rafmacalaba/datause-encoder-data huggingface dataset:
take off, countries which had higher levels of economic development and financial deepening would generally have larger stock markets and we would observe a higher correlation between the variables.

If the threshold hypothesis were true, one might expect to observe about the same levels of financial depth and income in the economies where the stock markets had a general upturn in the mid-1980s. In reality, as measured in 1985, the levels of income and financial depth varied widely across the countries. This is evident from Figures 14 and 15 which show the levels of QMY and DCPY in 1985 and the level of GNP per capita in 1985 respectively. The patterns emerging from these figures do not seem to support the threshold hypothesis.

However, several difficulties make the threshold hypothesis almost untestable. One is that it is difficult to determine whether the upturn in the size of the market around the mid1980s was actually a take-off. In order to identify the take-off points, to determine whether there exist threshold levels of income and financial depth and to identify what the threshold levels are, it is necessary to have a long enough time series. The time series we have are apparently too short for such an exercise. As a further test, it would be revealing to compare the levels of economic development and financial deepening in the developed economies at the time when there were a broad upturn in the size of their stock markets. But even for the developed economies, the test is difficult to conduct because of lack of data. Figure 13 shows the movement of MCY in the United States from 19296. In 1929, the United States had a MCY of more than 60% and GNP per capita in 1982 constant prices was already US$5834. It is thus necessary to trace back much earlier, probably back to the end of last century when the major developed economies had a rise of the stock market. But data are very difficult to obtain for that early period.

6 The figures of market capitalization before 1983 were those of the New York Stock Exchange, while the figures after 1983 were those of all United States.

9
control, and external factors, such as the volatility of terms or trade and the volatility of growth of trading partners. The empirical analysis is based on a dynamic panel General Method of Moments (GMM) estimation that corrects for unobserved effects and endogenous regressors, and data used cover 80 countries over the 1975-2005 period (see the Appendix for details).

**Part of the long-term observed decline in volatility is related to improved domestic fundamentals and better macroeconomic management.** Output growth volatility has fallen thanks to improved institutions, business practices, and other structural features that have enhanced the ability of East Asian economies to absorb shocks. In line with existing studies, we find that the shift towards more democratic and stable institutions and the increased depth and sophistication of financial markets are among the structural changes that have contributed to increase macroeconomic stability, both in East Asia and in the rest of the world. The most significant contribution to the long decline in volatility stem however from improved macroeconomic management: more stable fiscal policies are robustly linked to lower output fluctuations, and the success of policy makers in maintaining low and steady inflation has driven down uncertainty, fostering investment and bringing volatility down.

**Part of the long-term decline in volatility is however also related to improved external conditions out of countries’ control, which in face of the financial crisis are now exerting strong upwards pressure on volatility.** The long-term decline in volatility is also imputable to a decrease in the magnitude and frequency of external shocks. The study shows that while less frequent terms of trade shocks and more stable growth rates of trading partners helped reducing volatility in the past decades, these very same external factors are now exerting strong upwards pressure on volatility. Over the last two years world economic growth declined from 5.1 percent in 2007 to 3.4 percent in 2008, and is projected to fall to 0.5 percent in 2009, its lowest rate since World War II. While the impact of these dramatic changes are only beginning to be captured by formal volatility measures,<sup>4</sup> they are bound to increase medium-term volatility of terms of trade and foreign growth, which, in turn, will affect countries’ volatility.

**East Asia also remains more vulnerable to external shocks because of its high openness to trade.** Average openness to trade in East Asia (i.e. the sum of imports and exports) is 77.8 percent of GDP, against 46.0 for OECD countries (Figure 10). The impact of trade openness on output growth volatility is significant: an increase of one percent in trade openness raises volatility by 0.7 percent. While the expansion of trade has been a key driver of economic growth, it has therefore also increased East Asia’s vulnerability to external output fluctuations.

