rafmacalaba/datause-encoder-data huggingface dataset:
**Annex 4. Replications of Malaysia-focused analyses about the complementarities between digital, cognitive, and socioemotional skills for the occupational skills profile, Cambodia, Thailand, and Vietnam**

# **Table A.4.1. Correlations between skills (replications of Table 7)**

_Pairwise correlations based on the number of skills counts of skills categories_

# _A. Occupational skills profile_

||Advanced|Digital<br>Intermediate|Basic|Thinking|Cognitive<br>Communication|Organization|Emotions|Socioemotional<br>Relationships|<br>Personal<br>growth|
|---|---|---|---|---|---|---|---|---|---|
|Digital<br>Advanced<br>Intermediate|1.00<br>**0.45*****|1.00||||||||
|Basic|0.05|**0.31*****|1.00|||||||
|Cognitive<br>Thinking|**0.55*****|**0.40*****|**0.39*****|1.00||||||
|Communication|**0.22****|0.11|**0.37*****|**0.65*****|1.00|||||
|Organization|0.14|**0.30*****|**0.44*****|**0.59*****|**0.60*****|1.00||||
|Socioemotional<br>Emotions|0.15|**0.28*****|**0.64*****|**0.46*****|**0.40*****|**0.44*****|1.00|||
|Relationships|0.06|-0.04|0.08|**0.37*****|**0.59*****|**0.49*****|**0.17***|1.00||
|Personalgrowth|0.07|**0.64*****|**0.18****|0.07|0.13|**0.35*****|**0.29*****|**0.16* **|1.00|
|Others|0.02|**0.15* **|**0.33*****|**0.60*****|**0.66*****|**0.56*****|**0.23*****|**0.33*****|0.09|

# _B. Cambodia_

||Advanced|Digital<br>Intermediate|Basic|Thinking|Cognitive<br>Communication|Organization|Emotions|Socioemotional<br>Relationships|Personal<br>growth|
|---|---|---|---|---|---|---|---|---|---|
|Digital<br>Advanced<br>Intermediate<br>Basic|1.00<br>**0.31*****<br>0.10|1.00<br>**0.35*****|1.00|||||||
|Cognitive<br>Thinking<br>Communication<br>Organization|**0.34*****<br>0.14<br>0.09|**0.27*****<br>0.07<br>**0.35*****|**0.64*****<br>**0.62*****<br>**0.54*****|1.00<br>**0.68*****<br>**0.53*****|1.00<br>**0.43*****|1.00||||
|Socioemotional<br>Emotions<br>Relationships|0.01<br>-0.09|**0.46*****<br>0.02|**0.48*****<br>**0.33*****|**0.33*****<br>**0.39*****|0.08<br>**0.49*****|**0.31*****<br>**0.16***|1.00<br>**0.45*****|1.00||
|Personal growth|-0.07|**0.15* **|**-0.30*****|**-0.27*****|**-0.25*****|0.06|-0.06|-0.04|1.00|
|Others|0.08|0.12|**0.58*****|**0.62*****|**0.82*****|**0.43*****|0.05|**0.29*****|-0.13|

50
_The third and last duty of the sovereign [after the public institutions and public works necessary for the defence of the society, and for the administration of justice] is that of erecting and maintaining those public institutions and those public works, which, though they may be in the highest degree advantageous to a great society, are, however, of such a nature that the profit could never repay the expense to any individual or small number of individuals, and which it therefore cannot be expected that any individual or small number of individuals should erect or maintain._ Adam Smith, _The Wealth of Nations_ (Book V, Chapter 1, Paragraphs 69 and 70).

# **I.  Introduction**

Much of the current international debate on ways to spur growth, reduce poverty, and improve the quality of human life in low-income developing countries has centered on the need to promote a large increase in public investment. Reports by the United Nations Millennium Project (2005), the Blair Commission (2005), and the World Bank (2005 _a_ , 2005 _b_ ), have indeed dwelt on the importance of a “Big Push” in public investment in core infrastructure, financed by generous debt relief and a substantial increase in aid.

