rafmacalaba/datause-encoder-data huggingface dataset:
To define crises, we follow the approach adopted by Broner, Lorenzoni, and Schmukler (2004), which determines ex-ante certain criteria to identify the beginning and end of crises according to the behavior of certain market indicators. Their methodology allows us to distinguish country-specific crisis periods (which could be of domestic or foreign origin), without resorting to the use of ex-post data. We use two different procedures to identify crisis periods: one based on the exchange market pressure (EMP) index and one based on the local stock market index.<sup>23</sup> As crisis dating is arbitrary, we perform a robustness exercise.

The EMP index is computed as the weighted average of the daily changes in the interest rate and the log difference of the exchange rate, with weights equal to the reciprocal of the standard deviation of the respective variables.<sup>24</sup> A crisis initiates when the EMP volatility (its 15-day rolling standard deviation) exceeds a threshold level and remains above that level for at least four weeks, where the threshold is defined as the mean of the EMP volatility plus one standard deviation. A crisis ends the first date after which the EMP volatility drops below the threshold and remains there for three months.

When using stock market prices, crises are defined as follows. Stock market crises begin when the stock market index starts a decline of at least five consecutive weeks that reaches a cumulative drop in excess of 25%. A crisis ends on the first date after which the index grows for at least four consecutive weeks.

The exchange and interest rate series come from Bloomberg and Datastream and the local stock market index series come from the Emerging Market Database (EMDB).

> 23 To define crisis periods, Broner, Lorenzoni, and Schmukler (2004) use the 9-year bond spread, which is not readily available for all countries in our sample.

> 24 Ideally, one would also like to include the change in reserves; unfortunately, these data are not available on a daily frequency for the countries in our sample.
bolsters financial market confidence and underpins the continuation of benign financial conditions.

The baseline scenario projects a moderate expansion in global activity of 3.8 percent in 2021, following a 4.3 percent collapse in 2020 (Figure 4.A; World Bank 2021). Global growth is then envisioned to slow to 3.7 percent in 2022. Despite projected above-potential growth in 2022, output is expected to remain well below pre-pandemic trends at the end of the projection horizon. Following a sharp contraction of 9.5 percent in 2020, global trade is expected to experience a modest pickup to an average of 5 percent in 2021-22 (Figure 4.B). Growth in advanced economies is expected to strengthen gradually to 3.3 percent in 2021 and 3.5 percent in 2022 (Figure 4.C). Growth in EMDEs is expected to bounce back to 5.0 percent in 2021 from a 2.7 percent contraction in 2020, before slowing to 4.2 percent in 2022 (Figure 5.A). The modest rebound in EMDE growth would not be enough to restore debt sustainability in some EMDEs, with the gap between the debtstabilizing and the actual primary balance for EMDEs remaining negative through 2022 (Figure 5.B).

# Downside scenario

In the downside scenario, insufficient pandemic management and lax compliance with social distancing measures lead to notably higher levels of new cases in many countries in 2021, requiring longer-lasting and more stringent pandemic-control measures. Relative to the baseline scenario, vaccine deployment in advanced economies and major EMDEs is slowed by supply bottlenecks and the reluctance of a higher proportion of the population to receive vaccinations.<sup>11</sup> As in other scenarios, rollout in other EMDEs and LICs begins up to four quarters after rollout in advanced economies and major EMDEs owing to logistical issues. Caseloads decline only gradually through 2022, mostly due to sustained social distancing.

Economic activity remains depressed in this scenario, as households fear contact-intensive services, including recreation and tourism, and grapple with stringent social distancing measures. Firms—facing pandemic-control policies, a bleak outlook for consumer demand, and elevated uncertainty—curtail investment and hiring plans, keeping employment well below pre-COVID-19 levels. Financial conditions tighten markedly through 2021, as financial market sentiment continuously deteriorates in tandem with a string of unexpected vaccine delays and insufficient control of the pandemic, and as corporate and bank balance sheets deteriorate over prolonged demand weakness and forbearance requirements. While accommodative monetary policy keeps financial crises at bay, fiscal sustainability concerns limit the size of additional fiscal stimulus, leading to insufficient income support to the unemployed and struggling small and medium-sized firms.

> 11 Only about half of the population in advanced economies and major EMDEs is assumed to be amenable to vaccination, a level broadly consistent with the lower bound from global survey evidence (Lazarus et al. 2020).