> 4 Output growth volatility is usually estimated using a 5 or 10 years rolling window, hence the full impact of the current financial crisis will only be captured some years from now.

4
the treatment is medically indicated or not. Therefore, it cannot be determined in general whether a specific treatment is covered or not.

# **5.1 Cataract Surgeries**

According to the Yearbook of Health Statistics of Statistics Austria, all public and private hospitals conducted 60,699 in-patient cataract surgeries in 2005 (see Statistics Austria 2007).<sup>27</sup> There is a difference regarding prices for cataract surgeries between the Austrian LKFpoint system<sup>28</sup> and the specialized Hungarian private clinic “Focus Medical Margitsziget” (see Table 9). Whereas a cataract surgery costs between 1,608 to 2,059 Euro in Austria, it costs between 960 to 1,500 in the Hungarian clinic. If a private patient purchases a cataract surgery on her own in Hungary instead of receiving it in Austria, the potential benefit would range between 559<sup>29</sup> and 648 Euro30.

**Table 9: Prices for Cataract Surgeries in Comparison**

|**Treatment**|**Austria**<br>**According to the LKF-**<br>**System**|**Hungary**<br>**“Focus Medical**<br>**Margitsziget”**|**Potential Benefit**|
|---|---|---|---|
||Euro|Euro|Euro|
|Cataract surgery|1,608–2059<sup>31</sup>|960–1500|648/559<sup>32</sup>|

Source: These data have been taken from the homepage of the Hungarian Private Clinic “Focus Medical Margitsziget” and the Austrian LKF-system, March 2007.

The above calculation has been made on the basis of the Austrian LKF-points, which require several remarks: The LKF-points are only an approximate value. They are composed of a benefit component (in the case of cataract 509 to 551 points) and a daily component (831 to 1165). The LKF-system has its roots in the 1980s and follows basically the DRG-system of the United States. The calculation for a benefit provided in an Austrian hospital is complex and consists of three stages. First, LKF-points are calculated nationally for each and every group of treatments. Second, every region determines a value that corresponds to one LKF-point. And third, every region determines a hospital factor for university or standard hospitals. These three components then determine how much a hospital receives for a certain benefit.

> 27 In 2005, there were 264 in-patient hospitals in Austria officially registered under the Federal Hospital Act, with a total of 63,248 available beds. Independent out-patient health care centers numbered about 1,000 (see Federal Ministry of Health and Women 2007a: 17)

> 28 In 1997, Austria introduced the performance-oriented hospital financing system ( _Leistungsorientierte Krankenanstaltenfinanzierung_ , LKF) based on modified diagnosis-related groups (Austrian DRG System) (for a more detailed discussion see e.g. Hofmarcher and Rack 2006: 178–181).

> 29 Comparing the highest price.

> 30 Comparing the lowest price.

> 31 1340–1716 LKF-points: the benefit component is 509 (551); the daily component is 831 (1165); and the mean value of duration of stay is 2.9 (3.4). The LKF-points have been multiplied by 1.2 Euro. 32 The lowest and the highest prices have been compared.

19
instead of the mean sign deviations of the abnormal returns _G_<sup> ̄</sup> _t_ to build a normally distributed statistic _St_ as where _σ_ ( _G_<sup> ̄</sup> ) is the standard deviation of the mean _G_<sup> ̄</sup> _t_ , which should be zero under the null hypothesis that the event has no impact on returns. The cumulative mean sign deviations and their standard deviations, reported in various gures, are computed in the same manner as the cumulative mean rank deviations.

# 4 Results

This section presents the results of the event study analysis of the impact of multilateral debt relief announcements described above. It rst describes the results obtained for the major debt relief initiatives, followed by those obtained for country-level debt relief announcements (decision and completion points). Results from parametric and non-parametric tests and robustness analysis are presented in each case.

