rafmacalaba/datause-encoder-data huggingface dataset:
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non-representativeness of the treatment group. Addressing these biases is essential in any program impact evaluation.

Our results strongly suggest that a wide variety of potential program impacts be investigated. Training programs may have an impact on some labor market outcomes but not others. Focusing on one outcome versus others may make the difference between continuation or termination of the program. A broad approach is also potentially more revealing about the direct and indirect ways in which training programs affect target populations.

Although the Mexican retraining program has proven to be effective, several issues need to be considered before such programs are replicated on a large scale in other countries: (1) As the impact of the program depends on the characteristics of the unemployed, it appears essential to analyze the structure and characteristics of unemployment prior to implementing any training program.

(2) Results and cost-effectiveness of the program also appear to be sensitive to other factors such as the length of the program, and the overall state of the labor market. In the case of Mexico, the program may have been helped by the fact that unemployment remained moderate and employment fairly stable during the program implementation period.

(3) For the above reasons, implementation of these types of training programs should be gradual, and accompanied by a strong monitoring and evaluation system.

Finally, it is worth noting that this evaluation still has several important limitations. One, our control sample is quite small. Two, our unemployment duration data for controls had to be imputed by reconstructing complete spells from observations on the same individuals in successive quarters. Three, some important dimensions of PROBECAT--training duration, type of training, mix of theory and practice, and different training providers--were not studied. Future evaluations should attempt to address these shortcomings. In particular, they should investigate the differential impacts of alternative training modalities, and evaluate their relative cost-effectiveness. It could also be of interest to track PROBECAT trainees over longer periods of time to measure the long-term effects of the program.
together with banks, perform these functions.<sup>28</sup> Beck, Demirguc-Kunt and Levine (2000) construct a large cross-country panel database on the mixture of banks and financial markets across 150 countries.<sup>29</sup> Demirguc-Kunt and Levine (2001) construct measures of the comparative size and activity of stock markets and banks such as the ratio of Stock Market Liquidity (defined as the value of stock transactions as a share of national output) to Bank Credit (defined by the claims of the banking sector on the private sector as a share of GDP) and ratio of Market Capitalization (measured by the value of listed shares divided by GDP) to Bank Credit. An alternative measure focuses on the restrictions on bank activities with the idea that in financial systems where banks face fewer restrictions, they are potentially more powerful (e.g. Barth et al. 2001; Beck and Levine, 2002). Others have examined the percentage of assets of the ten largest banks in each country owned by the government as a measure of the extent to which the financial system is bank based (e.g. La Porta et al. 2002).

While the classification distinguishing between bank-based and market-based countries can vary, Beck and Levine (2002) show that these measures are highly correlated. Thus, bankbased financial systems also tend to be dominated by state-owned banks (e.g. Bangladesh, Pakistan, Costa Rica) and countries with no state ownership in the ten largest banks (e.g. Canada, Japan, U.K., U.S.) tend to be market based financial systems. Further, countries whose banking systems are dominated by state-owned banks also tend to impose more restrictions on their banks (e.g. Egypt, Bangladesh, and Israel). Demirguc-Kunt and Levine (2001) show that while both types of financial systems are more developed in richer countries, financial systems tend to become more market based as countries become richer. Further, countries with better institutions (e.g. a Common Law tradition, strong protection of shareholder rights, good accounting regulations, low levels of corruption) tend to be more market-based as shown in Demirguc-Kunt and Levine (2001) and Ergungor (2004).

# **_6.2 Banks versus Market-based Systems – Theory and Empirical Evidence_**

<mark>The theoretical debate on the advantages of markets versus banks derives from the advantages (dis) of each in performing specific financial functions. First, financial intermediaries reduce the cost of acquiring and processing information and thus improve resource allocation, which in their</mark>

> 28 See Gerschenkron, 1962; Goldsmith, 1969; Hoshi, Kashyap, and Scharfstein, 1990; and Allen and Gale, 1995

> 29 The latest update of this database is November 2010 as of writing this paper.

52
_Excluded-Fight_ is coded 1 if there is one or more excluded parties that in a given year engages in conflict with the government, and if not, this variable is coded 0.

The control variables in this study include such factors which previous research have argued to influence peace duration, and theoretically may affect the independent variables as well as the dependent variables. The control variables are basically the same for both levels of analysis, and where there is a difference this is pointed out. To begin with, it is essential to control for the number of parties in each conflict as this potentially can affect the results. The variable _Number of Parties_ measures the number of warring parties that has been active in the conflict from 1989 and onwards, up until the year in question.