11
## **Fig 6 Thailand, 2008 (attending public school, ages 7-18)**

<!-- Start of picture text --> Share of public expenditure on Share of public expenditure on<br>education by quintiles of consumption (2008) education by quintiles of probability (2008)<br>20 20 20 21 21 22 20 20 20 20<br>18 20 20 20 20 20 20 20 19 19<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Population (age 7 to 18) Public expenditure Population (age 7 to 18) Public expenditure<br>30 30<br>20 Percentage 20<br>10 10<br>0 0<br><!-- End of picture text --> Source: same as Figs 1-6

# **Appendix 3:  Estimating Educational Opportunities for Zambia, 2009 (Age 7-13)**

||Odds ratio|
|---|---|
|**Child:**||
|Female|1.05|
||(0.059)|
|**Household head:**||
|Female|1.24*|
||(0.108)|
|Age|1.02**|
||(0.003)|
|**Education level of household head**||
|Basic: 1st to 6th grade|1.24*|
||(0.131)|
|Basic: 7th grade|1.71**|
||(0.198)|
|Basic: 8th to 9th|2.5**|
||(0.324)|
|High School|2.89**|
||(0.421)|
|Tertiary|5.37**|
||(1.106)|
|**Lives with couple (household head)**|1.02|
||(0.37)|
|**Household:**||

39
rural patterns shows larger gaps but still high usage rates for particular functions even in rural areas. Rural residents are less likely than urban residents to make or receive calls on a daily basis (78 percent versus 88 percent). They are also less likely to use text messages on a daily basis (57 percent versus 71 percent) and to use mobile money each day (20 percent versus 28 percent).

The typical mobile money user does not use it every day. Half of all adults (68 percent of all mobile money users) use mobile money less than once per day. However, a substantial majority in all categories uses mobile money at least occasionally: 73 percent of all adults, including 70 percent of women, and 70 percent of rural residents.

# **3 Literature on Mobile Money**

The proliferation of mobile money in Kenya has generated research attempting to explain the roots of the phenomenon and to understand its effects. Kimenyi and Ndung'u (2009) attribute the rapid growth in mobile money in Kenya to four factors: a conducive legal and tax environment, private-public policy dialogue, strategic and prudent macroeconomic policies, and a guarantee of the existence of a contestable market discouraging dominance by initial entrants. Comninos et al. (2008) argue that the initial success of Kenya‘s mobile money transfer industry can be attributed to the high demand for remittances generated by rural/urban migration, while its rapid scaling is due to the mobile providers‘ growth strategy.

Jack and Suri (2011) report results of a 2009 survey of Kenyan households that use M-PESA. They find that M-PESA reached nearly 40 percent of the Kenyan adult population after only two years of operation. While M-PESA was initially adopted mostly by wealthier households, adoption by less wealthy households was also increasing. Jack and Suri also find an increase in the use of M-PESA by the unbanked population. However, their findings suggest that not owning a mobile phone is a major constraint to the adoption of M-PESA. They also find that M- PESA users with a bank account are much more likely to save on M-PESA than M-PESA users without a bank account. The majority of users cite ease of use and safety as the major reasons for saving on M-PESA.

4
(see Appendix 1 for details). Our basic specification adopts a quadratic polynomial on the initial shares to estimate _g_ ( _·_ ). For robustness, we also estimate a more general and flexible partially linear semi-parametric model as in Robinson (1988).

To reveal different AD effects at different levels of exposure, we evaluate the estimated impact function _g_ ( _·_ ) for different values of _sh_ . For households in M4, we define three levels of exposure: low, at a level equal to the median share (5.5 percent); medium, at the mean level equal to 11.2 percent; high, for a level equal to the median share among farmers above the sample mean (a value close to 20 percent). The corresponding figures for M6, the extended Mekong catfish provinces (adding Sac Trong and Tien Giang) are 4.4 percent, 9.6 percent, and 16.9 percent respectively.