## 4.1 Major Debt Relief Initiatives

The evolution of the estimated cumulative abnormal return _CAR_<sup>�</sup> _τ_ for the three major debt relief initiatives, in an event window of 10 trading days around the event date is reported in Table 4 and depicted in the various panels of Figure 3, along their 90 percent condence bands . Panel A shows the _CAR_ obtained by pooling the three major initiatives, and panels B to D display the _CAR_ separately estimated for the HIPC initiative, enhanced HIPC initiative, and MDRI, respectively.

The announcement of a major debt relief initiative is associated with a statistically signicant increase in the _CAR_ of about one percentage point (Panel A in Figure 3). While cumulative abnormal returns are 10 basis points three days before the announcement, they increase to 140 basis points one day after the announcement (Column (1) of Table 4). Considering that parent companies are large relative to their subsidiaries, this increase is economically signicant and suggests a much larger increase in the underlying value of the aliates. For instance, if a parent company's operations in all HIPC countries represent 10 percent of its overall value (roughly the median ratio of subsidiary to parent employment in the data), a one percent increase in the value of the parent company is 12
coefficients and β the cointegration vectors. The cointegration vectors β have the property that β ' zt is stationary even though _zt_ itself is non-stationary.

If the rank is reduced it is possible to interpret the VAR in first differences as a vector error correction model and to obtain estimates of α and β via the reduced rank regression. Since the rank of Π is equal to the number of independent cointegration vectors and the rank of Π is also equal to the number of non-zero eigenvalues, the test of cointegration thus amounts to a test for the number of non-zero eigenvalues. The trace statistics, λ trace, is a non-standard distributed likelihood-ratio test, which is commonly used to determine the number of cointegration vectors, (Johansen, 1988). The trace statistic tests the null hypothesis that there are at most _r_ cointegration vectors: _H0:_<sup>λ</sup> _i_<sup>=0, for i = r+1,..n</sup> where only the first _r_ eigenvalues, λ , are non-zero against the unrestricted hypothesis that r = n.<sup>2</sup> It is now common practice to try to identify the cointegration space and we follow the approach outlined in Johansen (1992) and Johansen and Juselius (1994). According to Johansen (1992) and Johansen and Juselius (1994) a system is exactly or just identified if _k = r_ - 1 restrictions are placed on each cointegration vector and the rank condition for generic identification is satisfied.<sup>3</sup> If more than _k_ = _r_ -1 restrictions are placed on each cointegration vector then it is possible to test with an LR test whether these over-identifying restrictions are valid and thus restrict the variation of the parameters. If the over-identifying restrictions satisfy the rank condition and the LR test is passed successfully then each cointegration vector is said to be uniquely identified.

> 2 The null hypothesis of at most _r_ cointegration vectors implies that there are _n-r_ unit roots and, theoretically, _n-r_ zero eigenvalues. This is because the hypothesis of cointegration is formulated as the ' '

> reduced rank of Π = αβ and the full rank of α⊥<sup>Γβ</sup> ⊥ , where α and βare _n_ × _r_ matrices and α⊥ and β⊥ are n× _(n-r)_ matrices orthogonal to α and β. This allows us then to distinguish between _r_ cointegrating I(0) relations and n _-r_ non-cointegrating I(1) relations.

> 3 In the case of two cointegration vectors generic identification requires that the rank condition _rank(Ri' Hj)_ ≥ 1 for _i_ , _j_ = 1, 2 and _i_ ≠ _j_ is fulfilled. Where _Ri_ is the orthogonal complement of _Hi_ , such that _Ri_ and _Hi_ are both of full rank and satisfy the conditions _Ri 'Hi_ =0, _Ri'_ βi = 0 and βi = _Hi_ φ i . _Ri_ are _p_ ∗ _k_ matrices and _Hi_ are _p_ ∗ _s_ matrices with _k + s_ = _p_ .

5
demand of farmers for inputs such as machinery and machinery repair can stimulate nonagricultural activity through backward linkages. Non-agricultural activity could be stimulated by agriculture at the same time via forward linkages such as the requirement to process agricultural products through spinning, milling or canning.

