rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 1**|**Summa**|**ry of key var**|**iables**||||
|---|---|---|---|---|---|---|
||**UP**<br>Mean|Std.Dev.|**MP**<br>Mean|Std.Dev.|**KA**<br>Mean|Std.Dev.|
|**Mean Student variables**|||||||
|% Correct score<br>Grade4- Math(class 5for KA)|.23|.29|.29|.29|.69|.28|
|Grade4- language (class 5for KA)|.27|.35|.33|.34|.75|.29|
|Grade 3- Math(grade4 for KA)|.17|.28|.26|.30|.67|.31|
|Grade 3- language (grade4 for KA)|.21|.32|.30|.35|.68|.31|
|Grade2- Math|.13|.26|.25|.31|-||
|Grade2- language|.20|.29|.31|.33|-||
|Age|8.72|1.61|8.95|1.57|10.08|.82|
|Gender(1 if male)|.49|.50|.51|.50|.52|.50|
|General caste (neither SC/ST, nor<br>OBC)|.15|.36|.20|.40|.04|.19|
|OBC|.40|.49|.31|.46|.51|.5|
|SC|.44|.50|.15|.36|.33|.47|
|ST|-|-|.34|.47|.13|.34|
|Mother literate|.21|.40|.13|.33|.37|.48|
|Father literate|.60|.49|.46|.50|.56|.50|
|Land owned (inacres)|1.13|1.65|2.50|4.27|3.33|4.54|
|**Mean School level characteristics**|||||||
|Enrollment|178|89|119|66|137|129|
|Pupil-teacher ratio|66|39|56|30|27|17|
|% schools withtoilet|.33|.47|.38|.49|.72|.45|
|% schools withdrinking water|.83|.38|.72|.45|.66|.48|
|% schools withplayground|.79|.41|.50|.50|.46|.50|
|% schools withelectricity|.01|.07|.07|.25|.59|.49|
|Numberofblackboards|3.75|1.77|3.32|1.84|5.84|3.81|
|**Mean Teacher level characteristics**|||||||
|Attendance (meanover 4visits)|.64|.48|.67|.47|.88|.32|
|Activity (meanover 4visits)|.25|.43|.30|.46|.68|.47|
|% ofcontract teachers|.41|.20|.15|.36|0|0|
|% of formercontract|-|-|.45|.50|-|-|
|Age (years)|38|14|39|9|39|8|
|Non-SC/ST(generalcaste+OBC)|.80|.40|.69|.46|.76|.43|
|Male|.60|.49|.80|.40|.59|.49|
|% withclass12degree|.42|.49|.48|.50|.72|.45|
|% withcollege degree|.32|.47|.30|.46|.26|.44|
|% withgraduate degree|.26|.44|.23|.42|.03|.16|
|% withpreservice training|.59|.49|.36|.48|.93|.26|
|Distance to work(km)|6|10|9|12|10|11|
|Teaching experience (years)|10.9|13|13.8|10|12|8|
|% doingmultigrade|.81|.39|.87|.34|.91|.29|
|Days of in service training|5.83|8|10.95|12|8.57|8|

**Definition of variables:** Teacher attendance= 1 if teacher present, 0 otherwise (and averaged over the four visits), teacher activity=1 if teacher actively engaged in teaching i.e., if teacher is teaching, writing on the board, supervising written work, teaching by rote, 0 if teacher is absent, chatting with others, sitting idle/standing outside classroom, keeping order in school/class but not teaching, doing some other work unrelated to teaching (and averaged over the four visits)

Index of infrastructure in school= sum of four indicator variables for whether school has water, toilet, playground, electricity and the total number of blackboards in school.

21
using the following form of the government's temporal budget constraint in (3): to get: The transversality condition (22) implies that the limit term on the right-hand side of (39) is zero. Provided that the first difference terms on the left-hand side are stationary, therefore, r,Bt, - SURP, must also be stationary for the transversality condition (22) to hold.

Decomposing SITRP, into T, -G, yields the implication that a necessary condition for the transversality condition (22) to hold is the presence of cointegration among G,, r,-,B, , and T, with cointegrating vector (1,1,-1). Equivalently, AB, = G, +r,-1 B11 -T, is stationary.

Ahmed and Rogers also show that this cointegrating relationship is also a _sufficient_ condition for the transversality condition (22) to hold provided the debt process falls within the following class of time series processes: Ahmed and Rogers apply their cointegration test to U.S. (U.K.) fiscal policy over the very long sample period 1792 (1692) to 1992. They are unable to reject the hypothesis that G,, r, 1 B,l , and T, are cointegrated with vector (1, 1,-I). They conclude, therefore, that the PVC holds for the long period. Their tests allows for changes in the dynamic relations among variables during major wars, where _a priori_ one might anticipate structural breaks to occur.