Although the findings are contradictory, previous research has indicated that the conflict duration increases the duration of peace, while the intensity of the conflict has been suggested to decrease peace duration (e.g. Doyle & Sambanis, 2000: 787; Fortna, 2004; Hartzell, Hoddie & Rothchild, 2001: 202). In the dataset at the settlement level, the variable _Conflict Duration_ counts the number of years since the conflict first reached the threshold of 25 battle-related deaths (which may be before 1989), up to a given year. In the dyadic dataset, I control for _Dyad-Conflict Duration_ , which is measured in the same way but starts counting from the year when a particular dyad first reached this threshold. Furthermore, the variable _Conflict Intensity_ is used at the settlement level and is coded 1 if the conflict (either in the year of observation or previously) reaches the level of war, meaning more than 1000 battle-related deaths in a year, and is otherwise coded 0. In the dataset at the dyadic level, I include the variable _Dyad-Conflict Intensity_ which is coded in the same fashion as conflict intensity, but this variable instead captures whether a particular dyad has reached the level of war or not. Moreover, whether the conflict concerns a certain territory or power over the government can possibly affect the prospects for peace (e.g. Wallensteen, 2002; Zartman, 1995). Thus, I created a control variable _Conflict Issue_ which is coded 1 if the incompatibility is fought over government and 0 if it is fought over territory (UCDP, 2006).

Peacekeeping forces have been found to play a key role in the post-settlement phase by increasing the duration of peace (Fortna, 2003). In addition, it has been proposed that UN peacekeepers may be more ‘neutral’ than regional peacekeepers (Zahar, 2003: 117). Hence, I control for _UN Peacekeeping_ , which is coded 1 if a peacekeeping operation led by the United Nations was present in a given year, 0 13
The follow-up firm survey also provides information about the total workforce in firms (including apprentices and other types of employees). Results are presented in appendix Table A8. While there is a significant impact on the inflow of youth that entered firms since the beginning of the experiment, there is no significant impact on the overall number of apprentices in firms at follow-up. The estimated impact is 0.464 with a standard error of 0.362. The impact on flows are not large enough to affect stocks significantly, which may be due in part to large standard errors in the stock variables.<sup>31</sup> Consistent with the lack of significant impact on the total number of apprentices in firms, the program does not have an impact on the overall workforce in firms. No significant impact is found on the number of interns or occasional workers either.

Table A8 shows that the program reduced the share of firms without any apprentice. Some firms that opened apprenticeship positions had no apprentice. In the control group, 20.3% of firms have no apprentice at the time of the follow-up survey. This proportion is significantly lower by 5.4 percentage points in the treatment group, but there are still 14.9 percent of firms without apprentice despite these firms having offered positions that were selected to be filled by the program.

In terms of measurement, the study shows the importance of carefully defining outcome measures. If we had implemented a simple survey only asking about the number of employees and apprentices in firms, we would have concluded that there was no significant impact on employment. However, this stock measure would have missed important impacts of the program. Indeed, the impact on stock is the combination of impact on inflows and outflows (see footnote 31). In addition, the flows between the start of the experiment and follow-up combine both entry and dropouts. With an employer-employee survey, data on the dates of entry and exit for each apprentice enable us to build a more precise set of measures of both entry and exit, providing a richer understanding of indirect impacts on firms.

protocol. The average number of formal apprentices who entered since the start of the experiment per control group firm is 0.188. As can also be seen from the table, this imperfect compliance did not last long: most (0.13) formal apprentices in control firms had left by the time of the follow-up survey. The non-compliance was highly concentrated in two out of the seven localities in which the program was implemented.

> 31For completeness, the impact of 0.464 on the stock of apprentice combines the previous impact of 0.613 on entry by follow-up with the impact on total number of apprentices who were in the firm before the randomization and are still in the firm at the moment of the survey, -0.154 (with a standard error of 0.239).

21
through easy comparison with cause-specific proportionate mortality rates of the most common causes of disease within a particular country.

Why compare with pre-pandemic mortality patterns? The expression of mortality in relative terms speaks to the fact that countries may have adapted to their specific patterns of mortality. Deviations from this pattern may create pressure points, such as for example on the health system. Comparisons with previous patterns give a country-specific flavor of the severity of the COVID19 pandemic, which could be used to corroborate realities on the ground. This relative expression complements other approaches – it does not substitute for them. A key advantage, particularly compared to the excess death ratio, is that the relative severity ratio is more readily available in countries where high-frequency data on mortality are not easily obtained in an accurate manner.

