rafmacalaba/datause-encoder-data huggingface dataset:
the dependent variable. The estimated coefficients also show the percentage change as the model uses a log transformation of the dependent variable. The use of PPML regressions as an alternative to OLS regressions is common practice in the trade and finance literature using bilateral country data. Countries typically do not trade with, or invest in, every other country in the world, thus yielding a data set with many zero-valued observations (Helpman, Melitz, and Rubinstein 2008). The use of standard OLS log-linear estimations might raise some concerns in this context as this approach excludes all these zero-valued observations. In contrast, the PPML estimator provides a natural way to deal with zero values in the dependent variable, while also being consistent in the presence of heteroscedasticity.<sup>12</sup> We report both OLS and PPML estimations because they provide different insights on the intensive and extensive margins.<sup>13</sup> By keeping observations with zero values, the PPML estimations capture the dynamics in international investments along the intensive margin (the growth in the value of investments) and the extensive margin (the growth in the number of bilateral links). In contrast, the OLS regressions better capture trends in the intensive margin as they estimate growth dynamics conditional on positive international investment. The C-C OLS regressions are especially informative in this regard. At higher levels of aggregation, there is less of a distinction between the intensive and extensive margins. That is, the entry and exit of bilateral links between individual country pairs affect the extensive margin at the C-C level, but at a higher aggregation (R-R and C-R) they only affect the value of the dependent variable, not whether the aggregate link is active or not.

The R-R regressions include R-R fixed effects (North-to-North, North-to-South, South-toNorth, and South-to-South). Analogously, the C-R regressions include C-R fixed effects. For each country in the sample, we include a set of four dummy variables capturing their investments in the

> 12 The PPML estimator presents some advantages over other estimators when multiple zero-valued observations are present (Santos Silva and Tenreyro 2006, 2010; Brei and von Peter 2018).

> 13 Because there are no observations with zero values at the R-R level, we estimate only OLS regressions in this case.

17
**Table 6: Networks, firm age and export participation**

|Dependent variable:||||export pa|rticipation||||
|---|---|---|---|---|---|---|---|---|
||full sa|mple|low prod|uctivity|medium pr|oductivity|high pro|ductivity|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||Coeff.|F-stat<br>(P-value)|Coeff.|F-stat<br>(P-value)|<br>Coeff.|F-stat<br>(P-value)|<br>Coeff.|F-stat<br>(P-value)|
|common language * born before 1976|0.1129***|51.42|0.0893***|17.68|0.1231***|44.46|0.1361***|26.87|
||(0.0376)|(0.00)|(0.0328)|(0.00)|(0.0406)|(0.00)|(0.0415)|(0.00)|
|common language * born in 1976-1990|0.0841**||0.0772**||0.0807**||0.0981**||
||(0.0357)||(0.0328)||(0.0352)||(0.0405)||
|common language * born after 1990|0.0738**|8.60|0.0744**|0.50|0.0735**|3.65|0.0734**|8.69|
||(0.0337)|(0.00)|(0.0340)|(0.48)|(0.0347)|(0.06)|(0.0323)|(0.00)|
|ln emigrants * born before 1976|0.0023***|8.29|0.0018**|6.29|0.0024**|6.79|0.0031***|9.41|
||(0.0009)|(0.00)|(0.0007)|(0.01)|(0.0010)|(0.01)|(0.0011)|(0.00)|
|ln emigrants * born in 1976-1990|0.0016**||0.0013**||0.0017**||0.0018**||
||(0.0007)||(0.0006)||(0.0007)||(0.0007)||
|ln emigrants * born after 1990|0.0011**|6.13|0.0008**|5.77|0.0011**|5.93|0.0014**|4.94|
||(0.0005)|(0.01)|(0.0004)|(0.02)|(0.0005)|(0.02)|(0.0006)|(0.02)|
|Firm fixed-effects|Ye|s|Y|es|Y|es|Y|es|
|Observations|2852|926|1107|627|929|874|815|425|
|Firms|147|82|57|39|48|18|42|25|
|Destinations|19|3|19|3|1|93|1|93|
|R2|0.1|1|0.|09|0.|11|0.|13|

<u>Notes: The estimation method is OLS. Columns (1), (3), (5) and (7) report estimated coefficients from regressions that additionally include</u> interactions between the other gravity variables and the age-cohort dummies. Robust standard errors clustered by importing country are in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Columns (2), (4), (6) and (8) report F-tests of equality of coefficients across the three age-cohorts, where the reference group is the 1976-1990 cohort.