As discussed above, under certain assumptions the transversality condition (22) can be transformed into (25) where the discount factor becomes the risk-free interest rate. Following 29
29 There also appears to be some support for the idea that imperfect indexation (with the real wages of the poor being eroded more rapidly than the incomes of the better-off) is an important part of the inflation story. Whereas the partial correlation of real wages with inequality (given inflation) remains significant, the partial correlation of inequality and inflation (given real wages) is positive, but only significant at 10%. Both corresponding partial correlations remain strong and significant for poverty.

While no inference of causality is made from the correlations described in Table 7, the patterns in the data are consistent with a strong association between rising inflation and rising inequality during the 1980s in Brazil. The patterns are also consistent with the suggestion that this association is mediated by changes in the distribution of real wages, possibly because indexation is imperfect in ways that are not distribution-neutral (Cardoso, 1992; Neri, 1995). The relationship weakens after stabilization in 1994, but it may help account for the residual increments in inequality which were not associated with shifts in the distribution of schooling, or with increasing labor market returns to experience, during the 1980s. The time-series for inflation and inequality are plotted in Figure 3, and those for the real wage index and poverty are plotted in Figure 4.

<!-- Start of picture text --> Figure 3: Inflation and Inequality in Brazil, 1981-2004<br>10 0.90<br>9<br>0.85<br>8<br>7 0.80<br>6<br>0.75<br>5<br>0.70<br>4<br>3 0.65<br>2<br>0.60<br>1<br>0 0.55<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Log Inflation Inequality (Theil)<br><!-- End of picture text -->
results from OLS and IV specifications for different levels of stock liquidity. While the OLS results suggest that there is no evident change in comovement across stocks following the trades by pension funds, the IV specification implies the opposite. As discussed earlier, the predictions of the OLS model may be explained by the fact that some of the trades by these institutional investors might result from stock-specific information, which is expected to reduce the correlation between the stock returns and the returns of other stocks in the peer portfolio. The IV results show that peer benchmarking generates excess in comovement between stocks returns and the peer portfolio, an effect that last for at least six months. For the average stock, peer benchmarking increases the correlation of daily stock returns by 0.37 during six months. This is a sizable increase in comovement, since average stock return correlation over the sample period is 0.46. According to Figure 3, stocks with lower liquidity display larger increases in comovement with the peer portfolio.

We also use an alternative measure of stock return variation that is firm-specific, commonly known as price non-synchronicity. This measure is defined as _ln_ ((1 _− Rs,t_<sup>2</sup> + _n_<sup>)</sup><sup>_/R_</sup> _s,t_<sup>2</sup> + _n_<sup>), where</sup><sup>_R_</sup> _s,t_<sup>2</sup> + _n_ is the _R_<sup>2</sup> from the regression of firm i’s daily returns and the peer portfolio returns during n-month rolling windows following _t_ . This measure is typically used to capture firm-specific information in stock prices (Roll, 1988).<sup>19</sup> Following the empirical strategy above, we test whether peer benchmarking impacts price non-synchronicity over one, two, three and six months following the trades of pension funds. Results are documented in Table 5. Consistent with the observed excess in comovement above, stocks with large flows from pension funds due to peer benchmarking tend to exhibit lower idiosyncratic volatility, or a higher fraction of total volatility that is common to all stock in the peer group.

# **5 Conclusions**

Effective management of retirement savings is fast becoming an important agenda in many countries due to a rapidly ageing population. In addition to fulfilling this critical function, pension

> 19To calculate _R_ 2, stock returns are typically regressed on the returns of a market index. In our case, the correlation between the peer portfolio and the Colombian market index (IGBC) is 82%. In unreported results we calculate price non-synchronicity using the IGBC for which results are quantitatively similar to the ones presented here. In both cases, the peer portfolio and market portfolio exclude the firm in question. This exclusion prevents spurious correlations between firm and both index and peer portfolio returns.

16
25 comprehensive specification reported in column (6) of table (2) is acceptable at conventional levels (pvalue≈ 0.02), it might signal that the instrument is not sufficiently robust: the associated F-statistic is about 5.5, below both the conventionally suggested benchmark value of 10 to claim robustness of the IV identification (Staiger and Stock (1997)) and the more appropriate critical values tabulated in Stock and Yogo (2005). This implies that standard tests have incorrect size, leading to wrong inference on the magnitude and significance of the structural parameters of interest. Recently, several tests have been proposed that have correct rejection regions even under potentially weak instruments<sup>24</sup> . In particular, a convenient test for a just-identified model with potentially weak instruments as ours is the Anderson-Rubin (AR) test (Anderson and Rubin (1949), Moreira (2001)). For this reason, in table (3) we report both the robust standard errors clustered at town level associated with the relevant estimate (LS and 2SLS) and, for the 2SLS, the p-values associated with the weak-instrument robust AR test.