Results from our regressions are in Table 5. We report the impact on total household income for the M4 sample in column (1) and for the M6 sample in column (2). The corresponding results for per capita household income are in columns (3) and (4) and, for net income, in columns (5) and (6). Panel A) shows the estimates from the quadratic model. All our estimates, in both samples and for the three outcomes, are negative and statistically significant at the 5 percent level or below. As indicated in Section 4.1, the impacts are to be interpreted as relative to the marginal fish farmer with _sh_ approximately equal to zero. In the remaining of the paper we will refer to such differential changes as to “relative income losses”.

In regions included in M4, a farmer with the median pre-shock share suffered a 6.2 percent relative income loss. A farmer with an average pre-shock share is predicted to have suffered instead a relative income loss of 11.3 percent. The relative losses for a high-exposure farmer are even higher at 16.9 percent. The impact on per capita income is very similar, 6.4, 11.7 and 17.6 percent, respectively. Instead, the impact on net income is slightly larger: 8.1 percent for low-exposure, 14.7 percent for average-exposure, and 21.7 percent for high-exposure.<sup>18</sup> When the Mekong M6 sample is used instead, the impact on each outcome is smaller. This was to be expected, because farmers in the two new provinces included in M6 are less specialized in catfish and there is therefore less overall exposure to the shock. Total income

> 18While these differences are not statistically significant, we explore adjustments in input use below.

22
Policy Research Working Paper 8606

# **Abstract**

This paper examines how temporary exposure to a different ethnic region affects national integration using original survey data from participants in Africa’s largest national youth service program. Seven years later, participants randomly assigned to serve in a state with a different ethnic majority are five times more likely to live all across the country compared to those who served in their ethnic region. They have more interethnic romantic relationships and express greater national pride. Nevertheless, immersion in a different ethnic region strengthens participants’ ethnic pride and favorable attitudes towards co-ethnics. The results suggest that national and ethnic identity can thrive together.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The author may be contacted at ookunogbe@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 2 shows the number of countries and associated populations in the GMD sample used for the analysis. In terms of child population, the GMD sample has high regional coverage of developing countries in South Asia, Latin America, Europe and Central Asia, and East Asia and the Pacific (above 84 percent); partial coverage of Sub-Saharan Africa (74 percent); very low coverage of Middle East and North Africa (3.9 percent); and no coverage of North America. As a result, the distribution of children by region is close to that of the world, but the sample heavily underrepresents the Middle East and North Africa (0.4 vs 6.9 percent) and slightly underrepresents Sub-Saharan Africa (20.7 vs 23 percent). South Asia is moderately overrepresented in the sample, as it accounts for 35.7 percent of the sample as opposed to 30.2 percent of the developing world child population. Because of underrepresentation in Middle East and North Africa we follow the World Bank and do not report that region separately in the results. However, this region accounts for a very low percentage of the extreme poor and therefore has a minor impact on the results.<sup>8</sup>

**Table 2: Population and Child Population distribution in Sample of Global Micro Database, by income group, region and welfare type**

||**Number**<br>**of**<br>**countries**|**Population**<br>**(millions)**|**Share of**<br>**sample**<br>**population**<br>**(%)**|**Population**<br>**of children**<br>**(millions)**|**Share of**<br>**sample**<br>**child**<br>**population**<br>**(%)**|**Share of**<br>**developing**<br>**world child**<br>**population**<br>**represented in**<br>**sample (%)**|
|---|---|---|---|---|---|---|
||89|5249.1|100.0|1686.1|100.0|82.1|
|||**In**<br>|**come Group**<br>||||
|Low Income|21|628.5|12.0|294.5|17.5|76.4|
|Lower Middle<br>Income|29|2281.2|43.5|837.6|49.7|86.3|
|Upper Middle<br>Income|26|2071.8|39.5|500.2|29.7|82.4|
|High Income|13|267.5|5.1|53.8|3.2|60.7|
||||**Region**<br>||||
|EastAsia &Pacific|11|1889.3|36.0|458.7|27.2|89.9|
|Europe and Central<br>Asia|24|424.7|8.1|93.8|5.6|84.5|
|Latin America &<br>Caribbean|18|550.3|10.5|176.9|10.5|89.6|
|Middle East & North<br>Africa|3|16.3|0.3|5.6|0.3|3.9|
|South Asia|7|1667.1|31.8|601.7|35.7|97.1|
|Sub-Saharan Africa|26|701.4|13.4|349.4|20.7|74.1|
|||**W**|**elfare Type **||||
|Consumption|59|4497.1|85.7|1452.5|86.1|N/A|
|Income|30|752.0|14.3|233.6|13.9|N/A|

Source: GMD, UNDESA, WDI

> 8 In 2008, the most recent year MENA estimates are reported, the region accounted for 0.7 percent of the total number of extreme poor.

