rafmacalaba/data-use-mentions-extended huggingface dataset:
_Popular_ (CINEP) _Noche y Niebla_ online database of human rights and political violence events, following the criteria and definition of the CNMH. The data exclude attacks perpetrated by criminal gangs or by the state’s armed forces, but nonetheless, these attacks in Colombia since the mid-1990s have been almost exclusively a strategy used by guerrilla groups.

The variation across time and space of violent attacks against civilians in the country is depicted in Figures 1 and 2. In the first, darker shades indicate a higher number of attacks through the period of analysis (1999-2016). Except for the south-east of the country, the most remote and low-density populated region,<sup>25</sup> the occurrence of attacks is scattered around the country. The map also shows their rarity, as most municipalities did not experience any attack in 16 years. Figure 2 shows the evolution of attacks by month of occurrence. There are more occurrences in the mid-2000s and less towards the end of that decade, coinciding with the pattern of conflict intensity in the country.

In the municipalities included in DHS (487 across the three survey rounds), there were 49 attacks in the period of study occurring in 29 different municipalities; that is, approximately 6% of the municipalities in the sample experienced at least one attack between February 1999 and March 2016. The recurrence of attacks in a municipality is low: ten of them experienced more than one attack in the period, and Tumaco, in the Pacific south-west, and Bogotá, the capital, had the highest occurrence with six and five attacks respectively. Not all events resulted in victims; in 23 attacks there were no fatalities and in 17 there were no wounded people. This reinforces the idea that the fear of potential devastation can be larger than the immediate effect on human lives.

# **B. Pregnancies and births data**

Violence data by municipality and month of occurrence are matched to household survey data from the Colombian DHS on the month of pregnancy (then aggregated and reported by
the mean number fell from 3.25 to 0.75 annually. Taking the individual differences to rid the data of any village fixed effects, the corresponding value of _|t|_ is 5.00: with 7 d.f., this is an overwhelming rejection of the null. This quantitative finding stands in stark contrast to that yielded by the household data, and it lends support to the suspicion that the failure to find any effect in the latter can be put down to sampling – fluctuation with just 8 households per village.

# **6 Ranking the Benefits in the Three Domains**

Both the household survey and the focus group interviews provide strong support for the contention that the benefits in the domains of education and health are likely to 32
common currency, dollarization, or a currency board) does not appear to be realistic in many emerging market economies, including East Asia, because these economies have strong preferences towards exchange rate stability, though not necessarily rigidity. Given East Asia's diversified trade and FDI relationships with the United States, Japan, and the European Union and given the continued large exchange-rate volatility among the tripolar currencies, a reasonable exchange rate policy for many East Asian economies would be to stabilize rates to a basket of currencies consisting of the US dollar, the Japanese yen, and the euro. This paper proposes that the East Asian economies should achieve real effective exchange rate stability by loosely tying their rates to currency baskets during normnal times, while allowing enough room for flexibility during a crisis.

The organization of the paper is as follows. Section II summarizes the nature of "reported" and "observed" exchange rate arrangements for developing economies. By econometrically identifying major currencies and their weights in a currency basket for almost all developing countries, this section demonstrates that many authorities in these economies exhibit a preference to stabilize their exchange rates vis-a-vis an international currency or a basket of such currencies. Using further regression analyses, the observed weights in a currency basket are explained by the country's share of trade with the relevant anchor countries or the currency areas formed by such anchor countries. Section III briefly describes the process of the East Asian currency crisis and contagion. It then empirically analyzes the importance of the US dollar, the Japanese yen and the euro as an international anchor currency for the exchange rate behavior of 12 East Asian economies before, during, and after the currency crisis using daily exchange rate data. It examines the changing roles of the tri-polar currencies in the 1990s and the changing 2
multiplier|0.70*|0.80*|0.82*|0.91*|1.08*|1.09*(24)|

Notes: This table reports spending multiplier and responses of government spending and net tax revenue to a government spending shock over the two sub-periods. The multiplier is the median, and * denotes that 0 does not belong to the confidence region. The last column reports the maximum multiplier and numbers in brackets denote the quarters in which the maximum is observed.

