rafmacalaba/datause-encoder-data huggingface dataset:
those episodes. In addition, it is also cheaper to borrow short-term during tranquil times because there is a positive transition hazard rate from tranquil to crisis times. As a result, when a country issues long-term bonds during tranquil times, it has to compensate bondholders for the drop in bond prices that would take place if a crisis were to materialize. In other words, the spreads on long-term bonds issued during tranquil times re fl ect not only the expected losses from default, but also the expected risk premia that would need to be paid during the lifetime of the bond. This is true irrespective of whether a crisis actually materializes. All that is needed is a positive transition probability.

A caveat to this reasoning applies. This argument is valid only if emerging economies cannot repurchase outstanding long-term bonds at the beginning of crises by issuing short-term bonds. In other words, we are assuming that by issuing long-term bonds during tranquil times, countries are forcing themselves to go through crises carrying these long-term obligations. The assumption that there is some cost associated with repurchasing long-term bonds during crises is supported by the fact that countries do not repurchase existing long-term bonds during those times, while they very seldom issue new long-term bonds during periods of fi nancial turbulence (as shown in the next section). It is difficult to explain this knife-edge result of zero net new long-term issues in the absence of some cost (signalling or otherwise) associated with retiring existing long-term bonds.<sup>38</sup>

# 5 Bond issuance

The model in Section 2 shows that the comovement between the term premium and the maturity structure of debt can shed light on the type of shocks characterizing the cyclical behavior of emerging market debt. On the one hand, demand side shocks such as an increase in current fi nancing needs or a reduction in future expected government revenues are likely to lead to an increase in the maturity structure of debt and a resulting increase in the term premium, generating a positive comovement. On the other hand, supply side shocks, such as a decrease in investors’ wealth, are likely to lead

> 38 The IMF (1999) reports that in fact there have been some attempts to buyback debt perceived to be “mispriced,” i.e. paying a high risk premium. An example is the buyback of Polish Brady bonds in 1998, which amounted to some 750 million dollars, after a buyback of around 1.7 billion dollars in 1997. Given that these operations, if known, would drive bond prices up, they are usually carefully conducted and not officially con fi rmed until after they have been completed. If successful, a quiet buyback at the discounted price delivers a net reduction in the debt burden. Though these operations exist, the difficulty in completing them successfully makes them rare and small relative to the outstanding debt. Moreover, these operations take place in favorable times.

26
The distribution of bonuses is based on the so-called contribution principle to avoid unjustified and distorting transfers across different groups of members and between members of pension plans and the owners of the institutions managing the plans. The parameters of the contribution principle are set by each institution and reported to the DFSA, which has the power to review and disallow them, if it finds them unfair or imprudent.

The return to capital normally consists of a return related to the result plus a “fair” risk premium. In strong financial years, life insurance companies that are set up as joint-stock companies can, in addition to the yearly dividend, pay the owners a dividend compensating for “shortfalls” in earlier, weaker, financial years. Those “fair” but not paid out dividends will normally be accumulated in a “shadow account” in bad years and be paid out in years when financial results are sufficiently strong to cover past shortfalls in the compensation of capital. This is a controversial way of handling "fair" payment to capital. It is not seen in many companies outside the Danish pension sector<sup>1</sup> .

# **III. Motivations for a Risk-Based Approach**

# **1. Insolvency and Rescue Operations in the 1990s**

In the early 1990s, the Danish FSA engaged in "rescue operations" for a number of banks involving the transfer of assets to stronger banks. Despite the avoidance of depositor losses the DFSA was criticized for allowing such problems to develop. The DFSA recognized that expectations were that it not only enforce legal compliance but that it also take preventative action to prevent problems from emerging.

These considerations led to a growing emphasis on risk-based supervision as a means for ensuring that financial institutions maintained capital and reserves that were commensurate with the risks they assumed. Risk-based supervision was also promoted as a means for identifying individual institutions that were likely to face solvency problems.

The early use of a risk-based approach for pension institutions was also encouraged by the fact that the Danish FSA combined supervision of all financial institutions, including banks and insurance companies.