Gopinath, Roe and Shane (1996) analyse the possible link between agriculture and food processing and find that productivity gains in agriculture feed back into the food processing industry, where they lead to cheaper inputs. Lower priced inputs lead in turn to increased derived demand for primary agricultural products, thus partly mitigating the price decline. The two sectors evolve interdependently over time, contrary to what the dual economy model predicts.

Further, sectoral studies of growth find long-run relationships and short-run causality between the industrial, agricultural and service sectors in C ô te d’Ivoire, Ghana and Zimbabwe (Blunch and Verner (1999) and Ecuador (Fiess and Verner (2000)).

The dual economy literature generally rules out two major issues about the later stages of development. First, the literature denies that agriculture may be an important growth-promoting factor. Second, it rules out feedback mechanisms between agriculture and industry.

As already mentioned, we find that this third cointegration relationship appears to be stable only from the 1990s onwards, Figure 1 shows the results of a recursive cointegration analysis, where the tracestatistics for the hypothesis of cointegration vector is estimated for different sample periods.

Operationally, the data from 1973:Q1 to 1988:Q1 is used as a base period for the calculation of the first test-statistic and the sample size is then successively increased by one observation at a time until the end of the sample.

10
rms that could formalize once formal sector's entry costs are removed, but choose to remain informal to enjoy the cost advantages of informality, correspond to 41.9 percent of all informal rms. Finally, the remaining 48.8 percent correspond to rms that are too unproductive to ever become formal. Hence a very small fraction of informal rms in Brazil seems to be constrained by high formalization costs, and therefore reducing them will have a limited eect on formalization. Since these policies involve scal costs, they are in general not costeective even when they have positive formalization eects.