A common argument for a large increase in public spending on infrastructure is that infrastructure services may have a strong growth-promoting effect through their impact on the productivity of private inputs and the rate of return on capital— particularly when, to begin with, stocks of infrastructure assets are relatively low<sup>1</sup> . In that regard, low-income countries are at a particular disadvantage. In Sub-Saharan Africa for instance, only 16 percent of roads are paved, and less than one in five Africans has access to electricity. The average waiting time for a fixed telephone connection is three and a half years. Transport costs are the highest of any region. A 1999 study by the African Development Bank on exports of the region to the United States found that freight charges, as a proportion of cif value, are on average 20 percent higher for exports of poor countries of the region than for comparable products from other low-income countries. Given that prospects for public-private partnerships (PPPs) in infrastructure investment for the region, and low-income

> 1Infrastructure in this paper is broadly defined to include transport, water supply and sanitation, information and communication technology (ICT), and energy.

3
# **2.3 Data Set Construction**

Our data on country accession to the Montreal Protocol and its amendments, including dates of accession, are taken from the United Nations Environment Programme (UNEP) website.<sup>7</sup> In addition, data on ODS consumption are taken from the United Nations Environment Programme (UNEP) database.<sup>8</sup> The data are self-reported at the country-year level in ozone-depleting tonnes. We target seven of the ten substances regulated by the Montreal Protocol reported by UNEP: CFCs, halons, other CFCs, CTC, TCA, HCFCs, and MB. We omit HBFC and BCM as data are only available for a handful of countries. Note that these data reflect net consumption levels—the data record negative values if net destruction of ODS exceed consumption levels.

For the last group of substances regulated by the Montreal Protocol, HFCs we use data on emissions from the Emissions Database for Global Atmospheric Research (EDGAR) (Crippa _et al._ , 2021).<sup>9</sup> Emissions are given at the yearly level in kilotons from 1990-2018. For our study, we focus on five HFCs that have been common substitutes for CFCs and HCFCs – 23, 32, 125, 134a, and 152. We convert emission kilotons to CO2 equivalent tons and aggregate the five HFCs together.

Since ODS consumption is bound by the phaseout schedules set by the Montreal Protocol, a country’s ODS consumption from UNEP needs to be normalized by the country’s baseline in order to determine the degree to which the country is in or out of compliance, as well as how trade agreements impact compliance. The Montreal Protocol does not specify yearly targets, only dates for key reduction targets. For example, for Article 5 countries, 2007 CFC consumption can be no more than 15% of base level, and 2010 consumption can be no more than 0% of base level. We construct yearly consumption targets for each country and substance, based on the phaseout schedules dictated by the Montreal Protocol. For each substance, for years in between specific target dates, we linearly interpolate consumption targets. For the example above, the 2008 target would be 10% of base level, and the 2009 target would be 5% of base level. We then take the difference between each country’s substance consumption and target value. If the value is positive, the country is consuming at a level

> `7` https://ozone.unep.org/all-ratifications.

> `8` https://ozone.unep.org/countries/data.

> `9` https://edgar.jrc.ec.europa.eu/datasetghg60;  also https://data.jrc.ec.europa.eu/dataset/97a67d67-c62e-4826b873-9d972c4f670b.

10
It is also important to notice that information disclosure is not enough to guarantee market discipline. Freixas and Laux (2012) argue that the latter requires that information e¤ectively reaches the market and is adequately processed and used. In their view, in the period that preceded the global ...nancial crisis, the lack of market discipline was not the consequence of insu¢ cient disclosure but of market participants’lack of incentives to process it.

With such distinctions clear in mind, we are now in a position to look at the recent literature on the e¤ects of government guarantees on bank risk taking behavior. Not surprising, this evidence is mixed, too. In Germany, Gropp et al. (2014) show that the unanticipated removal of saving banks’ government guarantees, because of a court decision, led to a reduction in credit risk; Dam and Koetter (2012), using political factors to identify the causal e¤ect of bank bailouts on risk taking, also ...nd economically signi...cant positive e¤ects. Conversely, looking at the bailout of distressed banks, Berger et al. (2016a) ...nd that capital support was conducive to a reduction both in lending and in risk taking.