In column (1) we focus on youths born between 1956 and 1965 in treated and control towns and only include town and cohort fixed effects and a quadratic in cohort size. Least squares estimates show that a 1 point increase in the percentage of high school graduates is associated with a statistically significant decline in 10-year adult mortality rates of about 0.015 percentage points. The corresponding 2SLS estimate suggests a one order of magnitude larger decline which turns out to be statistically significant at conventional levels only for deaths by natural causes. Similar results are obtained in columns (2) and (3) in which we restrict attention to younger cohorts born in 1959-65 and include controls for parental background and, in column (3) allow for area-specific cohort effects. In both cases, a 1 point increase in the proportion of high school graduates significantly lowers mortality rates by about 0.2 percentage points both for all deaths and for natural ones only. In column (4) the sample is further restricted to towns with the least recorded damages still yielding a significant and similar effect.

> 24For a survey of the tests that are valid under weak instruments see Stock, Wright and Yogo (2002).
- 22 -

Phase 1: Taking stock. The first task is to take stock of the country's existing system and identify the main problems (see appendix 3). Table 2 contains a sample checklist of characteristics that _vwill_ affect the payment system's design. Many activities in Phase 1 might take no less than four to six months to complete. Two to three months after the project begins it might be feasible to identify the areas needing immediate improvement and the remedial actions required.-'L The most important work in Phase I is to make short-term improvements while ensuring consistency with long-term improvements.

Table 2 SITUATlON ANALYSIS (STOCK TAKING PHASE: A CHECKLISI) <!-- Start of picture text --> County PAhysical<br>Chawckrsc, InJisrucure Thc Financial Systm The Current Payment System Inentve<br>Size PHYSICAL STRUCrURE INSIRUMEN7S CO-<br>Transpon Sysrem Central Bank Debit Relative Prices<br>Telecommunicaton FaciLities Commercial Banks Credit - 7abor/Capital)<br>mail System BI7S<br>Srork Exchange<br>Population LEGAL LAYOUT PROCESSING COMPEDTJON<br>Lawsltegulations Branches Ratch atwense<br>ContracrsiCodes Concentration On-Line Monopolistic<br>Juicial System Size Speed<br>Enforcement Functions Trraffc<br>Supervision<br>Terain INFORAIA1TON ACCOUArS CLEA4RNG BUSINESS NEEDS<br>Accosuwng Systens wiCunomersr : Bilateral flmeiness<br>Reliability Centralized Clearing House Interest Rates<br>limeliness Decentralized Inflation<br>wicCentral Bank<br>Reserve<br>leari-ng<br>Correspondent<br>INSI7T7TONAL 7ECHNOLOGY SEULAEMENT<br>Tradition Automation Immediate<br>Strengths Centradized Provisional<br>Organization Decentralized Gross/Rn<br>Efficiency IntrabanikPaynet Central Bank<br>Cooperation Settlenent Bank<br>Conflict Float (DiC)<br>Overdrafts<br>HUMAN DIVERSinFCA7ION PAYMEWN MEDIA<br>Education Money Market Paper<br>Skills Securities Market Electrnic<br>Know-How<br><!-- End of picture text --> _A useful technique to enhance the client's ownership of the project and to raise conscious amnong sensor staff is to hold an introductory_ **_paymt_** _systems workshop or a full seminuar to discus the preminry conclions of Phase I with the central bank_ **_and_** _the NPC._
# Xncome Distribution

# and Poverty

In this section we assume away the pure externalities referred to above and consider only the problem of "pecuniary" externalities, i.e., the negative effect of rapid population growth on income equality and thus on the poor. Rapid population growth can worsen the distribution of income in developing countries and it probably exacerbates the problems of the poor. At the aggregate level, rapid growth of population increases che availability of labor in an economy relative to land and physical capital, reducing wages. 18/ This is likely to worsen inequality and hurt the poor, who are more reliant on labor income. Moreover, there is evidence that unskilled, but not skilled laborers, suffer a relative decline in wages if they are members of a large cohort. 19/ At the family level, poverty and high fertility seem to form a vicious circle. As we point out in an earlier paper 20/, high fertility contributes to poverty. It strains the budgets of poor families, reducing available resources to feed, educate and provide health care to children. With households classified by per capita income in Brazil, Colombia, Malaysia and rural India (data from the 1970s), the ratio of income per child in the richest households to that in the poorest ranges from about 12 in ruirc India to more than loo in Brazil. L/ A study of families with twins in India found that the additional unexpected child

- 18/ Note that although wages are depressed in tais scenario, rents and profits are boosted. High fertility is therefore potentially Pareto superior to low fertility, assuming that there were a mechanism for redistribution of some of the rents of landlords and capitalists (Ng, 1986; Willis, 1987)..