# **2. Growing Concerns about Future Solvency of Pension Institutions**

Back in 1984 the maximum technical discount rate was set at 4.5 percent, and most of the policies issued on the basis of the new mutual common calculations explicitly contained a guaranteed minimum nominal interest rate of 4.5 percent after tax. With a nominal interest rate level above 20 percent at that time, the market looked at the guaranteed interest as a technicality. An option with no value as it was deep out of the money. As already noted, due to market developments the maximum discount rate, which also acted as the guaranteed minimum rate, was decreased to 2.5 percent in 1994 and 1.5 percent in 1999.

> 1 See Danish FSA – Pension Market Council’s report about fair return to capital, 2004 & Danish FSA –return on capital in the L&P industries and the guaranteed return to capital owners.

9
|Classes:|All|Some|None|
|---|---|---|---|
|a| Mathematics classes study similar content but at different levels<br>of difficulty|0.1038<br>(0.03512)|0.5626<br>(0.0811)|0.35534<br>(0.0079)|
|b) Different classes study different content or sets ofMathematics|o.02|0.5206|0.4595|
|topics that have different levels of difficulty|f0.01)|(0.0798)|(0.0809)|
|aa<br>;<br>;<br>cl) Students are grouped by ability within their Mathernatics classes|0.0763<br>(0.0341)|0.66<br>0.07)|0.2657<br>(0.0618)|
|dj In mathernatics classes, teachers use a pedagogy suitable for<br>«students with heterogeneous abilities><br>Source: 2003 PISA School Sarnple, General High School Strata<br>Note:Standarderrorsarereportedinparentheses.|0.1333<br>(0.0373)|0.75?<br>(0.052)|O.11<br>(0.03)|

Source: 2003 PISA School Sarnple, General High School Strata Note: Standard errors are reported in parentheses.
educational interventions, sorting of students or parental pressure. If these factors also affect outcomes, then our estimates of impact will be biased. In the next subsections, we address each of these potential biases separately. We first test the validity of the key identification assumption of difference in difference models: the equality in the evolution of the outcome variables prior to the intervention.

# **_5.1. Testing for Balance in Pre-Interventions Trends_**

We present two tests of the equality of pre-intervention trends of the outcomes of interest between the groups of treatment and comparison schools. The difference in differences model uses the post intervention trend in the control group as an estimate of the counter-factual, i.e. what would have been the change in the treatment schools’ outcomes if they had not had AGE. If pre-intervention trends are not statistically different, then it is likely that the post intervention trends would have been the same without AGE. The argument is as follows: in the absence of the intervention, the evolution of the dependent variables during the post-intervention period (at _t_ ) should not be significantly different between treatment and comparison schools, had it not been significantly different in the pre-intervention period (at _t’<t_ ).

Hence, we first estimate the following specification on pre-intervention data, i.e. for all _t’_ =1995-1997: where,

- α _s_ are school fixed effects;

- ξ _lt_ are state specific time dummies;

16
2 Apart from tariffs, developing countries generaly regard OECD nontariff measures (NTMs) as baving an even more negative impact on their exports. The UNCrAD Secreta (1993, p. 41) shows that nontariff measures inposed by industrial countries cover a nuch higher share of imports from developing countries than they do in respect of intra-OECD trade (see Table 1 for World Bank estimates). Almost one fift of all non-fuel imports from developing countries are subject to NTMs in industrial country markets, while the corresponding share for trade among industrial countries is about one-tenth.

The purpose of this paper is to determine how far the Uruguay Round, when its results are fully implemented, will change the level, nature and incidence of OECD countries' NTMs on developing countries' exports. Preliminary information on what the Round accomplished suggests that a major change has occurred, yet little or no quantitative evidence has been produced thus far on the magnitude of the change. By utilizing a trade information system mainined by the World Bank and UNCTAD, this study atteLp5s to estimate how the Uruguay Round will alter the profile of NTM protection facing developing countries, both in total and for regional groups of exporters. After an overall assessment of the findings, the study goes on to anticipate how OECD countries may atempt to neutralize or "claw back" the trade liberalition implied by the loss of NTM protection.