## **3.3.2 Sub-period tax revenue multiplier**

The impact of a net tax revenue shock on GDP over the two sub-periods is reported in Table 5. In response to an increase in net tax revenue of $1, GDP declines by $0.38 upon impact during the first sub-period and by $0.21 during the second sub-period. The peak response in the second sub-period is equal to -$0.51, while in the first sub-period, it is equal to the response upon impact.

For the cumulative multiplier, the same patterns are observed. The decline in GDP is larger in the first sub-period than in the second sub-period. The response of net tax revenue may explain this different impact on GDP. After the shock, net tax revenue declines steadily in the second sub-period, while the decline in the first sub-period tends to be faster.

# **3.4 Comparison of the results with existing findings**

To my knowledge, most of the papers examining empirical evidence of the impact of fiscal policy shocks in Canada do not explicitly report numerical results. Rather, they report the IRFs on graphs, making any quantitative comparison difficult. Therefore, the quantitative comparison is limited to theOwyang et al. (2013) study.

## **3.4.1 Comparison with Owyang et al. (2013)**

Table 6 reports the cumulative multiplier associated with spending increases in both studies. Owyang et al. (
>

- <mark>Frankenberg, Elizabeth, and Duncan Thomas. "The Indonesia Family Life Survey (IFLS): Study design and results from waves 1 and 2." (2000).</mark>

- <mark>Frankenberg, Elizabeth, Lynn A. Karoly, Paul Gertler, Sulistinah Achmad, I. G. Agung, Sri Harijati Hatmadji, and Paramita Sudharto. "The 1993 Indonesian Family Life Survey: overview and field report." (1995).</mark>

- <mark>García, Jorge Luis, James J. Heckman, Duncan Ermini Leaf, and María José Prados.</mark> _<mark>The lifecycle benefits of an influential early childhood program</mark>_ <mark>. No. w22993. National Bureau of Economic Research, 2016.</mark>

31
To study _harmful food coping_ at the household level, we measure the extent of adverse food-related coping behaviors via the Reduced Coping Strategies Index (rCSI) (Maxwell and Caldwell, 2008). The rCSI is based on five different coping behaviors at the household level, and reflects both the frequency of each behavior (on how many of the last 7 days it occurred) and its severity (how strongly it threatens a household’s well-being). The specific survey questions are: _Over the last 7 days, how many days did your household ..._

- _a) ... rely on less preferred and less expensive food? (severity weight (sw) = 1)_

- _b) ... borrow food or money for food from friends or relatives? (sw = 2)_

- _c) ... limit portion size at mealtimes for all household members? (sw = 1)_

- _d) ... restrict consumption by adults in order for small children to eat? (sw = 3) e) ... reduce the number of meals eaten in a day for all household members? (sw = 1)_

For each item, the number of days is multiplied with severity weight. In turn, the rCSI score is the sum of these five values. Therefore, the maximum raw score for the rCSI is 56, and households with scores above 10 can be considered “severely food insecure” (Vaitla, Coates and Maxwell, 2015). In the main analysis, we use a standardized rCSI, with zero mean and unit standard deviation.

Our main indicator of economic activity among adults equals one if an individual (age > 14) is engaged in at least one of the following five forms of work in the past 7 days: 1) paid labor for someone else; 2) unpaid labor in an off-farm family business; 3) non-agricultural self-employment; 4) agricultural self-employment; and 5) participation in unpaid training or workshops to foster
4|11.94|(0.07)|

