rafmacalaba/datause-encoder-data huggingface dataset:
|**Fixed effects panel regressions of uncertainty on the lag of crisis in initial crises countries**<br>Step 1, Effect of crises in the initial crises countries, A, on the uncertainty in potentially affected countries, B.<br>Uncertainty in country B at t is the dependent variable. Countries A are excluded from the group of other countries, B.<br>(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>(7)<br>all crises,<br>all<br>countries<br>all crises,<br>emerging<br>markets<br>regional<br>crises, asia<br>regional<br>crises,<br>emerging<br>asia<br>regional<br>crises,<br>emerging<br>asia +<br>turkey<br>regional<br>crises,<br>eastern<br>europe<br>regional<br>crises, latin<br>america<br>-0.018*<br>-0.032*<br>-0.115***<br>(0.010)<br>(0.018)<br>(0.041)<br>0.003<br>0.027<br>0.013<br>(0.013)<br>(0.025)<br>(0.052)<br>0.029*<br>0.036<br>0.109*<br>(0.017)<br>(0.032)<br>(0.063)<br>0.045***<br>0.080***<br>0.145***<br>(0.010)<br>(0.018)<br>(0.031)<br>0.063***<br>0.083***<br>0.101***<br>0.106***<br>0.104***<br>(0.009)<br>(0.016)<br>(0.016)<br>(0.024)<br>(0.023)<br>-0.020**<br>-0.046***<br>-0.068***<br>-0.027<br>(0.009)<br>(0.017)<br>(0.026)<br>(0.027)<br>2.582***<br>2.987***<br>5.228***<br>4.660***<br>3.226**<br>3.255*<br>6.328***<br>(0.517)<br>(1.007)<br>(0.825)<br>(1.253)<br>(1.361)<br>(1.708)<br>(1.875)<br>0.159***<br>0.131***<br>0.234***<br>0.232***<br>0.234***<br>0.070*<br>0.163***<br>(0.017)<br>(0.024)<br>(0.028)<br>(0.035)<br>(0.035)<br>(0.036)<br>(0.054)<br>-0.121***<br>-0.124***<br>-0.134***<br>-0.133***<br>-0.132***<br>-0.100***<br>-0.108***<br>(0.002)<br>(0.003)<br>(0.004)<br>(0.006)<br>(0.006)<br>(0.008)<br>(0.009)<br>5.161***<br>5.454***<br>4.652***<br>5.167***<br>4.982***<br>3.900***<br>5.972***<br>(0.349)<br>(0.481)<br>(0.679)<br>(0.846)<br>(0.845)<br>(0.721)<br>(1.333)<br>-0.001<br>0.001<br>-0.002<br>-0.005<br>-0.005<br>-0.003<br>0.019*<br>(0.004)<br>(0.005)<br>(0.006)<br>(0.007)<br>(0.007)<br>(0.013)<br>(0.011)<br>-0.007**<br>-0.013**<br>-0.009<br>-0.008<br>-0.009<br>-0.006<br>-0.022*<br>(0.003)<br>(0.006)<br>(0.006)<br>(0.008)<br>(0.008)<br>(0.010)<br>(0.013)<br>-0.004<br>-0.002<br>-0.013**<br>-0.019***<br>-0.018**<br>-0.001<br>0.009<br>(0.003)<br>(0.005)<br>(0.005)<br>(0.007)<br>(0.007)<br>(0.010)<br>(0.008)<br>0.703***<br>0.938***<br>0.840***<br>1.032***<br>1.129***<br>0.667***<br>0.677***<br>(0.032)<br>(0.059)<br>(0.045)<br>(0.065)<br>(0.074)<br>(0.100)<br>(0.116)<br>Observations<br>2855<br>1391<br>1142<br>696<br>696<br>353<br>342<br>Number of country<br>32<br>17<br>11<br>7<br>7<br>6<br>4<br>R-squared<br>0.59<br>0.64<br>0.68<br>0.72<br>0.73<br>0.46<br>0.48<br>Standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%<br>lag of crisis in Argentina<br>lag of crisis in Brazil<br>lag of crisis in Mexico<br>lag of crisis in Russia<br>lag of crisis in Thailand<br>lag of crisis in Turkey<br>lag of 10 year US gov bond rate<br>lag of crisis in country B<br>lag of political risk index<br>Constant<br>lag of mean growth expectations<br>lag of stock market volatility<br>lag of economic risk index<br>lag of financial risk index|
|---|