As for the COVID-19 data, we rely on the numbers reported daily by the Coronavirus Resource Center at Johns Hopkins University. We treat the reported data as it is presented. This sets the stage for the discussion in the next sections, which will touch upon data quality and other factors. It should be noted here, however, that the indicator of relative severity can be adjusted to take into account data quality issues. For example, if the estimate of COVID-19 deaths in the numerator were refined on the basis of excess death calculations, one would obtain a close relative of the excess death ratio.

The data on past mortality patterns is based on the WHO’s 2016 Global Health Estimates, which give us comprehensive information about all-cause mortality as well as the distribution of mortality across specific causes of death (WHO 2018a; 2018b; 2020a). We calculate the top causes of death for each country from the 123 disease families that can be derived from the latest International Classification of Diseases (ICD-10; WHO, 2016a). The main attraction of this data set is that it provides globally comprehensive and comparable data: we have data for 160 countries.

# **Comparisons across Countries**

Figure 2 puts a dimension on the unequal distribution of the pandemic’s impact through the lens of country-specific mortality profiles. The cumulative relative severity ratio is displayed as the hollow dot, whereas the proportionate mortality rates of the country-specific top three causes of death are shown by the stacked bars.

The results confirm the highly unequal distribution of the mortality burden. Here are some key take-aways:

- Countries that exceed the first cause of death:

- Belgium and Spain: highest severity of the death toll in the world, exceeding their first, second and third causes of death in the base year.

- United Kingdom, Ireland, Ecuador + Belgium and Spain: exceeding their first and second most common causes of death.

- Sweden, Italy, France, Netherlands + United Kingdom, Ireland, Ecuador, Belgium and Spain: exceeding their first cause of death.

7
and electricity consumption. We find that electricity consumption increased by 0.95 percent for each percent additional economic activity. For India, electricity increased slightly above 1 percent. Similarly, data on nighttime lights has also shown to be able to track economic activity. Henderson et al. (2012) develop a statistical framework to use satellite data on night lights to augment official income growth measures. They show that for countries with poor national income accounts, the optimal estimate of growth is a composite with roughly equal weights on conventionally measured growth and growth predicted from lights.

In this section, we explore the usefulness of electricity consumption and satellite night light as proxies for economic activity in India. To do so, we estimate the following quarterly (t) model: where the log of GVA is regressed on the log of electricity consumption and the log of nighttime light intensity. In addition, we include a trend and quarter fixed effects, _qt_ , to control of the intra-year seasonality of the data. We aggregate the electricity consumption and nighttime light data to quarterly frequency to match the frequency of the GVA and estimate the model for the period from the second quarter of 2013 to the first quarter of 2020.<sup>12</sup> Table 1: GVA, electricity and nighttime lights

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||log GVA|log GVA|log GVA|log GVA|log GVA|log GVA|log GVA|log electricity|log electricity|
|log electricity|1.296<sup>_∗∗∗_</sup>|0.350<sup>_∗∗∗_</sup>|||0.318<sup>_∗∗∗_</sup>|||||
||(0.061)|(0.078)|||(0.104)|||||
|log night light|||1.537<sup>_∗∗∗_</sup>|0.179<sup>_∗∗_</sup>|0.035|||1.205<sup>_∗∗∗_</sup>|0.451<sup>_∗∗∗_</sup>|
||||(0.175)|(0.066)|(0.073)|||(0.112)|(0.115)|
|log electricity (demean)||||||0.191<sup>_∗∗∗_</sup><br>(0.067)||||
|log night light (demean)|||||||0.149<sup>_∗∗∗_</sup><br>(0.031)|||
|trend||0.012<sup>_∗∗∗_</sup>||0.015<sup>_∗∗∗_</sup>|0.012<sup>_∗∗∗_</sup>||||0.008<sup>_∗∗∗_</sup>|
|||(0.001)||(0.001)|(0.001)||||(0.001)|
|constant|-6.047<sup>_∗∗∗_</sup>|5.664<sup>_∗∗∗_</sup>|8.999<sup>_∗∗∗_</sup>|9.874<sup>_∗∗∗_</sup>|6.038<sup>_∗∗∗_</sup>|0.000|0.000|11.597<sup>_∗∗∗_</sup>|12.083<sup>_∗∗∗_</sup>|
||(0.763)|(0.969)|(0.142)|(0.046)|(1.253)|(0.001)|(0.001)|(0.090)|(0.080)|
|Quarter FEs|YES|YES|YES|YES|YES|NO|NO|YES|YES|
|N|28|28|28|28|28|28|28|28|28|
|r2|0.953|0.994|0.775|0.992|0.994|0.238|0.477|0.846|0.958|

Standard errors in parentheses _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01

> 12The beginning of the sample coincides with the availability of the electricity daily data.