5
<!-- Start of picture text --> K=3*C L=K*(1-D) M N=L-M<br>Ultimate Ultimate Ultimate Ultimate<br>Line of business gross loss 2009- | netloss retained | loss covered by | net loss retained<br>2011 under prop. RI non-prop. RI under prop. & non-<br>2009-2011 2009-2011 prop RI 2009-2011<br>Motor vehicle liability insurance 42,000 42,000 500 41,500<br>Other motor insurance 9,000 9,000 500 8,500<br>Marine, aviation and transport insurance 12,000: 6,000 500 5,500<br>Fire and other damage to property insurance 31,500 22,050 1,000 21,050<br>General liability insurance 9,000 9,000 500 8,500<br><!-- End of picture text -->

<!-- Start of picture text --> A. B. C=B/A<br>Total gross loss | Total net loss of | Retention ratio<br>of last three last three years<br>103,500 85,050<br><!-- End of picture text -->

<!-- Start of picture text --> D. E. F. G=C*(0.18*E<br>Adjusted gross | Adjusted gross | Adjusted gross + 0.16 * F)<br>premium 2011 | premium under€| premium above €| Premium Index<br>52,500 s25005/7.5m | oo 5/7.5m | 7,765<br><!-- End of picture text -->

<!-- Start of picture text --> H. I=H/3 J. K. L=C* (0.26 *J+0.23* kK)<br>Adjusted gross | Mean adjusted | Mean adjusted | Mean adjusted Claims Index<br>loss 2009-2011 gross loss of | gross loss under | gross loss above<br>2009-2011 €40.3m € 40.3m<br>108,000 | 36,000 | 36,000 S| 7,691<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 8: Median Survival Time (adjusted for exporters) based on Fixed<br>Costs by Country<br>Ukraine<br>Kazakhs ...<br>Kenya<br>Greece<br>Portugal<br>Turkey<br>Morocco<br>Jordan<br>Italy<br>Russia<br>Peru<br>Colombia<br>0 4 8 12 16<br><!-- End of picture text --> <!-- Start of picture text --> 20<br><!-- End of picture text --> <!-- Start of picture text --> Weeks<br>Note : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,395<br><!-- End of picture text --> The analysis here also offers a partial answer to the puzzling result in Tables 2-4 that Schumpeter’s theory does not find support in our data. Previous analyses have shown that exporters are among the most productive firms in any economy (for example, Wagner 2007). As exports are among the most affected sectors of the economy during economic distress periods that involve health concerns, productive firms are in effect subjected to financial strain more than the average firm. The Schumpeter theory of creative destruction no longer holds.

# **VII. Policy Response**

The evidence in Section VI suggests that significant government response is warranted to prevent mass insolvency. Such response can proceed in two steps.

First, governments need to temporarily suspend bankruptcy procedures, which often dictate that illiquid firms’ assets get transferred to their secured creditors, mostly banks. A number of countries have already taken this step. For example, in France bankruptcy law normally gives 45 days from the moment a debtor can no longer pay its debts to filing for bankruptcy. The new ordinance says that the firms will have three months after the end of the state of emergency (i.e. as things now stand, until September 2020) to file for bankruptcy if needed. The German parliament passed a temporary suspension of the firms’ obligation to file for bankruptcy. The suspension is 19
Molina, E., Pushparatnam, A., Rimm-Kaufman, S., and Wong, K. (2018a). Evidence-based Teaching. Working Paper.

Molina, E., Trako, I., Hosseini Matin, A., Masood, E. and Viollaz, M. (2018b). The Learning Crisis in Afghanistan. Policy Paper. World Bank, Washington, D.C.

Morgan, D.N., and Wagner, C.W. (2013). “What’s the Catch?” Providing Reading Choice in a High School Classroom. _Journal of Adolescent and Adult Literacy_ , 56(8), 659-667.