Table 1: Step 1: Effect of crisis in _A_ on uncertainty in _B_ 19
outcomes. Their use has been debated,<sup>19</sup> but they remain the simplest measure to obtain with any accuracy from a large population, and therefore the most commonly used by both researchers and clinicians.

# **4. Trends in Child Malnutrition between 1992 and 1998: Gainers and Losers**

It is striking to see how widespread is the incidence of underweight amongst children in India. Even amongst boys (the sex in which parents invest the most) in the highest wealth tertile in 1998, over a third were underweight. This tertile has an average caloric consumption of around 2500 calories per day,<sup>20</sup> so child malnutrition cannot be attributed to shortage of food. High burdens of disease are the probable cause of these high proportions underweight.

Children’s nutritional status grows sharply worse amongst lower socio-economic groups (Table 1). Looking at differences _by mother’s education level_ , in 1998 the share of stunted boys amongst mothers with no education was 53 percent, compared with 29 percent among mothers with secondary or higher levels of education. The corresponding shares of underweight boys were 54 percent and 32 percent, respectively. The differences _by household wealth_ tertiles are equally sharp: for example in 1998, 35 percent of boys from the richest tertile were underweight, compared with 57 percent amongst the lowest tertile. Differences _between castes_ are less sharp, though the boys of upper castes show better outcomes than lower castes or tribes.

The higher socio-economic groups also made stronger gains in child nutrition status during 1992-98 than the lower groups. The gains are especially strong amongst the boys of mothers with secondary school education and above, compared with lower education groups. The same applies to the upper castes versus the lower castes and tribes. Household wealth groups show less differences in improvement during1992-98.

Girls’ position deteriorated relative to boys between 1992 and 1998: they had similar or marginally lower proportions stunted and underweight as boys in 1992, but by 1998 girls were nutritionally more disadvantaged than boys across all socio-economic groups. Girls from the highest socio-economic groups showed improvement in nutritional status between 1992 and 1998. Amongst most of the other socio-economic groups, girls showed much more modest declines in the prevalence of underweight. There was an _increase_ in the proportion underweight amongst girls in the poorest wealth tertile and the scheduled tribes, and in the prevalence of stunting amongst girls in all the lower socio-economic groups.

This suggests that levels of discrimination against girls may actually have _risen_ amongst the lower socio-economic status groups between 1992 and 1998: including uneducated mothers and poorer households. It has risen sharply amongst the tribal populations. This ⎯ could be because people are reducing family size in India which, combined with

> 19 See, for example, Kumar and Stewart.

> 20 World Bank 2005, citing data from the National Sample Survey Organisation, 2001.

7
# **3.3. Results**

Regression results are presented in Tables 1 and 2. Table 1 shows the results of the basic regression with no interaction terms (column 1) and the results of the regressions where openness is interacted with time-varying variables (columns 2-5). These variables represent areas where economic reform has been most active; they are human capital investment, financial depth, macroeconomic price instability, and public infrastructure, respectively. Table 2 shows the regression results where openness is interacted with timeinvariant variables. They represent institutional and regulatory areas where reform --often called of second generation-- has been most sluggish. They are indices of governance, labor market flexibility, firm-entry flexibility, and firm-exit flexibility. We treat them as constant per country because their underlying institutional characteristics vary little over time and, partly reflecting this, there is quite limited data on their time dimension.<sup>8</sup> The basic regression (Table 1, Col. 1) shows results consistent with the previous empirical literature. Initial GDP per capita carries a significantly negative coefficient, commonly interpreted as evidence of conditional convergence. The proxies of human capital investment, financial depth, and public infrastructure have positive and significant coefficients, denoting their beneficial impact on economic growth. Inflation, on the other hand, carries a negative coefficient, indicating the negative consequence of macroeconomic price instability. Trade openness is also a significant explanatory variable; as in other studies that rely on the cross-country variation of within-country changes, trade openness is found to have a positive impact on economic growth. Since in this basic specification only linear effects are allowed, the estimated openness impact on growth is an average effect; below we attempt to uncover what is behind this average. The period shifts indicate that international conditions for growth have deteriorated over time, resulting in considerably poorer conditions in the 1980s and 1990s than in the previous decades. Finally, both the Sargan and serial-correlation tests indicate that the null hypothesis of correct specification cannot be rejected, lending support to our estimation results. This is