- 19/ Behrman and Birdsall, 1988, show this for males in Brazil. 20/ Birdsall and Griffin, 1988.

- 21L/ Birdsall, 1988; or Birdsall and Griffin, 1988.
13 The rationale for including these variables in the first-stage choice equation follows the standard literature on migration and remittances. According to the basic human capital model, human capital variables are likely to affect migration because more educated people enjoy greater employment and expected income-earning possibilities in destination areas (Schultz, 1982; Todaro, 1970).<sup>15</sup> In the literature household characteristics – such as age of household head and number of male members and children – are also hypothesized to affect the probability of migration and the receipt of remittances. In particular, some analysts (Adams, 1993; Lipton, 1980) have suggested that migration is a life-cycle event in which households with older heads, more males over age 15 and fewer children under age 5 are more likely to participate. With respect to migration networks, the sociological literature has stressed the importance of family and village networks in encouraging migration (Massey, Goldring and Durand, 1994; Massey, 1987). For this reason, the firststage equation includes three variables measuring migration networks at the level of the ethno-religious group: the number of female-headed households receiving internal remittances (from Ghana) in 1998/99; the number of female-headed households receiving international remittances (from African or other countries) in 1998/99); and the square of the number of internal migrants in 1998/99. Each of these network variables is measured at the level of the ethno-religious group for the year of the last Ghana GLSS Survey (GLSS 4 in 1998/99). The first-stage equation also includes the two instrumental variables discussed above, as well as the control variable, square of income in the ethno-religious group of the household. The reason for including this control variable is that social networks influence the choices of households both directly and indirectly. For example, while a household can benefit directly from a social network if it has a household member
|**Table A20: iJET Country Se**<br>**Data Provider**<br>iJET|**curity**|**Risk**|**Ratin**|**gs (IJ**|**T)**|||||
|---|---|---|---|---|---|---|---|---|---|
|**Description**<br>Commercial security risk consultin<br>**Website**<br>www.ijet.com<br> <br>|g comp|any ba|sed in|Annapo|lis, Un|ited St|ates|||
|**Data Source**<br>Country Security Risk Ratings<br> <br>||||||||||
|**Type**<br>Expert assessment||||||||||
|**Respondents**<br>iJET staff||||||||||
|**Frequency**<br>Annual since 2004<br> <br>||||||||||
|**Coverage**<br>Global sample of countries||||||||||
|**Public Access**<br>Commercially available||||||||||
|**Description**<br>iJET provides assessments of securi|ty risk|s faced|by trav|elers,|coded|on a 5-|point sc|ale.||
|2008<br>**Voice and Accountability**|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|NA<br>..|..|..|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**||||||||||
|Security Risk Rating<br>X|X|X|X|X|..|..|..|..|..|
|**Government Effectiveness**||||||||||
|NA<br>..|..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**||||||||||
|NA<br>..|..|..|..|..|..|..|..|..|..|
|**Rule of Law**||||||||||
|NA<br>..|..|..|..|..|..|..|..|..|..|
|**Control of Corruption**||||||||||
|NA<br>..|..|..|..|..|..|..|..|..|..|
|**Country Coverage**<br>**185**|**184**|**182**|**177**|**167**|**..**|**..**|**..**|**..**|**..**|
|**Year of publication**<br>**2008**|**2007**|**2006**|**2005**|**2004**|**..**|**..**|**..**|**..**|**..**|

59
# **Table A23:  Cerberus Corporate Intelligence Gray Area Dynamics (MIG, GAD)**

|**Data Provider**|Cerberus Corporate Intelligence|
|---|---|
|**Description**<br>**Website**|Commercial risk rating agency headquartered in London, United Kingdom<br>www.merchantinternational.com|
|**Data Source**|Gray Area Dynamics|
|**Type**|Expert assessment|
|**Respondents**|Cerberus Corporate Intelligence staff|
|**Frequency**|Quarterly since 2002. MIG has been discontinued in 2007 and replaced with GAD, due to<br>takeover of former Merchant International Group. GAD questions are not comparable with<br>those rated earlier by MIG, and so we treat GAD as a new source starting in 2008.|
|**Coverage**|Global sample of countries|
|**Public Access**|Full dataset is commercially available. Averages of sub-indicators are publicly available in<br>this spreadsheet.|
|**Description**|Provides assessments of risks to foreign investor posed by 10 risk factors assessed on a 10-<br>point scale.|