Muijs, D., Kyriakides, L., van der Werf, G., Creemers, B., Timerley, H., and Earl, L. (2014). State of the art – teacher effectiveness and professional learning. _School Effectiveness and School Improvement_ , 25 (2), 231-256.

Nye, B., Konstantopoulos, K., and Hedges, L. V. (2004). How Large Are Teacher Effects? _Educational Evaluation and Policy Analysis,_ 26 (3): 237-257.

O’Connor, E. E., Dearing, E., & Collins, B. A. (2011). Teacher-child relationship and behavior problem trajectories in elementary school. _American Educational Research Journal_ , 48(1), 120-162.

OECD (2009). _Creating Effective Teaching and Learning Environments: First Results from TALIS._ Paris, France.

Parker, J. G., and Asher, S. R. (1993). Friendship and friendship quality in middle childhood: Links with peer group acceptance and feelings of loneliness and social dissatisfaction. _Developmental Psychology_ , 29(4), 611-621.

Patrick, H. (1997). Social self-regulation: Exploring the relations between children's social relationships, academic self-regulation, and school performance. _Educational Psychologist_ , 32(4), 209-220.

Pianta, R. C. (2016). Teacher-Student Interactions: Measurement, Impacts, Improvement, and Policy. 3(1), 98-105.

Pianta, R. C., Hamre, B., and Stuhlman, M. (2003). Relationships between teachers and children. _Handbook of Psychology_ , 199-234.

Pianta, R. C., La Paro, K. M., and Hamre, B. K. (2008). _Classroom Assessment Scoring System (CLASS)_ . Baltimore, MD: Brookes.

Pischke, J. S. (2007). The impact of length of the school year on student performance and earnings: Evidence from the German short school years. _The Economic Journal,_ 117(523), 1216-1242.

Porter, A. C., & Brophy, J. (1988). Synthesis of research on good teaching: Insights from the work of the Institute for Research on Teaching. _Educational Leadership_ , 45(8), 74-85.

Reeve, J. (2006). Teachers as facilitators: What autonomy-supportive teachers do and why their students benefit. _The Elementary School Journal_ , 106(3), 225-236.

34
<!-- Start of picture text --> oO<br>oS<br>oS<br>a<br>oO<br>So<br>o<br>oS<br>Aw<br>oS<br>So<br>So<br>re)<br>oS<br>oS<br>So<br>S<br>So<br>oS<br>So<br>lo<br>oS Total at Q2 Q3 Q4 Q5<br>ME 2000 MEN 2003 MEM 200 NEN 2000 ME 20:12<br><!-- End of picture text -->
# **1. Introduction**

An adequate supply of infrastructure services has long been viewed as a key ingredient for economic development, by both academic economists and policymakers. Indeed, transport infrastructure played a central role in Adam Smith’s vision of economic development. Over the last two decades, starting with the work of Aschauer (1989), academic research has devoted considerable effort to theoretical and empirical analyses of the contribution of infrastructure development to growth and productivity; see for example Sánchez-Robles 1998; Canning 1999; Demetriades and Mamuneas 2000; Röller and Waverman 2001; Esfahani and Ramirez 2003; Calderón and Servén 2004b, 2010). More recently, increasing attention has been paid also to the impact of infrastructure on poverty and inequality (Estache, Foster and Wodon 2002, Calderón and Chong 2004). While the empirical literature on these two topics is far from unanimous, on the whole a consensus has emerged that, under the right conditions, infrastructure development can play a major role in promoting growth and equity – and, through both channels, helping reduce poverty.

From the policy perspective, the renewed concern with infrastructure can be traced to two worldwide developments that took place over the last two decades. The first one was the retrenchment of the public sector since the mid 1980s, in most industrial and developing countries, from its dominant position in the provision of infrastructure, under the increasing pressures of fiscal adjustment and consolidation. The second was the opening up of infrastructure industries to private participation, part of a worldwide drive towards increasing reliance on markets and private sector activity, which has been reflected in widespread privatization of public utilities and multiplication of concessions and other forms of public-private partnership. While this process first gained momentum in industrial countries (notably the U.K.), over the 1990s it extended to most developing economies, with Latin America leading other developing regions in terms of both speed and scope of private involvement in infrastructure industries.