> 8 The ICRG governance index is available since the mid 1980s and shows some time variation.  Given that we are forced to assume that its value was the same in the 1960s and 1970s as in the mid 1980s, we take the conservative assumption that its growth effect cannot be estimated separately from that of the unobserved fixed effect, as is the case with the other institutional variables that are completely constant over time.

22
administrative costs and profit margins. Allocated annuities receive the same tax and income test treatment as life annuities. Despite the higher risks retained by their holders, they are perceived by many investors as having considerable advantages over life annuities, mainly because of their greater flexibility.

The boost in term and lifetime business in 2004 appears related to a desire by retirees to seek a guaranteed income source following the poor investment performance of investment-linked products in the previous couple of years, and the impact of government policy which brought forward purchases. In February 2004 the government announced that from September 2004 monies invested in term and lifetime annuities would only be 50 percent exempt from the asset test for the age pension instead of 100 percent, making them considerably less attractive; this brought forward purchases of these products resulting in a spike in sales in 2004 and a sharp fall in subsequent years.

# **7 Trends in Product Composition**

## **_7.1 Product Mix – a story of attitudes_**

Until the late 1980s Australians had a voluntary system of retirement savings. The age pension was seen as the principal form of support in retirement and private savings were viewed as providing funds which they could use freely as they chose. Employees who changed jobs were able to cash their entitlements. The average balance in private superannuation was relatively small. In the mid-1990s fewer than 10 percent of lump sums exceeded $200,000. Those receiving a smaller lump sum were more likely to use it to clear debts, for home improvements and for general living expenses. Data on intended disbursement of lump sums appear to indicate that as a lump sum increases in average size, a greater proportion will be spent on financial investments.

For the whole of the superannuation industry, lump sum payments declined in recent years from representing 79 percent of all benefits in 2002 to 68 percent in 2005 and further to 55 percent in 2007 (Table 16). Total benefit payments have fluctuated around 4 percent of GDP.

**Table 16** : **Composition of Benefit Payments**

||2002|2003<br>percen|2004<br>t of total be|2005<br>nefit payme|2006<br>nts|2007|
|---|---|---|---|---|---|---|
|Lump Sums|79.1|78.6|68.4|67.7|58.6|55.2|
|Pensions|20.9|21.4|31.6|32.3|41.4|44.8|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|
|Total (AUD billion)|32.5|33.2|30.7|32.5|37.7|41.1|
|Total(% of GDP)|4.3|4.1|3.7|3.6|3.9|4.0|

**Source: APRA**

This pattern conceals large differences across types of funds, even though all superannuation funds report a decline in the relative importance of lump sum payments (Table 17). Public sector funds, reflecting the historical importance of defined benefit and pension paying funds, show a high proportion of pension payments. These grew from 42 percent of all benefits in 2002 to 69 percent in 2007. In corporate funds pension payments 41
1999 annual report.<sup>3</sup> In 2001 the focus was on investment strategies that did not appear to have been designed in the best interests of fund members.<sup>4</sup> APRA regulators also questioned why key features of the standard banking/insurance supervisory model should not be adapted for use with superannuation - features including universal licensing to control entry to the industry more effectively, a more prescriptive approach to the minimum acceptable features of risk management systems, and the application of prudential standards on the fitness and propriety of industry participants and on outsourcing that were then under development for the other sectors. APRA’s post-1999 organization structure that eschewed specialist industry divisions encouraged such questioning.