# 5.3.2 Other formalization policies (reduction of entry costs, increase in enforcement)

Even though reforms targeted at the reduction of formal sector's entry costs have not been eective in inducing rms to formalize, they might still produce important aggregate eects. Indeed, some papers show that reducing entry costs into the formal sector can produce positive and sizable aggregate eects in general and specically in Brazil. Ulyssea (2010) develops a two-sector matching model that incorporates the main features of Latin American labor markets. The model is numerically solved using Brazilian data and several policy simulations are performed. The simulation results show that lowering the costs of entry into the formal sector substantially improves employment composition. Making formal sector's entry costs similar to informal sector's would increase formal employment by nearly 31 percent and reduce unemployment by 36 percent. As a consequence of the changes on employment composition and the reduction of unemployment rate, average productivity and specially welfare show a substantial increase. Hence, high entry costs not only play an important role in determining employment composition, but also generate a considerable deadweight loss. This latter eect comes from the fact that high creation costs reduce the number of formal vacancies created and, as the informal sector is not able to completely absorb all workers, unemployment increases. In a recent article, Ulyssea (2018) shows that reducing entry costs into the formal sector eliminates deadweight losses from wasteful barriers to entry, which leads to more competition and an increase in aggregate production in the formal sector. It also increases high-skill wages, since the formal sector is more intensive in high-skill labor. Since the formalization process is concentrated among low-productivity rms, there is a negative composition eect that leads to a decrease in aggregate TFP. Nevertheless, total output increases because there is a substantial increase in the mass of active rms in the economy. In contrast, increasing enforcement can have a substantial impact on informality and productivity, since a large fraction of rms could be formal but optimally choose to remain informal. However, this policy can have potentially large negative eects, since nearly 31
<mark>Anomalies: A long-run Test for Rural Tanzania.</mark> _<mark>Ecological Economics</mark>_ <mark>, 164, 106365.</mark> **<mark>[</mark> 4]** Di Falco, S., Veronesi, M., & Yesuf, M. (2011). Does adaptation to climate change provide food security? A micro‐perspective from Ethiopia. _American Journal of Agricultural Economics, 93.3_ , 829-846. **[1]** Di Falco, S., Kis, A. B., & Viarengo M. (2022). Cumulative Climate Shocks and Migratory Flows: Evidence from Sub-Saharan Africa. IZA Discussion Paper No. 15084. **[1]** <mark>Dillon, A., Mueller, V., & Salau, S. (2011). Migratory responses to agricultural risk in northern Nigeria.</mark> _<mark>American Journal of Agricultural Economics</mark>_ <mark>,</mark> _<mark>93</mark>_ <mark>(4), 1048-1061.</mark> **[1]** <mark>Dinku, T. (2019). Challenges with availability and quality of climate data in Africa. In</mark> _<mark>Extreme hydrology and climate variability</mark>_ <mark>(pp. 71-80). Elsevier.</mark> **[3]** <mark>Feng, S., Krueger, A. B., & Oppenheimer, M. (2010). Linkages among climate change, crop yields and Mexico–US cross-border migration.</mark> _<mark>Proceedings of the National Academy of Sciences</mark>_ <mark>,</mark> _<mark>107</mark>_ <mark>(32), 1425714262.</mark> **<mark>[</mark> 1]** <mark>Franklinos, L., Parrish, R., Burns, R. M., Caflisch, A., Mallick, B., Rahman, T., ... & Trigwell, R. (2020). Key opportunities and challenges for the use of big data inmigration research and policy.</mark> _<mark>UCL Open: Environment Preprint</mark>_ <mark>.</mark> **<mark>[</mark> 2]** <mark>Findlay, A. M. (2012). Flooding and the scale of migration.</mark> _<mark>Nature Climate Change</mark>_ <mark>,</mark> _<mark>2</mark>_ <mark>(6), 401-402.</mark> **<mark>[</mark> 1]** <mark>Fröhlich, C. J. (2016). Climate migrants as protestors? Dispelling misconceptions about global environmental change in pre-revolutionary Syria.</mark> _<mark>Contemporary levant</mark>_ <mark>,</mark> _<mark>1</mark>_ <mark>(1), 38-50.</mark> **<mark>[</mark> 1]** <mark>Gaikwad, N., Hanson, K., & Tóth, A. (2022). How overseas opportunities shape political preferences: A field experiment in international migration.</mark> _<mark>Unpublished manuscript</mark>_ <mark>. Available at:</mark> <u><mark>https://nikhargaikwad.com/resources/OverseasOpportunities.pdf</mark></u> **<mark>[</mark> 2]** <mark>Gemenne, F. (2011). Why the numbers don’t add up: A review of estimates and predictions of people displaced by environmental changes.</mark> _<mark>Global Environmental Change</mark>_ <mark>,</mark> _<mark>21</mark>_ <mark>, S41-S49.</mark> **<mark>[</mark> 1]** <mark>Gibson, J., McKenzie, D., & Stillman, S. (2011). The impacts of international migration on remaining household members: omnibus results from a migration lottery program.</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>,</mark> _<mark>93</mark>_ <mark>(4), 1297-1318.</mark> **<mark>[</mark> 2]** 42
**Table 6. Steps 6 and 7 from the hierarchical two-level linear regression models for mathematics and science achievement,** **<u>grade 4, TIMSS 2019</u>**