5
# I. Introduction

**Between 2015 and 2020, economic growth in Bulgaria positively impacted the living standards of the average and the poorest 40 percent of households, despite the COVID-19 shock.** During this period, economic growth contributed to a decrease in absolute poverty. Notably, Bulgaria experienced an acceleration in economic growth before the COVID-19 pandemic, benefiting various income groups, especially those with lower incomes. Despite the COVID-19 shock, GDP per capita increased by 2.4 percent annually from 2015-2020. As a result, real incomes steadily approached the average levels of the European Union (EU), reaching more than 50% of the EU average. Incomes witnessed a significant rise among the bottom 40 percent of households, with an average annual increase of 10.1 percent from 2015 to 2020. This growth rate slightly exceeded the overall average. Moreover, the bottom 20 percent of households experienced even faster income growth, with an average annual increase of 13.6 percent over the same period. The median household's income growth also performed well, rising at a rate of 8.1 percent annually.

**Due to these improvements, Bulgaria made noteworthy progress in reducing poverty over the past decade; however, levels are high by EU standards.** International poverty in Bulgaria notably decreased, with poverty incidence based on the US$6.85 2017 PPP poverty line declining by 9.6 percentage points over 2015-2020 (figure 1, panel a) **.** Despite these positive developments, poverty levels in Bulgaria remain relatively high compared to EU standards (figure 1, panel b) **.** The anchored at-risk-of-poverty rate (fixed in 2019) has also experienced substantial declines in recent years.

**Figure 1. Bulgaria Poverty and Inequality Trends and EU Comparison**

<!-- Start of picture text --> a. Income-based poverty rate, upper-middle income (UMI) poverty b. Income-based poverty rate, upper-middle income (UMI) poverty<br>line ($6.85 per day, 2017 PPP), Bulgaria, 2006-2020, Percent line ($6.85 per day, 2017 PPP), Selected EU countries, 2020, Percent<br>30<br>12<br>25<br>10<br>20<br>8<br>15<br>6<br>10<br>4<br>5<br>2<br>0<br>0<br>Percent<br>Poverty Headcount (Percent)<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Luxembourg Slovenia Finland Cyprus Netherlands Belgium Czech Republic Denmark Norway Malta Sweden Austria Estonia Lithuania Slovak Republic Croatia Latvia Hungary Portugal Spain Greece Bulgaria Romania<br><!-- End of picture text --> 2
Comuodity Prices, Macroeconomic News, and Business Cycles

# by Dhaneohwar Chura

# Table of Contents

# Table of Contents

|I.|Introduction|)|
|---|---|---|
|II.|Survey of Literature|5|
|III.|Theoretical<br>Considerations|12|
|IV.|Data and Variable Specification|20|
|V.|Emperical Framework<br>and Results|26|
|VI.|Summary and Conclusions|35|
|Referenc|es|37|
|Table 1:|Commodity Futures Contract Characteristics|39|
|Table 2:|Immediate and Lagged Commodity<br>Responses to Macroeconomic<br>News<br>Period 01/02/85- 05/31/89|40|
|Table 3:|Immediate and Lagged Commodity<br>Responses<br>Macroeconomic<br>News,<br>Period 01/02/85- 09/30/86|41|
|Table 4:|Immediate and Lagged Commodity<br>Responses<br>Macroeconomic<br>News,<br>Period 10/01/86- 12/31/87|42|
|Table 5:|Immediate and Lagged Individual<br>Responses<br>Macroeconomic<br>Newt,<br>Period 01/02/88- 05/31/89|43|
|Table 6:|Individual and Lagged Grouped Commodity<br>Responses<br>to Macroeconomic<br>News Over Various Periods|44|
|Figures||45|
Table 2: Korea: Sources of Funds for Housing Purchase by Househotd Incoa Leel for All Households, 1966.