_Panel B.1: Debt_

||N|Mean|Median|StDev|Min|Max|ˆ_τRD_|
|---|---|---|---|---|---|---|---|
|Total Debt, Pre-Program||||||||
|Log total debt, Rs|2,825|10.83|10.71|1.04|6.91|15.13||
|_Treated_|1,672|10.73|10.65|0.98|6.91|15.02|0.03|
|_Control_|1,152|10.99|10.82|1.11|6.91|15.13|(0.02)|
|Bank debt, %|2,732|86.71|100|28.35|0.00|100||
|_Treated_|1,600|85.9|100|29.32|0.00|100|-1.303|
|_Control_|1,132|87.86|100|26.9|0.00|100|(1.45)|
|Informal debt, %|2,732|8.44|0.00|22.27|0.00|100||
|_Treated_|1,599|9.09|0.00|23.69|0.00|100|0.325|
|_Control_|1,133|7.52|0.00|20.08|0.00|100|(1.18)|

_Panel B.2: Investment and Productivity_

||N|Mean|Median|StDev|Min|Max|ˆ_τRD_|
|---|---|--
# _Complementarities between digital and non-digital (cognitive, and socioemotional) skills_

Next, we investigate complementarities between digital, cognitive, and socioemotional skills in Malaysia’s jobs.<sup>30</sup> We first examine pairwise correlations. The pairwise correlations show the magnitude of relationships between two subsets of skills. We use Pearson correlations and test the significance of the correlation through t-test.

We then carry out a factor analysis using the skills count of the nine digital, cognitive, and socioemotional skills subsets (three each) detailed in section 2. The exploratory factor analysis allows us to model the relationships among skills subsets by reducing them to fewer variables thus highlighting stronger links between several skills subsets. We use a standard exploratory factor analysis that finds a number q of factors that linearly reconstruct our nine original variables according to the following relationship: where is the value of the _i_ th observation on the _j_ th variable, is the _i_ th observation on the _k_ th common factor, yyooii i is the set of linear coefficients called the factor loadings, and zzooii is similar to a residual but is known as the _j_ th variable’s unique factor (StataCorps 2019). We use varimax rotation ooii bb ee to produce orthogonal factors,<sup>31</sup> to get maximized factor loadings. Again, we use the SEAD data for Malaysia, weighted by the distribution of employment across occupations, to study those relationships within the existing job structure. Occupation-level results (unweighted by the distribution of occupations in a particular country) and replications for other countries are also estimated and presented in Annex 4.

# _Occupation digitalization and skills requirements in Cambodia, Malaysia, Thailand, and Vietnam_

Finally, we use descriptive statistics to calculate the levels of occupation digitalization and skills requirements across the four countries we study. We use the SEAD data set to understand the distribution of skills across the four countries of interest. We also generate occupation-weighted data sets over several years for Malaysia, Thailand, and Vietnam to understand how the
16

# Table 7: Ukraine

Unofficial Economy Share: three scenarios.

||1989|Uno<br>1299|fficial Ec<br>1991|onomy S<br>1992|har<br>1993|1994|
|---|---|---|---|---|---|---|
|Unitary Elasticity Scenario|12.0|16.2|25.8|34.7|39.7|48.5|
|Conservative Elasticity Scenario|12.0|16.3|25.6|33.8|38.0|45.7|
|Overall Conservative Scenario|12.0|14.9|23.5|31.6|35.9|44.3|

All three scenarios point to similar trends, orders of magnitude, and observations on the evolution of the unofficial economy in Ukraine. In particular:

1. The unofficial economy in Ukraine has virtually tripled in size, while the official economy is less than one-half the size it was in 1989.

2. The decline in the overall economy since 1989 has been by almost 30 percent. While significant, this is still less than the decline derived from official statistics. Clearly, the decline in the official economy in Ukraine has been mitigated by the rapid growth in the unofficial economy during the period.

3. By 1994 the estimated unofficial share in the overall Ukraine economy exceeded 40 percent of the overall economy. These figures are roughly consistent with the microsurvey estimates reported in the previous section.

4. Varying the initial 1989 baseline estimate for the unofficial economy by using lower (say, 8 percent of overall activity) or higher (for instance, 16 percent) figures would only change the estimated share of the unofficial economy by 1994 minimally--since the lion's share of unofficial economy growth took place after 1990.