There is also a large and growing literature trying to ascertain whether the Troubled Asset Relief Program (TARP) in the US was successful in reducing systemic risk by providing, among other things, an “upside” to banks, or whether it increased risk taking incentive because of the moral hazard it generated. Again, the evidence is mixed: Duchin and Sosyura (2014) show that bailedout banks tend to be riskier, even if such an increase in risk may not be perceived by regulators;<sup>12</sup> Schenk and Thornton (2016), instead, indicate that TARP bailouts are likely to increase risktaking in “weak”institutions (low charter values and high leverage) and to decrease it in “strong” institutions (high charter values and low leverage). Finally, using a di¤-in-di¤ strategy, Berger et al. (2016b) also show that TARP contributed to the reduction in systemic risk and that such a reduction has been stronger for larger, safer banks, in states with better economic situations.

The crisis also o¤ered natural experiments to look into the pricing behavior of bank investors. Data on banks’sovereign debt holdings disaggregated by bond issuer are not generally available. However, the EBA released detailed bank-level information when it announced the results of its stress tests. And evidence suggests that this had an impact on banks’CDS spreads. The table below (from Dell’Ariccia et al. 2016) shows that banks that were found to hold larger amounts of distress sovereign bonds on their balance sheets experienced a sharper increase in CDS spreads and drop in stock returns. This evidence is not consistent with the notion that investors price risk at

> 12 Because it occurs in the same asset classes and thus does not a¤ect the CAR.

23
**Table 7: Determinants of the incidence of sickness**

||**(1)**||**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|---|
|||**Adults**|||**Children**|
|Year dummy, 2002 = 1|-0.028||0.026|-0.044**|0.029|
||(1.54)||(1.30)|(2.21)|(1.32)|
|Total asset value (log) [α1]|-0.008***||-0.006***|-0.004**|-0.001|
||(3.96)<br>||(2.82)<br>|(2.03)<br>|(0.67)<br>|
|Assets*year [β1]|0.006**||0.003|0.007***|0.004|
||(2.56)||(1.32)|(2.71)|(1.26)|
|Cement floor|-0.010*||-0.012**|-0.008|-0.010*|
||(1.88)||(2.21)|(1.45)|(1.91)|
|Pit latrine|-0.010**||-0.009*|-0.009*|-0.008*|
||(2.09)||(1.90)|(1.94)|(1.78)|
|Age in years|0.004***||0.004***|-0.049***|-0.049***|
||(7.20)||(7.26)|(29.24)|(29.26)|
|Age squared|<br>-0.000||<br>-0.000|<br>0.189***|<br>0.188***|
||(0.29)||(0.35)|(16.01)|(15.97)|
|Female|0.003||0.003|0.003|0.002|
||(0.66)||(0.67)|(0.67)|(0.62)|
|Education|<br>-0.005***||<br>-0.005***|<br>0.001|<br>0.001|
||(10.60)||(10.72)|(1.62)|(1.41)|
|Orphan [α2]||||0.034***|0.016*|
|||||(5.19)|(1.85)|
|Orphan*year [β2]|||||0.038***|
||||||(2.99)|
|Other relative [α3]||||-0.025***|<br>-0.023***|
|||||(4.65)|(3.28)|
|Other relative*year [β3]|||||<br>-0.001|
||||||(0.12)|
|Household size|-0.011***||-0.011***|-0.007***|<br>-0.007***|
||(17.32)||(17.45)|(10.79)|(10.84)|
|Rural area [α4]|0.009||0.005|-0.001|-0.007|
||(1.23)||(0.77)|(0.16)|(0.90)|
|Rural*year [β4]|0.000||0.004|0.004|0.010|
||(0.04)||(0.44)|(0.40)|(1.05)|
|East|<br>0.092***||<br>0.123***|<br>0.108***|<br>0.150***|
||(16.81)||(16.03)|(19.65)|(19.46)|
|North|<br>-0.015**||<br>0.019**|<br>0.000|<br>0.046***|
||(2.42)||(2.15)|(0.00)|(5.01)|
|West|-0.007<br>||0.009<br>|-0.020***<br>|0.013*<br>|
||(1.32)||(1.23)|(3.69)|(1.73)|
|East*year|||<br>-0.056***||<br>-0.075***|
||||(5.65)||(7.66)|
|North*year|||-0.065***||-0.083***|
||||(5.61)||(7.20)|
|West*year|||<br>-0.033***||<br>-0.067***|
||||(3.14)||(6.49)|
|Observations|52,849||52,849|54,550|54,550|
|Pseudo R-squared|0.04||0.04|0.06|0.06|
|<br>Loglikelihood ratio|-29,231||-29,209|-30,480|-30,485|
|χ<sup>2</sup>forα1+β1= 0|0.40||1.19|2.11|0.96|
|<br>Prob value|0.523||0.276|0.147|0.326|
|χ<sup>2</sup>forα2+β2= 0|||||33.96|
|Prob value|||||0.000|
|χ<sup>2</sup>forα3+β3= 0|||||8.86|
|Prob value|||||0.003|
|χ<sup>2</sup>forα4+β4= 0|2.15||2.28|0.14|0.21|
|Prob value|0.143||0.131|0.705|0.644|