9
Appendix 8: Linear probability model with dependent variable identified as Lavigne (2011) Table 48

|Dependent variable: N|eed of fisca|l adjustme|nt based o|n Lavigne(2|011)||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|0.029***|0.032***|0.041***|0.043***|0.041***|0.037***|0.031***|0.040***|
|(% of GDP)|(0.006)|(0.006)|(0.005)|(0.006)|(0.009)|(0.011)|(0.011)|(0.009)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.217||-0.136|-0.152|-0.140|-0.111|-0.144|
|||(0.231)||(0.266)|(0.261)|(0.267)|(0.262)|(0.263)|
|Inflation (t-1)||0.358**||0.601***|0.606***|0.604***|0.581***|0.604***|
|||(0.146)||(0.130)|(0.129)|(0.129)|(0.128)|(0.130)|
|U.S. Interest rate||0.009||-0.002|-0.002|-0.002|-0.003|-0.002|
|||(0.011)||(0.013)|(0.012)|(0.012)|(0.012)|(0.013)|
|**Political factors**|||||||||
|Gini|||-0.007***|-0.006**|-0.006**|-0.009*|-0.006**|-0.006**|
||||(0.003)|(0.003)|(0.003)|(0.005)|(0.003)|(0.003)|
|Rule of law|||0.025|0.030|0.031|0.031|-0.040|0.030|
||||(0.040)|(0.041)|(0.041)|(0.041)|(0.066)|(0.041)|
|Margin of Majority|||0.015|0.033|0.034|0.037|0.034|-0.041|
||||(0.106)|(0.111)|(0.111)|(0.111)|(0.112)|(0.212)|
|Election's year|||-0.003|-0.001|-0.001|-0.001|-0.001|-0.000|
||||(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||0.003<br>(0.011)||||
|Gini * Rigidity||||||0.000|||
|||||||(0.000)|||
|Rule of law * Rigidity|||||||0.003<br>(0.003)||
|Majority * Rigidity||||||||0.004<br>(0.011)|
|Constant|-0.384***|-0.495***|-0.542**|-0.651***|-0.648***|-0.553**|-0.418|-0.609**|
||(0.129)|(0.144)|(0.225)|(0.231)|(0.233)|(0.278)|(0.267)|(0.249)|
|Observations|2,474|2,462|1,824|1,819|1,819|1,819|1,819|1,819|
|R-squared|0.076|0.086|0.129|0.143|0.143|0.143|0.146|0.143|
|Number of ifscode|127|127|97|97|97|97|97|97|
|CountryFE<br>Robust standard error<br>*** p<0.01, ** p<0.05|Yes<br>s clustered<br>, * p<0.1|Yes<br>by country|Yes<br>in parenthe|Yes<br>ses|Yes|Yes|Yes|Yes|

- 77 -
ASEAN (200 lb). Statistics of Foreign Direct Investment in ASEAN: Enhanced Data Set. 2001 Ed. Jakarta: ASEAN Secretariat, 2001.

Asian Policy Forum/Asian Development Bank Institute. Policy Recommendations for Preventing Another Capital Account Crisis. Tokyo: ADB Institute (Forum Secretariat), 7 July 2000.

Asian Policy Forum/Asian Development Bank Institute. Designing New and Balanced Financial Market Structures in Post-Crisis Asia. Tokyo: Asian Development Bank Institute (Forum Secretariat), October 2001.

Austria, Myrna S. "APEC's Commitments on Investment." Assessing APEC's Progress: Trade. Ecotech & Institutions. Eds. R. E. Feinberg and Ye Zhao. Singapore: Institute of Southeast Asian Studies, 2001. 59-70.

Balasubramanyam, V. N. "Foreign Direct Investment In Developing Countries: Determinants And Impact." New Horizons for Foreign Direct Investment. OECD Global Forum on International Investment. Paris: OECD, 2002. 187-208.

Bank for International Settlements. "The Banking Industry in the Emerging Market Economies: Competition, Consolidation and Systemic Stability." BIS Papers No. 4. Basel, Switzerland: BIS, August 2001 <http://www.bis.org>.