|**Voice and Accountability**<br>Military Involvement in Politics<br>|2008<br>X|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|---|
|Inclusiveness / Patronage|X||||||||||
|Religious Freedom|X||||||||||
|Opposition|X|..|..|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**<br>|||||||||||
|Extremism (MIG)|..|X|X|X|X|X|X|..|..|..|
|Autonomy and Separatism|X||||||||||
|Civil Unrest|X||||||||||
|State of Emergency / Martial Law|X||||||||||
|Active Terrorist Groups in the Last Two Years|X||||||||||
|**Government Effectiveness**|||||||||||
|Bureaucracy (MIG)|..|X|X|X|X|X|X|..|..|..|
|**Regulatory Quality**|||||||||||
|Unfair Trade (MIG)<br>|..|X|X|X|X|X|X|..|..|..|
|Unfair Competition (MIG)|..|X|X|X|X|X|X|..|..|..|
|Stock Exchange / Capital Markets|X||||||||||
|Foreign Investment|X||||||||||
|**Rule of Law**|||||||||||
|Legal Safeguards (MIG)<br>|..|X|X|X|X|X|X|..|..|..|
|Organized Crime (MIG)|..|X|X|X|X|X|X|..|..|..|
|Nationalisation / Expropriation|X||||||||||
|**Control of Corruption**|||||||||||
|Corruption (MIG)|..|X|X|X|X|X|X|..|..|..|
|Cronyism|X||||||||||
|Government Efforts to Tackle Corruption|X||||||||||
|_Country coverage (GAD)_|**164**|**..**|**..**|**..**|**..**|**..**|**..**|**..**|**..**|**..**|
|_Country coverage (MIG)_|**..**|**156**|**156**|**155**|**155**|**155**|**118**|**..**|**..**|**..**|
|_Year of Publication_|**2008**|**2007**|**2006**|**2005**|**2004**|**2003**|**2002**|**..**|**..**|**..**|

62
Measuring the affordability of housing using the second approach – based on home values rather than imputed rent – yields qualitatively similar results, though the income/consumption multiples are structurally higher in Uzbekistan than in most countries, even in rural areas. Few areas meet the standard criterion according to which housing is “affordable” with a multiple of 3 or less. By the home value measure, most rural areas fall into the category of moderately unaffordable (with a multiple of between 3.1 to 4.0), while all urban areas fall into the severely unaffordable classification (with multiples above 4.1). At the extreme, Tashkent city was measured at 14.8 times annual household consumption in 2018, and 10.8 times annual household income. Per the home value-based measure, the city of Tashkent is more unaffordable than many famously exclusive metro areas, such as San Francisco in the United States and Vancouver in Canada (Table 4).

**Table 4: Uzbekistan Cities Median Multiple Affordability Measures vs. International Cities**

|Affordability<br>Rank|Country|City|Median Multiple|
|---|---|---|---|
|1|China|Hong Kong|18.1|
|_*_|_Uzbekistan_|_Tashkent (Consumption)_|_14.8_|
|2|Australia|Sydney, NSW|12.2|
|*|_Kazakhstan_|_Astana City (Consumption)_|_11.8_|
|3|Canada|Vancouver, BC|11.8|
|_*_|_Kazakhstan_|_Almaty City (Consumption)_|_10.6_|
|_*_|_Uzbekistan_|_Tashkent (Income)_|_10.5_|
|4|N.Z.|Auckland|10.0|
|5|U.S.|San Jose, CA|9.6|
|6|Australia|Melbourne, VIC|9.5|
|7|U.S.|Honolulu, HI|9.4|
|8|U.S.|Los Angeles, CA|9.3|
|9|U.S.|San Francisco, CA|9.2|
|10|U.K.|Bournemouth & Dorset|8.9|

_Source: 13th Annual Demographia International Housing Affordability Survey: 2017; the 2015 Household Budget Survey of Kazakhstan, and the L2CU Baseline Survey, Author’s Calculations._ The housing market in Uzbekistan has several relatively distinct characteristics, many of which result from the legacy of Soviet policies. Most notable among these is home ownership which – at more than 95 percent – is very high. In the Soviet Union, central planning reduced the salience of spatial price differentials because official prices for labor, land, housing, and consumer goods did not reflect relative scarcity. For instance, official rents for apartments in Moscow were not significantly higher 28