While far from providing definitive validation to the macroelectric approach,
10

# **Efficiency:**

_Greater Inefficiency:_ In addition to equity problems, China is getting less incremental health improvement as a return on its expenditures than in the mid-1980s, thus creating efficiency problems. Once again, Table 2 shows how the maximum outcomes--percentage reductions in infant and maternal mortality rates--have been achieved with minimum percentage increments in public health expenditure. It is clear that the greater the percentage increase in public health spending the smaller the reduction in mortality rates in the poorest provinces (quintile I) than in the wealthiest provinces (quintile V) between 1982 and 1993. Figure 6 shows that most of the wealthy provinces are significant (negative) outliers, having lower infant mortality rates predicted for its government health spending. When regression is done with provincial data, the impact of government health spending on reducing mortality rates in 1982 was greater in poorer provinces than in richer provinces. Conversely, in 1993, a somewhat reduced effect of government health spending on health status was found in poorer provinces than in wealthy provinces (Technical Annex A).

_Relative Effects of Public Policies on Health Status._ Before eliciting future strategies in the final section, it would be useful to assess the effects of several public health interventions on the overall health status of Chinese population. For more than three decades, China has made remarkable stride in improving the health status of its population. Given the extent of this progress, it is, important to examine whether the improvement in health outcomes can be linked to specific components of public policy or, as is also possible, whether the achievement is due mainly to gains in overall income overtime. Thus, this section posits to what extent can particular government policies such as public expenditure on health, access to various public health services improve the health status of the population?

Regressions are used to assess the impacts of changing government policies and income levels on infant mortality rates and life expectancy at birth. The data used are a time-series of 198393 from cross-section of 29
government forces and has lasted for 17 years until a recent peace treaty in 2020. The war caused tens of thousands of civilian casualties, and millions were forced to flee their homes due to the conflict and general economic instability due to years of fighting that inflated commodity prices. Moreover, much of Sudan is covered by desert and arid land, posing further difficulties as most people rely on agriculture for their livelihoods. Overuse of the land, droughts and flashfloods have contributed to displacement as people escape food insecurity and natural disasters. As of December 2019, there were 2.4 million IDPs in Sudan (IDMC 2020), around 5.6% of the total population (World Bank 2020a). Sudan also has one of the largest refugee populations in Africa, with many people from surrounding countries such as Ethiopia and South Sudan, and even as far as the Syrian Arab Republic and the Republic of Yemen, seeking safety from violence and conflict. As of 2014, 12.2% of the population was living on less than $1.90 a day (World Bank 2020b) and 52.3% were MPI poor in the same year (UNDP and OPHI 2020). Widespread displacement and poverty following decades of conflict has also contributed to gender-based violence, particularly against the most vulnerable population groups such as IDP girls and women. According to the UN Women database, the prevalence of child marriage in Sudan is 34%, while 87% of the population are subject to female genital mutilation.<sup>12</sup>

# **3. Methodology**

## **3.1 Alkire-Foster method**

The Multidimensional Poverty Index (MPI) constructed for this analysis is based on the AlkireFoster (AF) method of multidimensional poverty measurement (Alkire and Foster 2011). The AF method allows for the construction of individual- and household-level deprivation profiles that can then be used to identify multidimensionally poor people. There are three key statistics for any MPI: incidence or headcount ratio (H
There have been many recent attempts to quantify these linkages between infrastructure and growth, poverty reduction, and achieving related development goals.<sup>ii</sup> Of 102 studies conducted over the past 15 years, few find that infrastructure investment has a negative effect on productivity or growth (table 1). The sample includes 30 studies of multiple countries (including developing countries), 41 studies on the United States, 19 on Spain, and 12 on individual developing countries (Argentina, Brazil, Colombia, India, and the Philippines).