Against this background, there is a growing perception that poor infrastructure has become one of the key barriers to growth and development across Latin America. Such perception is found among policy-makers and observers, as well as in surveys of infrastructure users in the region. The underlying concern is that private sector 2
**Table A.3.6: Global aggregate economic cost at present value by region, income group, and lending type**

|||Post-COVID-19<br>||
|---|---|---|---|
|Aggregate|Optimistic|Intermediate|Pessimistic|
|EAP|-1.8 T|-3.8 T|-5.9 T|
|ECA|-1.1 T|-2.8 T|-4.6 T|
|LAC|-0.4 T|-0.8 T|-1.2 T|
|MNA|-0.2 T|-0.5 T|-0.8 T|
|NAC|-0.5 T|-1.4 T|-2.3 T|
|SAR|-0.3 T|-0.5 T|-0.8 T|
|SSF|-0.2 T|-0.3 T|-0.5 T|
|Grand Total|-4.3 T|-10.0 T|-15.9 T|
|HIC|-1.7 T|-4.8 T|-8.0 T|
|UMC|-1.9 T|-4.0 T|-6.0 T|
|LMC|-0.6 T|-1.1 T|-1.7 T|
|LIC|-0.1 T|-0.2 T|-0.2 T|
|Grand Total|-4.3 T|-10.0 T|-15.9 T|
|Part I|-1.7 T|-4.6 T|-7.7 T|
|IBRD|-2.4 T|-4.9 T|-7.5 T|
|IDA/Blend|-0.3 T|-0.5 T|-0.7 T|
|Grand Total|-4.3 T|-10.0 T|-15.9 T|

Source: Authors’ calculation. Aggregate economic cost of foregone earnings at present value (2017 PPP $). Simulation 1 results based on latest available LAYS of 157 countries (unweighted average), with the change in LAYS expressed as the global aggregate economic cost at present value of students’ foregone earnings.

50
Table 1: Macro Indicators **in** Ghana, 1984-94.

|Indicator|1984|1985|1986|1987|1988|1989|1990|1991|1992|1993|1994|Kenya|Cote D'<br>Ivoire|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|GNP per capita<br>||1.1|1.4|1.0|2.5|2.2|0.7|2.3|1.0|1.7|0.6|0.2|-4.7|
|(percent growth)||||||||||||(1980-92)|(1980-92)|
|Gross Domestic Income<br>(percent growth)||2.9|6.3|4.5|5.1|3.3|2.7|5.4|3.3|3.5|5.2|||
|Terms oftrade|100|91|105|101|96|81|75|77|71|62|73|67|65|
|(1984=100)||||||||||||(1987-100)|(1987=100)|
|Nominal exchange rate|36|_54.5_|106.4|162.4|202.4|270|330|_375_|437|649|956.6|32.22|264.7|
|(domestic currency/dollar)||||||||||||(1992)|(1992)|
|Debt service (percent of exports)|21.6|23.5|28.5|45.8|56.0|51.8|34.7|28.3|24.6|35.1|26.6|||
|Inflation (percent growth)||10.4|24.6|39.7|31.4|25.2|37.2|17.9|10.1|25.0|24.8|9.3<br>(1980-92)|1.9<br>(1980-92)|
|Trade balance( millions USD)|-66.6|-44.7|-0. 1|-114.0|-117.2|-202.4|-303.4|-315.3|-470.8|-664.1|-350.5|-374<br>(1992)|-873<br>(1992)|
|Life expectancy at birth (years)|||||||||56.0|||59.0|56.0|
|Illiteracy rate (in percent)|||||||40.0|||||31.0|46.0|
|Population growth rate|2.2||||||||3.2|||3.6|3.8|
|(in percent p.a)|(1970-80)||||||||(1980-|||(1980-92)|(1980-92)|
||||||||||92)|||||
|Labor force growth rate|2.4||||||||2.7|||3.5|2.6|
|(percent p.a)|(1970-80)||||||||(1980-<br>92)|||(1980-92)|(1980-92)|
|Infant mortality rate|111||||||||82|||66|91|
|(per live 1000 births)|(1970)||||||||(1992)|||(1992)|(1992)|

Source: **Ghana private** sector growth and poverty reduction: A country Economics Memorandum, The World Bank, 1995; World Development Report, **1994.**
Providing quality education - The successes in enrolment also sit beside deficiencies in quality: While primary school enrolment rate is high, many children do not stay in school to reach grade five: approximately one third of children that enrol do not complete primary education. This outcome is much worse than the average for East Asia and most of the neighboring countries (Figure 8) and represents a large waste of talent and resources.