# **1.5 Superannuation inquiry 2002**

Late in 2001 the government commissioned a report from a superannuation working group (SWG) on improving the safety of superannuation. The working group was chaired by a non-executive member of APRA’s board and its report, completed late in 2002, concluded that it was an opportune time to review the existing prudential regulatory framework from a ‘preventative maintenance’ perspective.

The working group’s recommendations were mostly adopted by the government and, when carried into legislation, became the basis for reforms recently introduced by APRA.

The revised supervisory framework strengthens APRA’s powers over entry (licensing) and the fitness and propriety of persons in the industry, spells out in more detail what APRA expects in risk management plans and the resources available to funds and introduces tougher attestation and whistle-blowing rules. APRA also updated its statistical reporting framework to provide more powerful statistics for making risk assessments **.**

# **2. Strengthening the Risk-Based Policy Framework**

The main elements of the revised framework introduced over 2004-2006 were as follows.

# **2.1 Licensing and registration**

All trustees must now be licensed by APRA, and all superannuation funds with a licensed trustee must be registered. Previously, APRA licensed only trustees of public offer funds (‘approved trustees’).

Licenses are available to trustee corporations or, where the trustee board consists of individuals, to group/s of individual trustees. There are two main classes of license – public offer and non-public offer. Applicants for the former have to meet the capital requirements.

- 3 Australian Prudential Regulation Authority, _Annual Report 1999_ , Sydney, p.20.

- 4 Australian Prudential Regulation Authority, _Annual Report 2001_ , Sydney, p.9.

10
**Table 4: Impacts on Market Level**

||Total Market <br>Profits|Total Market <br>Sales|Total Market<br>Customers|# New<br>Entrants|Total Other<br>Firms|Other Firm<br>Profits|
|---|---|---|---|---|---|---|
|Market Assigned to Training*1 Year|792|2672|131|0.193|1.473|4439|
||(1378)|(5211)|(104)|(0.509)|(1.375)|(2940)|
|Market Assigned to Training *3 Years|2554|15353**|318**|0.833|1.290|2662|
||(1568)|(6613)|(124)|(0.785)|(1.784)|(3685)|
|Mean of Control Markets|27651|110944|1916|8.59|22.48|34870|
|Sample Size|628|628|628|301|301|301|
|P‐value: 1 Year=3 Years|0.201|0.021|0.038|0.517|0.933|0.684|

Notes: Standard errors in parentheses, clustered at the market level. Unit of observation is market‐survey round. *, **, and *** denote significance at the 10, 5, and 1 percent levels respectively.

Regressions include controls for randomization strata, survey round, and baseline value of outcome where available.

31
_Table 4: Program impact on food budget share for nonbeneficiary households with 6-60-month-_ _<u>old children and children’s food intake</u>_