|..................<br>:<br>_:NH INCCIE_<br>:10,000W<br>*|_\_<br>m\|,i........<br>SALEOF<br>PREVIOtUS<br> <br>HIOUSE<br>(1)|i................................<br>RETURI<br> <br>CHONSE<br> <br>DEPOSI<br>(2)|...........................................................<br>NED<br>SAVIuGS<br>I SECWRITIES<br>T<br>(3)|..........................................<br>KE<br>(4)|..<br>SALE<br>REAL<br>ESTATE<br>(5)|kIl<br>LOUN<br>(6)|FORMA<br>LOAN<br>(7)|L It K<br>PLACE<br>LOUN<br>(a)|DIM<br>RKET<br>LOAN F<br>(9)|CIOISEIAS<br>DEPOSIT FR<br>RON RENTER<br>OR<br>(10)|SISTAIUCE<br>ON PARENT<br>RELATIVE<br>(11)|<br>WORK<br> <br>PLACE<br>GRANTS<br>(12)|OTERS<br>(13)|IOTAL|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|.......<br>1-10|...|...........................<br>11.9|......................<br>23.9|.............................<br>31.3|.....................<br>6.0|...................<br>1.5|..................<br>4.5|..................<br>0.0|0.0|6.0|3.0|3.0|1.5|7.4:|100.00|
|11-20||11.9|23.5|27.7|8.9|5.6|2.6|1.0|0.3|6.9|1.7|5.6|0.3|3.7|100.00|
|21-30|*|<br>8.6|26.3|28.8|8.1|3.7|1.8|1.8|0.4|4.7|4.6|7.8|0.4|3.0|100.00|
|31-40||10.5|22.5|29.3|8.9|2.6|2.5|2.8|1.4|5.4|4.5|7.3|0.2|2.1|100.00|
|41-50||12.7|18.9|29.5|9.4|2.9|3.8|2.6|1.3|4.9|5.3|5.8|0.2|2.7|100.00<br>\'|
|51-70||14.2|15.5|26.4|8.3|3.0|4.9|3.9|0.6|5.0|6.3|7.1|0.2|2.6|l1 O.0 :|
|71-100|*|<br>18.3|13.0|<br>26.5|7.4|3.8|8.6|2.4|1.1|4-|6.2|6.4|0.3|1.9|100.00|
|:over100<br>..........|*|<br>23.9<br>..................|7.1<br>.......................|29.5<br>.............................|6.7|4.5|3.4|6.3<br>............................|0.4<br>....................................|2.6<br>........................|7.1|6.0|1.2|1.3 *<br>-------...<br>-|100.00<br>-----------...................................|

Source: Research an Policies to Strengthen the Housing Finance System. Interim Report. Dec. 1986, Based on Neu IRINS Survey. P. 69.
26 capabilities, and making the legal and regulatory framework more conducive for business.14

# B. Jncreagsing Savinat

To sustain investment for a desirable rate of growth, the adjusting countries have to increase their rate of saving. This need is greatest in the highly indebted countries, mostly in Latin America and in Sub-Saharan Africa, whose saving rates fell significantly in the 1980. (Table 5).

Although public policies can affect public saving directly, there are limits to their effect on private saving. Public saving and the way it is financed affect the economic environment -- GDP growth and inflation -- and Table 5: Gross domestic and national saving ratios in 83 developing countries

|_Inda2trlrgqi_|_1970480_|_198148_|_41985-8_|
|---|---|---|---|
|Grossdomestic savinglGDP||||
|Total|0.15<br>|0.12|0.13|
|Africa|0.11<br>|0.06<br>|0.08|
|_Asia_|0.19|0.19|0.21|
|Europe/Middle East/||||
|North Africa|0.15|0.16|0.15|
|Latin America and||||
|CAribbean|020|0.17|0.17|
|Gross national saving/GNP||||
|Total|0.14|0.10|0.10|
|Afrtca|0.11|0.06|0.06|
|Asia|0.18|0.18|0.19|
|Europe/Middle East/||||
|North Africa|0.15|0.14|0.12|
|Latin America and||||
|Caribbean|0.17|0.12|0.11|

Note: Data were not avaiable for Guinea. Hunpary, Mozanbique, Poland, and Yemen PDR.

a. In a few cues, t988 data were unavailable; for them, the 1987 saving ratios were assumed.

Source: ANDREX database, World Bank _14_ For a thorough discussion of these issues, see World Bank (1989a); see also G.M. Meier and W.F. Steel, eds., (1989).