmerica stdBMP Privatecredit Muslim Spanish English LabForce NequipInv Jewish Abslat French Catholic Protestants OutwarOr YrsOpen Brit PublEdupct Foreign PrScEnroll Hindu WorkPop EcoOrg Popg EthnoL Age RevnCoup PrExports WarDummy Area CivlLib PolRights HighEnroll<br><!-- End of picture text --> A2. JOINTNESS OF FINANCIAL INDICATORS To check the dependence between our financial variables, we compute the so–called jointness measure, which is based on the posterior distributions of explanatory variables over the model space. The goal of this exercise is to determine whether the different financial variables capture different sources of information in explaining the dependent variable (jointness) or if they represent similar factors and should not be considered jointly in the model (disjointness) (Ley and Steel 2007).

Jointness statistics for our financial indicators are available in tables A1–A3 with each table representing a different approach to jointness computation. tables A1 and A2 show that none of the numbers exceeds the threshold suggested by Ley and Steel (2007) (LS) for decisive (dis)jointness. Nevertheless, jointness statistics for some of the pairs suggest very strong jointness (e.g., market capitalization and private credit). Another way of constructing jointness statistic has been developed by Doppelhofer and Weeks (2009) (DW). Regarding DW statistic, we find strong substitutability for private credit and net interest margin. However, as has been 55
**<u>Table 5: Expenditure Elasticities</u>**

||**All**|**R**|**ural**|**Urb**|**an**|
|---|---|---|---|---|---|
|||Poor|Non-Poor|Poor|Non-Poor|
|Cereal|-1.563***|2.907***|-0.076**|1.405***|-0.928***|
||[0.039]|[0.082]|[0.032]|[0.010]|[0.060]|
|meat/Fish|0.996***|3.292***|1.249***|0.466***|0.573***|
||[0.000]|[0.070]|[0.005]|[0.016]|[0.008]|
|Oil|0.675***|0.636***|0.825***|1.327***|4.566***|
||[0.003]|[0.007]|[0.003]|[0.005]|[0.066]|
|Tubers/Plantain|-0.269***|-1.103***|-0.398***|-0.462***|-1.386***|
||[0.013]|[0.052]|[0.030]|[0.010]|[0.039]|
|Other Foods|9.753***|2.870***|8.923***|10.154***|7.429***|
||[0.111]|[0.043]|[0.168]|[0.223]|[0.231]|
|Observations|21,239|6,766|4,785|5,743|3,945|

Standard errors in brackets, 100 bootstrap replications *** p<0.01, ** p<0.05, * p<0.1 Source: 2012 1-2-3 Survey, DRC Table 5 shows expenditure elasticities for the aggregate commodities. Expenditure elasticities are computed in a two-step approach. First, they are computed for each household; then sample mean and standard error are calculated using bootstrap. Elasticities are statistically different from zero for all commodities in each area, showing that a linear demand system would provide inadequate estimates of welfare losses.

From these results, aggregate commodities display different expenditure elasticities depending on the analysis group. Among the non-poor (in rural and urban areas), cereals are inferior goods. Animal proteins (meats and fish) are normal goods. They are luxury goods in rural areas, whereas they are necessity goods in urban areas for both the poor and the non-poor. For all the analysis groups, tubers and plantains are inferior goods. An important fact from this table is that a price change is bound to have an income effect for most commodities; the size of the demand response to the price change will then depend on both income and substitution effects; the latter is captured by compensated elasticities.

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tion generates greater effects for the less violent children.

These results are consistent with a body of microlevel evidence, such as papers on random assignment of freshmen or students (Thiemann, 2013); on elite exam schools (Abdulkadiro ̆glu et al., 2014; Dobbie and Fryer Jr, 2014; Lucas and Mbiti, 2014); and programs for gifted individuals (Bui et al., 2014). Additional evidence on academic and labor contexts is presented by Hoxby (2000); Zimmerman (2003); Angrist and Lang (2004); Rao (2019); Griffith and Rask (2014); Lafortune et al. (2016); Chetty et al. (2016); Oreopoulos et al. (2017). Overall, these papers find positive impacts of exposure to a very different set of peers. They argue that the integration effects occur due to the interaction between different individuals within groups, supporting the rainbow model of peer effects (Hoxby and Weingarth, 2005).

As I briefly noted before, my results relate mostly to those of Rao (2019), who provides the first evidence of how changes in peer composition at school can shape a student’s social preferences through an improvement in her generosity, prosocial behavior, and equity. My paper contributes to these results by providing additional experimental evidence that is especially relevant for the developing world. I test how exposure to diversity regarding violence positively impacts additional noncognitive outcomes such as violence, approval of peers’ antisocial behavior, misbehavior, and attitudes toward school and learning. An additional outstanding characteristic in Rao (2019) is that the author uses well-constructed measures of social preferences. In my study, I collected measures of noncognitive outcomes from students’ self-reports and administrative data provided by schools. These two sources of information enable me to contrast and validate the results.