# II. Nontariff Measures Facing Develooir Countries

Utilizing World Bank-UNCTAD records on OECD trade barriers, Table 1 shows the share of these countries' imports from: (i) all industrial counties, and (ii) all other countries that encounter NTMs. The data show that OECD nontariff measures affect a notably higher share of imports from 3Laird and Yeats (1990, Chapter 4) provide a detailed description of how this inventory of nontariff measures was construtd and what its limitaions are for research and polcy studies. In particular, tade coverage ratios are a rough approimation of the importance of NTMs, in dua they give no indication of the restrictiveness of the measures concerned. Low coverage ratios could be assocated with higbly rcstictive NTMs, and vi-versa. The Laird and Yeats book also provides extensive empircal information on the results of NTM mvetory studies for industi comntries. UNCTAD (1993, p. 37) tabulate the amnal share of developing counties' exports that encountered nonaiff measures over the last decade. The share of developing countries non-fuel exports covered by NTMs rose from 16.2 percent in 1981 to 18.3 percent in 1991.
18. W ht works boi? In the second type of res-arch, the questior, is not whether training is better than no training, but rather, whcb types of training are most effective. Perhaps the dominant mode of research for this purpose is the literature review: The investigator collects a large number of studies of the first type (which compare particular training programs to . control) and seeks to identify the characteristics of the programs prodacing the largest effects.

19. For example, Joyce and Showers (1980) reviewed 200 studies of a large variety of inservice training programs. They found that the most effective courses were characterized by five components: presentation of theory, "modeling" or demonstration of teaching techniques, practice teaching under simulated conditions, structured feedback after classroom observation, and coaching for application to new settings.

20. Ingvarson and Mackenzie (1988) reviewed the effects of week-long courses on computer education provided over a two-year period to 2000 teachers in Australia. They aimed to identify the factors that influenced teachers roles as "change agents" in disseminating knowledge in their schools after completion of the course. They found that teacher action as change agents varied from school to school and depended heavily on administrative support and follow-up assistance. They concluded that "Returns to investments in inservice education by school systems will be limited if planning for policy implementation goes no fur.her than the provision of inservice courses alone, without the orchestration of follow-up support, both from within the school and from external sources." a
effects, leads to a gross overstatement of the procyclical behavior of equity returns. It also weakens dramatically the estimated model’s empirical performance. Including the common factor, while still omitting spatial effects, greatly helps correct these problems, at the cost of remaining residual (weak) dependence. In turn, allowing for spatial effects, while omitting the common factor, also improves the model fit, but – leads to overstated spatial effects exaggerating the propagation of shocks through – local linkages and strong residual dependence. Overall, these results confirm the need to account for cross-sectional dependence, both strong and weak, in empirical modeling of equity returns across countries.

The rest of the paper is organized as follows. Section 2 lays out the factor-augmented dynamic spatial model of equity returns employed in the paper. Section 3 presents the data. Section 4 reports the results, and the final section provides conclusions.

# **2 Analytical framework**

To study the comovement of equity returns, we use a dynamic model that allows for both common factors and spatial dependence. This section describes the model and summarizes our estimation approach.

## **2.1 A factor-augmented dynamic spatial model of equity returns**

Let _git_ denote the real equity returns in country _i_ = 1 _, . . . , n_ at time _t_ = 1 _, . . . , T_ , and let _gt_ = ( _g_ 1 _t, ..., gnt_ )<sup>_′_</sup> . Following Shi and Lee (2017), we assume that _gt_ follows a spatial dynamic panel data (SDPD) model of the form: Thus, each country’s real equity return is related to current real equity returns in (economically) neighboring countries, _Wgt_ , where _W_ is an _n × n_ spatial weight matrix; lagged equity returns in the own country, _gt−_ 1, as well as in neighboring countries _Wgt−_ 1; a set of observable explanatory variables _Xt_ ; a set of _r_ time-varying unobserved factors _ft_ common to all countries; and a stochastic disturbance _Vt_ .

This general specification allows for both spatial dependence and unobserved common factors. Spatial dependence, embedded in the spatially-lagged dependent variable _Wgt_ as well as its time-lagged value _Wgt−_ 1, reflects the effects of current and lagged equity returns of nearby countries on the equity returns of a particular country. The extent of spatial dependence is measured by the contemporaneous spatial autoregressive parameter _ρ_ and the space-time lag coefficient _λ_ .<sup>6</sup> The relative contribution of each country to the overall spatial effect is measured by the spatial weight matrix _W,_ which can be understood as providing a measure of economic proximity between countries.