||**Math**|**ematics**|**Sci**|**ence**|
|---|---|---|---|---|
||**Step 6**|**Step 7**|**Step 6**|**Step 7**|
|<sup>_2_</sup><br>student-level (%)|21.0|26.2|22.5|27.4|
|_R_<br>class/school-level(%)|54.9|71.4|46.0|64.8|
|**Intercept (****_SE_)**|396.29(21.00)|296.27(37.86)|433.35(21.20)|320.48(35.66)|
|**Student-level variables(reference category)**|**_B(SE)_**|**_B(SE)_**|**_B(SE)_**|**_B(SE)_**|
|Student gender (male)|-6.71 (11.92)|164.98 (54.64)**|21.52 (8.79)*|249.50 (59.64)***|
|Student immigration status (native)|||||
|second-generation immigrant students|15.40 (11.36)|14.58 (11.44)|14.12 (11.98)|13.25 (12.08)|
|first-generation immigrant students|26.74 (17.41)|26.53 (17.24)|32.10 (14.51)*|37.67 (13.70)**|
|Home resources for learning|4.35 (2.11)*|4.64 (2.11)*|2.85 (2.79)|2.83 (2.79)|
|Student owns mobile phone (no)|-0.10 (4.70)|0.10 (4.51)|-1.58 (1.96)|-1.45 (1.96)|
|Literacy and numeracy readiness for school|7.57 (1.44)***|7.57 (1.43)***|7.62 (1.41)***|10.35 (1.91)***|
|Preschool attendance and duration (did not attend)|||||
|1 year or less|1.51 (6.97)|0.38 (6.81)|4.88 (5.19)|3.79 (5.14)|
|<br>2 years|<br>-6.20 (8.84)|<br>-8.60 (8.84)|<br>8.61 (6.40)|<br>6.55 (6.36)|
|3 years or more|15.10 (9.06)|14.00 (8.81)|8.71 (8.62)|8.66 (8.49)|
|Student absenteeism (never or almost never)|||||
|once every two months|5.36 (9.85)|4.74 (9.69)|7.90 (7.20)|8.14 (7.23)|
|once a month|7.54 (8.60)|7.21 (8.49)|-2.91 (7.42)|-2.24 (7.39)|
|once every two weeks|-20.34 (8.88)*|-21.87 (8.85)*|-15.47 (9.37)|-15.07 (9.20)|
|once a week|-23.53 (7.35)**|-34.61 (8.67)***|-21.05 (5.67)***|-20.78 (5.50)***|
|Student likes learning mathematics/science|<br>4.67 (1.60)**|<br>4.82 (1.55)**|<br>5.33 (1.76)**|<br>5.16 (1.75)**|
|Student confident in mathematics/science|7.91 (1.50)***|8.24 (1.47)***|7.89 (1.62)***|8.09 (1.61)***|
|Student sense of school belonging|<br>-3.10 (1.16)**|<br>-3.10 (1.17)**|<br>-3.68 (1.32)**|<br>-3.55 (1.31)**|
|Bullying<sup>a</sup>|4.91(1.28)***|5.20(1.26)***|5.78(1.44)***|5.91(1.44)***|
|**Class/school-level variables(reference category)**|||||
|School mean of home resources for learning|15.70 (9.03)|10.65 (8.07)|22.16 (6.74)**|17.82 (5.67)**|
|School location (urban)|||||
|suburban/medium size city or large town|-8.40 (11.43)|-3.81 (9.56)|-10.96 (9.66)|-36.03 (10.25)***|
|small town or village/remote rural|54.91 (26.49)*|42.47 (24.77)|60.77 (20.36)**|60.10 (16.90)***|
|<br>Teacher age (40 years or older)|||||
|29 years or younger|15.59 (19.55)|-4.73 (18.68)|-43.78 (17.99)*|-17.31 (16.16)|
|30–39 years|16.37 (14.17)|18.35 (12.26)|-28.52 (10.26)**|<br>-20.29 (9.24)*|
|<br>Safe and orderly schools|<br>0.32 (3.21)|<br>6.63 (4.00)|<br>5.29 (3.07)|<br> <br>9.92 (3.52)**|
|School emphasis on academic success|-0.26 (2.76)|2.93 (2.58)|-1.91 (1.83)|-0.75 (1.71)|
|School discipline|-3.91 (3.80)|-1.60 (3.74)|-1.84 (2.68)|-0.85 (2.51)|
|Teacheryears of experience|1.76(0.60)**|2.23(0.59)***|-1.51(0.60)*|-0.95(0.59)|

22