Additional evidence from my experimental design regards the tracking effects for marginal participants. For example, an individual at the median in the violence distribution who is assigned to a high-violence group can be “contaminated” by her peers and increase her violence level. Or, 31
What happens over time? While the (explicitly) relative poverty lines used by _Eurostat_ are automatically adjusted over time in line with the changes in the median, the process of adjustment is slower for absolute lines. It appears to be the case that a (positive) minimum aggregate income gain to a low-income country is needed before upward pressure on the poverty line emerges—that is implied by the non-linearity evident in Figure 2(b). And there can be strong political resistance to revising the line. The official poverty line in the US—at $13 per person per day for a family of four—has not been updated in real terms for many decades, despite some public dissatisfaction with the old line.<sup>19</sup> Similarly, the poverty line in China had not been revised upwards in real terms for over 20 years, despite a four-fold increase in mean income; this has led some observers to question the relevance of the old poverty line to current conditions.<sup>20</sup> Upward revisions to the real values of national poverty lines in growing developing countries cannot be expected to happen quickly, and nor would downward revisions at times of economic contraction be likely. But an upward adjustment to a new (sustained) equilibrium level of average consumption can be expected. The governments of China and India are in the process of revising upwards their official poverty lines, after many decades.<sup>21</sup>

# **4. Relative poverty lines**

Psychologists, sociologists and others have argued that the circumstances of the individual relative to others influence welfare at any given level of individual command over commodities.<sup>22</sup> Survey questions on how respondents think about poverty also suggest that there is a degree of relativism even in low and middle income countries.<sup>23</sup> However, while there is probably wide agreement that relative position matters to welfare (and hence welfare-based poverty measures), the challenge has been how to put this into practice in a credible way.

> 19 See the interesting discussion in Blank (2008).

> 20 See, for example, Osberg and Xu (2008)

> 21 Although in India’s case it is not clear how much this is an upward revision to reflect changing perceptions of poverty versus dissatisfaction with past price indices; see Planning Commission (2009).

> 22 Runciman (1966) provided an influential exposition, and supportive evidence. Also see the discussions in Easterlin (1974), Scitovsky (1978), Clark and Oswald (1996), Oswald (1997), Solnick and Hemenway (1998), Walker and Smith (2001), Ferrer-i-Carbonell (2005) and Ravallion and Lokshin (2010) amongst others.

> 23 See Corazzini et al. (2010) who asked about 2,000 university students in eight countries about whether they saw poverty as absolute or relative, or a mixture.

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10. Summary of Causal Inferences for Nineteen Countries-SSA (1971-90) _55_

# Annex Tables

Al. Evidence on African Smallholder Supply Elasticities _73_ A2. Ten SSA Countries: Cash-Crop Supply Elasticities 75 A3. Estimates of -Aggregate Supply Response to Agriculture in SSA 78 A4. Export Concentration and Export Share of Manufactures, and Fruits and - Vegetables in SSA, 1987-88 79 _AS._ Major Depreciations in SSA Countries, 1980-92 80 A6. Real Effective Exchange-Rate in SSA _83_ A7. Per Capita Real Growth Rates _84_ A8. Average Annual Inflation Rates _85_ Figure 1 The Parallel Premium and Auction Exchange Rate Relationship _15_
market infrastructure. So there is an important interaction between price transmission and the distribution of gains from global trade reforms.

One of the poorest countries in the world, which also has very poor infrastructure and is plagued by high domestic marketing costs, is Mozambique. In fact, recent work by Arndt et al. (2000) estimates producer-consumer margins as high as 300% (for cassava). The biggest margins reported in their study are for food products, which tend to dominate both the consumption and production bundles of the poor. So the existence and behavior of these margins is critically important for any poverty study. Channing Arndt (2005) explores this issue in the context of the Doha Round scenarios for Mozambique. As with the Mexico study, the combination of these marketing margins with modest world price changes means that the impact on household welfare in Mozambique is quite small. Indeed, about one-third of rural households are unaffected by the Doha scenario. The largest rural losses are about one percent of income, with some households experiencing modest gains. The dispersion among urban households is larger, due to the presence of smaller marketing margins. Overall the impact of multilateral trade reform on Mozambique is adverse, as preferences are eroded and prices of imports rise.

# **4. The Disaggregated Impact on Households**

Moving beyond the question of price transmission, we come to the issue of household level impacts of – and household responses to – the price changes ensuing from trade reforms. The simplest way of exploring this link is to focus on a single commodity. This is the approach taken by Jorge Balat and Guido Porto (2005) on the impact of trade reform on cotton producers in Zambia. They note that the critical factor in 15