Bayoumi, Tamim, Barry Eichengreen, and Paolo Mauro. "On Regional Monetary Arrangements for ASEAN." Journal of the Japanese and International Economies 14 (2000): 121-148.

Beck, Thorsten, **Asli** Demirgu9-Kunt, and Ross Levine. "The Financial Structure Database." Financial Structures and Economic Development. Eds. A. Demirgii9-Kunt and R. Levine. Cambridge, Massachusetts: MIT Press, 2001. 17-80.

Beck, Thorsten, **Asli** Demirgiiu-Kunt, Ross Levine, and Vojislav Maksimovic. "Financial Structure and Economic Development: Firm, Industry, and Country Evidence." Financial Structures and Economic Development. Eds. A. Demirgii9-Kunt and R. Levine. Cambridge, Massachusetts: MIT Press, 2001. 189-241.

Beck, Thorsten, Ross Levine, and Norman Loayza. "Finance and the sources of growth." Journal of Financial Economics 58.1 (2000): 261-300.

Bekaert, Geert, Campbell R. Harvey, and Christian Lundblad. "Does Financial Liberalization Spur Growth?" NBER Working Paper 8245. Cambridge, MA: National Bureau of Economic Research, 2001.

Benassy-Quere, Agnes. "Optimal Pegs for East Asian Currencies." Journal of the Japanese and International Economies. 13 (March 1999): 44-60.

132
and confirms that the literature suffers from publication selection bias. Thirteen variables present a PIP higher than 0.5, indicating that they are relevant for explaining the differences in the estimates. We focus on the variables for which we have the most robust evidence across the two specifications: at least a strong PIP in BMA, and a significance level of at least 10% in OLS. As could be expected due to the meta-sample’s heterogeneity, studies testing theoretically detrimental mechanism or introducing interactions with a risk enhancer find a higher local risk of conflict subsequent to income shocks. Studies controlling for GDP find a higher risk of conflict and, conversely, controlling for the size of the population, using a quasi-experimental framework or studying small grid cells is associated with a smaller risk of conflict.

Table 6 reports the results of the BMA and OLS for the sub-samples of AS+ (808 regressions), AS- (951 regressions), and HS+ (543 regressions) studies. The results we obtain for publication bias after controlling for our set of moderators confirms the presence of a substantial positive publication bias in the literature focusing on AS-, and the absence of evidence for such biases in the literature on AS+. However, Table 6 shows a negative and statistically significant coefficient of publication bias for HS+, which differs from the statistically non-significant constant found in Table 2. This indicates that HS+ studies not published recently in a top 5 journal suffer from a substantial negative publication selection bias when they focus on a small sample and countries from different regions, do not control spatially for conflicts or for specific effects, and do not study armed conflicts and price shocks. We find that AS+ studies specifically testing for stressful mechanisms<sup>25</sup> or introducing interactions with a risk enhancer find a higher risk of conflict. They also find a higher risk of conflict when they go through a stricter publishing process or when they examine the post-Cold War period. Studies focusing on AS+ in Latin American and African countries find a generally lower risk of conflict. Studies encompassing negative agricultural shocks (AS-) approaching income shocks through climatic events or, to a lesser extent, price variations find a higher risk of conflict. They also find a stronger association between income shocks and local criminality (e.g., robberies, property trespassing, assaults). Finally, studies published in journals specialized in conflict analysis, controlling for the size of the population, or focusing on Latin American countries find a smaller risk of conflict. Studies focusing on positive hydrocarbon/mineral shocks (HS+) find a lower risk of conflict when they encompass larger samples or test for price shocks.

> 25A few studies of AS+ test agitating mechanisms through rapacity effects.

31
|co<br>Loss ratio|result as [%] ofthe<br>reinsurance premium|;<br>Jat|
|---|---|---|
|2%|43%||
|46%|4%||
|AT%|3%||
|48%|3%||
|87%|3%||
|88%|2%||
|89%|1%||
|90%|0%||
|91%|-0.8%||
|92%|-1.6%||
|120%<br>200%|-24%<br>-JA%||

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Chosen LoB's development | YR1 YR2 YR3 YR4 YR5 YR6 YR7 YR8 pattern pomees [er] ms | | | mo me [ms [ms | we we we [ee [ee | Motor vehicle liability vehicle liability liability | NO | 100% | 0% | 0% | 0% | 0% | 0% | 0% | 0% | [| Defauttpattem || 40% | 30% | 20% | 5% | 2% | 1% | 1% | 1% | |