Absolute value of z statistics in parentheses *significant at 10%; ** significant at 5%; *** significant at 1% 25
- Toye, John and Carl Jackson, 1996, “Public Expenditure Policy and Poverty Reduction,” _IDS Bulletin_ , Vol. 27, No. 1: pp. 56-66.

- Walker, Mabel L., 1930, _Municipal Expenditures_ (Baltimore: Johns Hopkins Press), reprinted 1983.

- Warr, Peter, 2003, “Fiscal Policies and Poverty Incidence: The Case of Thailand,” _Asian Economic Journal,_ Vol. 17, No. 1: pp. 27-44.

- Williamson, Tim and Sudharshan Canagarajah, 2003, “Is there a place for Virtual Poverty Funds in Pro-Poor Public Spending Reform? Lessons from Uganda’s PAF,” _Development Policy Review_ , Vol. 21, No. 4: pp. 449-480.

- Wilkinson, Richard G., 1992, “Income distribution and life-expectancy,” _British Medical Journal_ , No. 304: pp. 165–68.

World Bank, 2003a, _African Development Indicators_ (Washington: The World Bank).

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26
(utilization conditional on a classification of the child’s symptoms as either “care required” or “care not (yet) required” according to IMCI symptom charts).

Our analysis here uses the baseline data along with data collected in the second follow-up survey in 2014. This data contains an abridged health calendar that focuses on diarrhea symptoms along with care received over the 7 days preceding the survey visit. In addition, we collected anthropometrics, preventive knowledge and behavior, self-reported receipt of treatment, and other indicators, with a focus on information relevant to malnutrition and diarrhea. Enumerators were unaware of the experiment or treatment arms.

The original treatment groups had been kept intact for a second year, and children in enrolled families in one of the treatment arms continued to receive the subsidy, CHW visits, or both, regardless of age. Moreover, any children born into these families were also enrolled into the same treatment. However, it is worth noting that Mali Health was aware of the findings from the 2013 data collection that the CHW program had relatively low impacts on acute care seeking (Sautmann et al. [2020]). While the interventions were ongoing, Mali Health continued to provide additional training to CHWs. This might affect comparability of the CHW effects in 2013 and 2014. Note also that between the two follow-up surveys Mali Health had received a gift that allowed them to supply all beneficiary households in need with treated mosquito nets (both in the three treatment groups and in other Action for Health households not part of the sample). For ethical reasons, it was decided not to withhold this benefit from any households in the treatment groups that did not currently own treated nets. This may affect the incidence of health concerns in the three treatment groups.