Figure 8. Primary Completion Rates for selected countries and region, 1997 <!-- Start of picture text --> Primary Completion Rates, 1997 BMale<br>100 @ Female<br>70 aa | ot i at ae<br>0 Z Co: : ;<br>. ra &“ oe 2Y &Ro<br>NS 3° ye ea<br>x<br>Source: DFID (2002)<br>And even when they do complete primary school, students will have received<br>international norm for annual annual teaching/ learning time time (about 900-1000 hours), less than half the number of<br>tuition hours a child completing primary school in Sri Lanka would have had and only 40 40<br>number of hours a child of hours a child hours a child a child child in Thailand would have enjoyed (Table would have enjoyed (Table have enjoyed (Table enjoyed (Table (Table 3).<br>Table 3. The Duration of Primary Schooling in sclected countries, 2001<br>a<br>Hours per day (average) 3 | 6 | lc<br>Vos<br>otal hours per year 1200 1200<br>Hours per cycle 2887 7200 6000<br><!-- End of picture text -->

And even when they do complete primary school, students will have received approximately half the international norm for annual annual teaching/ learning time time (about 900-1000 hours), less than half the number of tuition hours a child completing primary school in Sri Lanka would have had and only 40 40 percent of the number of hours a child of hours a child hours a child a child child in Thailand would have enjoyed (Table would have enjoyed (Table have enjoyed (Table enjoyed (Table (Table 3).

Source: DFID (2002)

# Targets andfuture challenges

The proposed national goals and targets for the education sector as presented in the CPRGS reflect the challenges ahead in universalising primary education and ensuring that the quality of teaching and learning outcomes is improved, especially for the poor (Box 3).

17
the border. Countries may thus be more likely to import arms after discoveries closer to the border if the arms imports are linked to potential interstate conflict. Here I interact the discovery dummy with a measure of the country’s discovery’s minimum distance to the border. Since this distance does not exist for countries with no discoveries, I focus only on discovery countries here. Furthermore, since this distance exists only for years with a discovery, I aggregate it at the country level by taking the minimum of the distances of the country’s discoveries. The coefficient on the discovery dummy’s interaction with _MinDist_ therefore captures whether discoveries have a larger effect on arms imports in countries where discoveries are closer to the border. I take the minimum rather than the average distances as only one discovery near the border in a multi-discovery country may have as much effect as one in a single-discovery country. Results suggest no significant interaction however, and COMTRADE data suggest that discoveries far away from borders might have more of an effect, if any at all. Overall there does not seem to be any clear pattern suggesting that certain types of discoveries matter more than others.

# **4 CONCLUSION**

In this paper I have shown that arms imports increase by about 25% in the 5 years after a giant oil discovery. This effect is even larger, at around 51%, if the oil price is high. As a share of GDP, arms imports increase by around 30% in the 5 years after a giant discovery. I suggest that this relationship may be due to newly oil-rich countries wanting to protect their resources from rebels or foreign invasion, or to arms exporters using arms to _offset_ pricey oil, or to oil-dependent countries seeking to secure future oil supply and contain the risk of instabilities in a future oil supplier. I also suggested, based on the anecdotes of Mozambique and Angola, that an alternative mechanism is that shady businessperson exploit these opportunities to siphon debt money made available by the new discoveries.