**Table 1** Distribution of study findings on impact of infrastructure investment on productivity or growth

|Area studied|Number of studies|Percentage showing a<br>positive effect|Percentage showing no<br>significant effect|Percentage showing a<br>negative effect|
|---|---|---|---|---|
|Multiple countries|30|40|50|10|
|United States|41|41|54|5|
|Spain|19|74|26|0|
|Developing<br>countries|12|100|0|0|
|Total/average|102|53|42|5|

Source: de la Fuente and Estache 2004.

The studies of multiple countries and the United States offer mixed results: half or more find that infrastructure investment does not have a significant effect on productivity or growth, and some find that it has a negative effect. The results for the United States reflect the effects of higher infrastructure endowments in a mature economy. Findings of negative effects can often be explained by diminishing returns to some types of infrastructure, so-called “white elephants,” or by negative externalities. For example, a new transport project in one U.S. state may lead to an exodus of workers or industries from other states, slowing growth in those states. Similar conclusions emerge from the cross-country data.

5
# **Box 2: Household spending on education makes up a large proportion of total spending in lowincome and lower-middle-income countries**

Household spending on education tends to account for a greater percentage of GDP in low-income countries than wealthier countries (see figure below). Comparable information on household education spending at the country level is relatively scarce. However, using country averages over four-year periods, we found that, on average, household spending on education in 2010-13 accounted for 2.6 percent of GDP in low-income countries but only 0.7 percent in high-income countries.

It also appears that household spending makes up a larger share of total education spending in low-income countries. In 2010–13, we found that average government spending on education accounted for 3.7 percent of GDP in low-income countries and 4.8 percent in high-income countries. Combining the data on household spending with those on government spending, it becomes clear that households in low-income countries provided 41 percent of all education spending compared to only about 13 percent in high-income countries.

In terms of regions, households in Africa and Latin America contribute the most to education as a share of GDP. In the four-year period from 2010 to 2013, households in Africa spent the equivalent of 2.1 percent of GDP on education. When household and government spending are combined, African countries spent the equivalent of 6.4 percent of GDP on education and Latin American countries spent 6.8 percent.

Only about 40 countries have more than 10 data points on household education spending between 2000 and 2015 in the UIS database. In this limited sample of countries, average household spending appears to have fallen during the first half of the 2000s from 1.4 percent in 2000 to 0.8 percent in 2005, but then began to rise and in 2013 accounted for about 1.2 percent of GDP.

**Households Contribute the Largest Share of National Income to Education in Low-income Countries** _Household Education Spending as a Percentage of GDP, 2006_ – _13_

<!-
Lithuania, Hungary, and Georgia, and is just below 10 for Albania and Latvia. Thus, assuming that all the assets of Ukrainian banks are loans these banks would require more than 30 years of pre-crisis profits to cover the loan losses incurred during the crisis. By contrast banks in Estonia or Turkey could cover the increase in NPL during the crisis with 1 year of pre-crisis profits.

The analysis above suggests that banks in several transition countries are likely to struggle with the high NPL ratios they have accumulated during the crisis and the possible losses associated with such loans. One consequence might be a contraction in lending and the question that arises is whether firms or households would be more affected. To assess this, we take data on the share of private credit extended to firms and households from the EBRD transition report. On average across our sample of countries the share of private credit extended to firms is 58% (mean 2007-2009), ranging from 44% in Croatia to 76% in Slovenia. The share to households is thus on average 42% in our sample. We plot the severity of the NPL problem (change in NPL ratio / pre-crisis ROA) by country against the share of loans made to firms (Figure 4.3.b) or households (Figure 4.3.c) respectively.

Figures 4.3.b and 4.3.c show that the countries with the most severe NPL problems (Ukraine, Lithuania, Hungary, and Georgia) all have structures of lending that correspond to the sample average. In all of these countries the share of lending to firms is roughly 60%, while that to households is roughly 40%. This indicates that debt overhang in the banking sector of these countries is likely to affect both sectors, with firms slightly more affected than households.

# <u>Foreign Banks during the Crisis</u>

Political as well as financial integration with Western Europe including a major presence of multinational banking groups had been the predominant model of development in