# **3.1 Balance and Attrition**

The primary unit of analysis is the child or the caretaker (typically the mother). We describe the evolution of the sample here in terms of the number of children in the different waves. At baseline, we enrolled 1,732 children in the data collection. In both follow-up survey rounds, some children could not be found. In both years, 5 children died; additionally, families had 10
12 be the **case** if **PSI** reduced cases where extreme price differences (say 100 percent or more) occurred, or if the variance in prLces about<sup>the mean were lowered. 13</sup> Table 4 summarizes information on the distribution of Madagascar's<sup>relative</sup> import prices during 1979-82 and 1984-88. Shown here are decile values for import price relatives from France, as well as for Germany, Italy and Japan combined. That is, the table entries corresponding to any given Di indicates that lO percent of all Madagascar's price relatives fell below the value shown while (100-10) percent were higher.<sup>1 4</sup> As Table 4 shows, PSI had little or no influence on the frequency<sup>of</sup> extreme relative price differences. In both the 1979-82 and 1984-88 periods, 10 percent of all French exports to Madagascar had unit prices of 150 percent or more above those paid by industrial countries--a pattern very similar to that 13A potential limitation of the approach employed in this paper is that all improvements in relative import prices are attributed to the adoption<sup>of PSI,</sup> when they in fact may be due to other factors which lowered incentives<sup>for false</sup> invoicing and capital flight. According to the World Bank (1989), Madagascar undertook several major reforms in the 1984-88 period that should<sup>have, on</sup> balance, lowered its relative import prices. In 1987 and 1988, a marketdetermined trade and foreign exchange regime was adopted that included the elimination of quantitative import restrictions and also simplified (reduced) tariffs. While economic growth sharply deteriorated between 1980 and 1982, financial stabilization and a limited expansion were achieved from 1983 through 1988. These developments should have improved Madagascar's "credit worthiness" and reduced finance and insurance costs for imports. Since the latter are reflected in exporter's f.o.b. unit values they should have reduced 1984-88 relative import prices. Evidence also suggests that the black market exchange rate dropped in 1984-88, a development that should have had a positive impact on import prices. See Pryor (1988, Table G-2, p. 17) for estimates of the black market premium.

> 14 As an example, Table 4 shows (see the entry corresponding to D8) 20 percent of French 1979-82 exports to Madagascar had unit values 116.9 percent or more higher than similar goods exported to industrial countries. In 198488, 20 percent of these shipments had unit values that were 112.3 percent higher. Table 4 indicates the other industrial countries' (Germany, Italy and Japan) distribution was even more skewed toward high relative unit values--in 1979-82 20 percent of Madagascar's import values exceeded those for industrial countries by 140.8 percent or more.
20 Table 3: Nominal assistance coefficients<sup>a</sup> for rice, wheat, maize, groundnuts and all farm products, key country groups, 1972 to 1976 (percent)

|**Rice**|**1972**|**1973**|**1974**|**1975**|**1976**|
|---|---|---|---|---|---|
|Asia (excl. Japan)|1.07|0.72|0.43|0.76|0.92|
|Africa|0.98|0.61|0.36|0.60|0.82|
|Latin America|1.00|1.04|0.84|0.90|0.98|
|All developing countries|1.07|0.73|0.45|0.76|0.92|
|High-income countries|2.65|1.84|1.24|1.72|2.33|
|**Wheat**||||||
|Asia (excl. Japan)|1.31|0.72|0.83|0.97|0.94|
|Africa|1.19|0.85|0.67|0.87|1.00|
|Latin America|0.89|0.59|0.57|0.93|1.02|
|All developing countries|1.24|0.71|0.75|0.95|0.96|
|High-income countries|1.05|0.88|0.88|0.97|0.93|
|**Maize**||||||
|Asia (excl. Japan)|1.52|1.17|1.04|0.82|1.03|
|Africa|1.03|0.90|0.69|0.83|0.92|
|Latin America|1.06|0.89|0.81|0.85|0.84|
|All developing countries|1.18|0.98|0.83|0.83|0.92|
|High-income countries|1.13|1.06|1.00|1.03|1.04|
|**Groundnut oil**||||||
|Asia (excl. Japan)|1.08|1.00|1.00|0.69|0.86|
|Africa|0.51|0.46|0.48|0.53|0.57|
|Latin America|na|na|na|na|na|
|All developing countries|0.92|0.88|0.78|0.63|0.75|
|High-income countries|na|na|na|na|na|
|**All farm products**||||||
|Asia (excl. Japan)|1.18|0.90|0.69|0.87|1.04|
|Africa|0.83|0.75|0.70|0.80|0.79|
|Latin America|0.73|0.69|0.58|0.65|0.85|
|All developing countries|1.01|0.83|0.67|0.82|0.96|
|High-income countries|1.32|1.19|1.14|1.25|1.36|

a Nominal assistance coefficient = 1 + NRA/100 Source: Authors’ compilation based on data in Anderson and Valenzuela (2008)
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25