This paper adds to our understanding of the resource curse by suggesting a new channel 22
|UIw|PPR Working Paper Series<br>Auda|<br>_DAh_<br>t<br>or|Contact<br>for<br>pr|
|---|---|---|---|
|WPS320 Protection Facing Exports from<br>Sub-Saharan Africa in the EEC,<br>Japan, and the U.S.|Refik Erzan<br>Peter Svedberg|||
|WPS321 Economic and Policy Determinants<br>of Public Sector Deficits|Jorge Marshall<br>Klaus Schmidt-Hebbel|||
|WPS322 The Earmarking of Government<br>Revenues: A Review of Some<br>World Bank Experience|William McCleary|||
|WPS323 The Old and the New is Heterodox<br>Stabilization Programs: Lessons<br>from the Sixties and the Eighties|Miguel A. Kiguel<br>Nissan Liviatan|||
|WPS 324 Ethics and Human Values in Family<br>Planning: The African Perspective|F. Sai<br>K. Newman|||
|WPS325 Manufacturers' Responses to Infra-<br>structure Deficiencies in Nigeria|Kyu Sik Lee<br>Alex Anas|||
|WPS326 Do Exporters Gain from VERs?|Jaime de Melo<br>L Alan Winters|||
|WPS327 Making Noisy Data Sing: Estimating<br>Production Technologies in<br>Developing Countries|James R. Tybout|||
|WPS328 Europe, Middle East, and North<br>Africa (EMN) Region Population<br>Projections, 1989-90 Edition|Rodolfo A. Bulatao<br>Eduard Bos<br>Patience W. Stephens<br>My T. Vu|November 1989|S. Ainsworth<br>31091|
|WPS329 Latin America and the Caribbean<br>(LAC) Region Population<br>Projections, 1989-90 Edition|Rodolfo A. 6ulatao<br>Eduard Bos<br>Patience W. Stephens<br>My T. Vu|November 1989|S. Ainsworth<br>31091|
|WPS330 Africa Region Population<br>Projections, 1989-90 Edition|Rodolfo A. Bulatao<br>Eduard Bos<br>Patience W. Stephens<br>My T. Vu|November 1989|S. Ainsworth<br>31091|
|WPS331 Asia Region Population Projections,<br>1989-90 Edition|Rodolfo A. Bulatao<br>Eduard Bos<br>Patience W. Stephens<br>My T. Vu|November 1989|S. Ainsworth<br>31091|
refugee children in low-, lower-middle, upper-middle, and high-income countries; while Figure C shows the absolute number of school-age refugees in each income group. As these figures show, the number of school-age refugees in upper-middle income countries (UMICs) has increased dramatically in recent years, both in relative and absolute terms. In fact, between 2017 and 2022, the number of school-age refugees in UMICs increased tenfold, as crises in the Syrian Arab Republic and the República Bolivariana de Venezuela saw millions flee to neighboring UMICs, such as Türkiye and Colombia.

> <sup>8</sup>

<!-- Start of picture text --> 100%<br>4%<br>9% 15%<br>80%<br>32%<br>60%<br>74%<br>40%<br>31%<br>20%<br>21%<br>13%<br>0%<br>Low income Lower-middle income Upper-middle income High income<br><!-- End of picture text -->

# _Source: UNHCR (2023)_

As shown in Figure B, the proportion of school-age refugees hosted in LICs and lower-middle income countries (LMICs) has been falling in recent years. However, both income groups have seen a steady increase in the absolute numbers of school-age refugees within their countries. In fact, the total number of school-age refugees hosted by LICs tripled between 2010 and 2022, with year-on-year increases reported for 16 of the last 20 years. Similarly, the number of school-age refugees in LMICs doubled in the last ten years, and has tripled since 2009, as shown in Figure C, indicating that refugee numbers are increasing in all income categories.

displacements witnessed in 2023, with displacements from the República Bolivariana de Venezuela, Ukraine, Sudan, Haiti, Afghanistan and other countries likely to significantly increase the number of refugees globally in 2023.

> <sup>9</sup>

> 8 Some hosting countries recorded incomplete demographic data. For countries where data on school-age populations were incomplete, it was assumed that 19% of the total refugee population was aged 5-11 and 13% was ages 12-17. These proportions represent the average populations in those countries with complete demographic data.

> 9 This graph shows all school-age populations protected under the UNHCR mandate. This does not include Palestinian refugees under UNRWA’s mandate.

8