||(1)<br>Budget<br>Share of<br>Food|(2)<br>Whether<br>Eggs<br>Were Fed<br>to Child in<br>Past Week|(3)<br>Number<br>of Eggs<br>Fed to<br>Child in<br>Past<br>Week|(4)<br>Whether<br>Meat<br>Was Fed<br>to Child<br>in Past<br>Week|(5)<br>Whether<br>Fish Was<br>Fed to<br>Child in<br>Past<br>Week|(6)<br>Whether<br>Dairy Was<br>Fed to<br>Child in<br>Past Week|
|---|---|---|---|---|---|---|
|_Panel A: Program impact_|||||||
|Program village|0.036*|0.037|-0.154|-0.058|0.002|0.011|
||(0.021)|(0.038)|(0.170)|(0.046)|(0.036)|(0.047)|
|_Panel B: Interaction with satu_|_ration_||||||
|Program village|-0.016|0.301**|0.743|0.102|-0.015|0.382***|
||(0.066)|(0.127)|(0.640)|(0.131)|(0.113)|(0.135)|
|Saturation|0.089|-0.071|-0.216|-0.385**|-0.313**|0.171|
||(0.104)|(0.212)|(0.963)|(0.173)|(0.152)|(0.200)|
|Program village*Saturation|0.085|-0.439**|-1.495|-0.253|0.045|-0.627***|
||(0.117)|(0.215)|(1.056)|(0.223)|(0.178)|(0.226)|
|_Panel C: Interaction with abo_|_ve median s_|_aturation_|||||
|Program village|0.033|0.123**|0.063|-0.047|0.005|0.064|
||(0.024)|(0.049)|(0.221)|(0.058)|(0.048)|(0.066)|
|Above median saturation|0.032|0.100|0.184|-0.105|-0.114**|0.058|
||(0.041)|(0.068)|(0.311)|(0.064)|(0.052)|(0.080)|
|Program village*Above<br>median saturation|0.007|-0.203***|-0.504|-0.024|-0.006|-0.125|
||(0.044)|(0.077)|(0.350)|(0.095)|(0.067)|(0.097)|
|_Panel D: Interaction with top _|_quartile of _|_saturation_|||||
|Program village|0.031|0.053|-0.022|-0.026|0.002|0.054|
||(0.024)|(0.044)|(0.184)|(0.051)|(0.039)|(0.054)|
|Fourth quartile of saturation|0.005|0.033|0.516|0.042|-0.144**|0.012|
||(0.047)|(0.085)|(0.476)|(0.073)|(0.071)|(0.092)|
|Program village*Fourth|0.026|-0.092|-0.846|-0.176|0.055|-0.212**|
|quartile of saturation|(0.057)|(0.099)|(0.516)|(0.120)|(0.093)|(0.108)|
|Control Observations|214|269|268|269|269|269|
|Treated Observation|230|295|292|292|293|292|
|Control Mean|0.596|0.755|2.131|0.665|0.818|0.342|

note: *** p<0.01, ** p<0.05, * p<0.1 All child-level specifications include quadratic terms for child age (in months), and all specifications include municipality fixed effects. Standard errors are clustered at village level; there are 130 villages. Source: 2011 _Pantawid_ impact evaluation survey 47
- 8 -

supervision of trained health personnel. To control for differences between facilities, the number of beds ( _BED_ ) was used as a proxy for each institution’s capacity. A dummy variable for health centers ( _CENTR_ ), as opposed to hospitals, is also included in _X_ . Health centers generally have less capacity than hospitals. In addition, to control for the size effect, population density within a 10-kilometer radius of each facility, calculated using the global population data set, WorldPop ( _POPDEN_ ), was included. In populated areas, it is expected that more people will visit a health facility regardless of transport connectivity.

# **<u>Table 1 Summary Statistics</u>**

|<br>Variable|Abb.|Obs.|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Number of patients in outpatient|OPD|69|8.76|7.20|0|44.78|
|department (1,000)|||||||
|Number of malaria cases treated (1,000)|MAL|69|1.29|1.14|0|5.15|
|Number of babies delivered (1,000)|DEL|69|0.39|0.45|0|2.61|
|Population density in a 10-km radius of<br>each facility (1,000 per km<sup>2</sup>)|POPDEN|69|0.74|1.19|0.01|3.63|
|Poverty rate (percent)|POV|69|56.15|18.28|31.60|78.90|
|Number of beds (1,000)|BED|69|0.38|1.01|0|8.02|
|Road density within a 10-km radius of<br>each facility (km/100 km<sup>2</sup>)|RDDEN|69|34.66|24.31|5.68|94.75|
|Share of good roads within a 10-km<br>radius (%)|GDRD|69|14.39|13.26|0|63.86|
|Dummy for health center|CENTR|69|0.57|0.50|0|1|
|Instrumental variables|||||||
|Slope of land where facility is located<br>(degrees)|SLOP|66|0.4|0.5|0.0|3.6|
|Elevation of land where facility is<br>located (m)|ELEV|66|143.3|162.2|5.0|547.0|
|Distance to nearest historicport(km)|DIST|69|76.7|67.2|0.4|222.7|