In turn, the unobserved common factors _ft_ capture systemic shocks that affect stock returns across all countries. The _n×r_ matrix of factor loadings Ψ measures the (possibly

> 6The parameter _λ_ , termed ’diffusion parameter’ by Shi and Lee (2017), captures spatio-temporal correlations in equity returns that may result from partial adjustment or inter-temporal decision making by investors, see e.g., Tao and Yu (2012).

4
Table S. Gains In Births Averted through Female Education and Family Planning Program Services: Simulation Result4 Births per year, 1985 (millions)

||RaiingFe|mal Secondary Enrolment||||Rasing_Fami_|_ly_Pbanning Sei|c|||
|---|---|---|---|---|---|---|---|---|---|---|
|Countriesp|Actual|Rmarks|Simulated|Bilbw<br>Averted|Col._5_as<br>% of col. 2|Remarks|Simulated|Biths<br>Avrted|CoL 9<br>as% of<br>l 2||
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|co.<br>(10)||
|_LAw Nad_Middle Income<br>(a60|102.5|Scenario 1: if female<br>gross enollmet atesin<br>1975 wene doubled to<br>|72.9|29.6|29|Scenaro 1W<br>Scenario 2Y|98.9<br>93.4|3.6<br>9.1|_3.5_<br>8.9||
|Low Income (n=31W|81.2|38% from 19% (actual).?<br>.|_55.6_|25.6|32|Scenario_1e_<br>Scenario<br>2|79.6<br>77.1|1.6<br>4.1|2.0<br>_5.0_||
|Sub-Saaran Afica (n-26)P|17.4||11.3|6.1|35|Scenario<br>lI<br>Scenario 2Y|16.2<br>15.5|1.2<br>1.9|6.9<br>11.0|w4S.|
|SouthAsia(n=S)P|33.9||17.7|16.2|48|Scenario 1l<br>Scnario 2'|33.7<br>31.9|0.2<br>2.0|0.6<br>5.9||

1/ These simulations are based on equation 4 in Tabk 1. 2/ According to World Bank claification. 11 Thes 65 countries represent 76% of the world's population and 93 of the LDC population, including China. _4i_ Keeping all other variables constant at their mean values.

_I These 31 countries represent 61% of the world's populaticoa **n** d 97% of the low-income LDC population, including China. __i_ These 26 countries are: Benin, Botswana, Buriira Faso, Burundi, Central African Republic, Cot d'Ivoire, Ethiopia, Ghana, Guinea, Kenya, Lesotho, Libeia, Madagascar, Malawi, Mali, Mauritius, Mozambique, Niger, Nigera, Rwanda, Sierra Leone, Somalia, Sudan, Tanzania, Togo, Uganda, Zaire, Zambia, and Zinbabwe. They represent 8.7% of the world's population nd 94% of SSA's population. 2/TheseS counries ae: Bagladesb, Nepal, India, Pakistan, and Sri Lanka. They repreet 23.1% of the world's population and 96% of South Asia's popubtion.

_jI_ Scenario 1: if family planning service index score in 1982 were doubled to 50%, from 25% (actual) (see footnote 4). kI Scenrio 2: if family planning service index score in 1982 were tripled to 75%, fom 25% (acua (see footnote 4).
LATE = _E_ [ _Y_ 1 _i_ – _Y_ 0 _i_ | _D_ 1 _i_ ≠ _D_ 0 _i_ ]. (1)

Imbens and Angrist (1994) show that if _Y_ 1 _i_ , _Y_ 0 _i_ , _D_ 1 _i_ , and _D_ 0 _i_ , are independent of _Zi_ and if _D_ 1 _i_ ≥ _D_ 0 _i_ , for all _i_ (monotonicity), Since all the participants in our program were invited, _D_ 0 _i_ is 0 and _D_ 1 _i_ is either 0 or 1. Those invited entrepreneurs with _D_ 1 _i_ = 0 are refusers and those with _D_ 1 _i_ = 1 are participants. Thus, _D_ 1 _i_ ≠ _D_ 0 _i_ in equation (1) means that entrepreneur _i_ will participate in the training if invited, and LATE in our case is equivalent to _E_ [ _Y_ 1 _i_ – _Y_ 0 _i_ | _Di_ = 1], i.e., the average training effect on the participants. It is easy to show that the monotonicity condition is satisfied in our case. Equation (2) implies that LATE can be estimated as coefficient  in a regression model, _Yi_ =  +  _Di_ +  _i_ , by using _Zi_ as an instrumental variable (IV). In our case, the sample size is small and, hence, it is advisable to use the pre-training data as well as entrepreneurs’ attributes as follows:

where subscript _t_ denotes year 2000, 2002, 2004, or 2008, _Tt_ is a dummy variable which is equal to 1 if year _t_ is after the training (i.e., _t_ = 2008), _Xit_ is a vector of the entrepreneur’s background attributes (which are mostly time invariant),  _t_ is a year effect, _ui_ is an unobserved individual effect, and  _it_ is an error term. The average training effect on the participants,  , is estimated by using _Zi_ and _ZiTi_ as instruments.

The magnitude of the training effect may vary from participant to participant. Heterogeneous training effects may be captured by adding _WitDiTt_ and _WitDi_ to equation 16
Caprio and Demirguc-Kunt (1998), Corbo and Schmidt-Hebbel (2003), BIS (2007), Borensztein, et al. (2008), Eichengreen (2009), Della Croce, et al. (2011), OECD (2013a,b), and The Economist (2013, 2014). Institutional investors are also expected to professionally manage assets, diversify risk, and overcome problems of asymmetric information and transaction costs that pervade financial markets. But how long institutional investors invest depends on many factors including their utility function, their liability structure, and the incentives managers face from markets and regulators (Bajeux-Besnainou, et al., 2001; Campbell et al., 2001; Campbell and Viceira, 2002).<sup>2</sup> Despite the expectations that many authors have placed on institutional investors and their large size and continuing rapid growth in many countries, little evidence exists on whether these investors actually invest long term and how they structure the maturity of their portfolios. This lack of evidence is mainly due to the difficulty in obtaining detailed portfolio data on institutional investor holdings. The literature on portfolio composition has focused almost exclusively on specific institutional investors in developed countries (to a large extent, US mutual funds). The international evidence has concentrated on international mutual funds domiciled in international financial centers and their investments across countries, ignoring the behavior of the large domestic institutions and the heterogeneity across investor types. Moreover, the literature tends to center just on equity holding, and is therefore silent on the maturity choices of institutional investors.<sup>3</sup>

> 2 For general references on their expected impact on capital markets, see, for example, Davis (1995), Davis and Steil (2001), and Impavido et al. (2003, 2010).

> 3 See, for example, Grinblatt and Keloharjub (2000), Kim and Wei (2002), Borensztein and Gelos (2003), Kaminsky et al. (2004), Gelos and Wei (2005), Broner et al. (2006), Hau and Rey (2008), Jotikasthira et al. (2012), Raddatz and Schmukler (2012), and Didier et al. (2013).

3
The remainder of the paper is organized as follows. In Section 2, we develop the theoretical model that underlies our priority index methodology. Section 3 introduces the Tunisian database, while Section 4 presents our priority index computations and discusses the results. We provide a summary and conclusions in Section 5.

# **2.  Optimal Allocation of Cleanup Investment**<sup>**5**</sup>

We model the welfare impact of pesticide stockpile cleanup activities as a function of their level and distribution across Tunisian sub-regions (delegations). Political considerations make it desirable to strike a balance between area representation and national welfare maximization in resource allocation decisions. We cannot realistically characterize Tunisia’s objective function as linear (infinite elasticity of substitution across sub-regions), because sole allocation of cleanup resources to one sub-region is highly unlikely, whatever the relative scale of its problem. At the same time, Tunisia’s objective function is not purely fixed-coefficient (zero elasticity of substitution across sub-regions), because nothing forces the government to maintain cross-subregion parity in per-capita resource allocation. This assertion is acceptable in the case of the pesticide cleanup program, since the distribution of obsolete pesticide stockpiles across subregions may not be highly correlated with the distribution of population.

We adopt an intermediate assumption: that the government’s objective function is characterized by unit-elastic substitution across subregions. A unit-elastic (CobbDouglas) welfare function permits tailoring of cleanup programs to conditions in each subregion, while encouraging some diversification through the operation of diminishing returns. We assign the same opportunity values to human life and health in all subregions.

We specify the objective function for pesticide cleanup as:

> 5 The allocation model developed in this section is a significant extension of the model developed by the authors in Bolt _et al_ . (2003).

4