An empirical challenge to estimating Equation (1) is that transport connectivity may be endogenous. For instance, more people may visit a particular hospital because it is better connected, but transport connectivity may be better because the government invests in roads where more people live. The risk of this self-selection bias is generally high when the effect of infrastructure investment is examined (e.g., Donaldson 2010; Mu and van de Walle 2011). Several techniques to address the problem have been discussed in the literature. Panel analysis can eliminate selection bias, as long as unobserved characteristics are time-invariant (Dercon et al. 2008; Khandker et al. 2009; Khandker and Koolwal 2011). Unfortunately, the
**Table 16: Inputs to Lee trimming procedure**

||Control|Treatment|
|---|---|---|
|Number of observations|444|453|
|Proportion non-missing|71.0%|79.3%|
|Math score|73.0%<br>|71.1%<br>|
||(20)|(23)|
|English score|61.0%|62.0%|
||(18)|(21)|

Notes: The dependent variable is a standardized test score. Standard deviations are in parentheses.

# **Table 17: Bounds for the average treatment effect, accounting for selection using the trimming procedure**

||Lee’s upperbound|Lee’slowerbound|
|---|---|---|
|Math|0.17|-0.19|
||(0.06)|(0.09)|
|English|0.26|-0.16|
||(0.07)|(0.11)|

Notes: The dependent variable is a standardized test score. Standard errors are in parentheses.

**Table 18: Classroom Stallings, instructional time allocation**

|||Share o|f time* (%)||
|---|---|---|---|---|
||All|WSD|GRANT|CONTROL|
|Learning activities|44|44|44|45|
|Social interaction|22|21|23|22|
|Student (s) uninvolved|19|20|18|19|
|Discipline|1|1|2|1|
|Classroom management|2|2|1|1|
|Classroom management alone|3|3|3|2|
|Teacher out of the room|9|8|10|10|
|Obs.|534|176|183|175|

Notes: Based on ten two-minute snapshots of classroom activities in 534 classroom observations.

35
to an increase in its price of 2.94%. This estimate is closer to the more inelastic estimates produced in prior studies.

Three main takeaways emerge from our analysis, all of which are consistent with theories of limits to arbitrage such as those developed by Koijen and Yogo (2019) and Gabaix and Koijen (2020). First, there is a broad range of investors (beyond passive funds and ETFs) that have ‘implicit’ mandates to closely track the composition of the indexes they follow so as to deviate very little from benchmark returns.<sup>5</sup> Second, hedge funds and other investors, that are supposed to act as arbitrageurs in financial markets, are too small to soften the price impact of demand shocks generated by investors with such ‘implicit’ mandates. Third, consistent with the other two results, the demand elasticity for stocks is highly inelastic, contrary to what is typically predicted in canonical asset pricing models.

# **2 Data and Institutional Framework**

To study rebalancing events, we exploit episodes of additions to and deletions of Colombian stocks from MSCI international equity indexes, together with transactionlevel data from the Colombian stock exchange, _i.e._ , the _Bolsa de Valores de Colombia_ (BVC, henceforth). In this section, we describe these episodes and discuss the main features of the BVC and the proprietary transaction data.

## **2.1 MSCI Additions and Deletions**

MSCI indexes are the most widely tracked international benchmarks for institutional investors in equity markets. Importantly, many of their flagship indexes contain stocks from different countries, and are thus followed mostly by large international investors. In our analysis, we exploit additions to and deletions of stocks from MSCI Standard Indexes, which are the largest in terms of the AUM benchmarked against these indexes.<sup>6</sup> Starting from the MSCI quarterly reports for the Standard Indexes, we identify 18 episodes of additions and deletions of Colombian stocks in the 2006-2017 period.<sup>7</sup> We retrieve from the reports the an-

> 5This behavior is consistent with benchmark indexes being the preferred habitat for many different investors. See for instance Pavlova and Sikorskaya (2020) and Kashyap et al. (2021).

> 6MSCI also provides other indexes, such as Small Caps, Value, and Growth indexes, that might differ in their composition from the Standard Indexes.

> 7Colombia is part of the MSCI All Country World Index, the MSCI Emerging Markets Index, and the MSCI EM Latin America, among other indexes. When a Colombian stock is announced to be included into or excluded from the Standard Indexes, this recomposition affects all of these indexes. Our sample includes 10 additions and 8 deletions. Stock ticker

4