rafmacalaba/datause-encoder-data huggingface dataset:
turmoil, stock markets collapsed in a highly synchronized way across regions; and credit stagnated. This suggests a reinforcing pattern in which a decline in capital flows might have triggered collapses in asset prices and a generalized deterioration in conditions in domestic financial systems that in turn could have amplified the international flow of capital due to margin calls and capital requirements, among other triggers.

# _3.3. Correlates of growth collapses and growth recoveries_

To shed light on the impact of different variables related to the trade and financial mechanisms discussed above on growth collapses and recoveries, we present cross-country regressions for the 2008–2009 global financial crisis and, for comparison purposes, regressions for the Asian and Russian crises of the late 1990s. To mitigate some of the endogeneity concerns, we measure the explanatory variables at their pre-crisis values, namely 2007 values in the case of the 2008–2009 crisis and 1996 values for the Asian and Russian crises.<sup>17</sup> Table 3 reports regressions of growth collapses during the 2008–2009 crisis and the Asian and Russian crises on a number of variables capturing transmission channels. These regressions suggest that economies more open to trade, with larger current account deficits, with higher growth rates of domestic credit relative to GDP over 2003–2007, and more financially open suffered greater growth collapses during the 2008–2009 crisis. Although reserves in 2007 seem to be positively associated with growth collapses (top panel), the effect disappears after controlling for trade openness (bottom panel). Besides being statistically significant, these estimated coefficients are also economically significant. For example, the estimated coefficient

> 17 Our goal in this section is to consider a reduced set of, arguably, uncontroversial explanatory variables. There are several papers focused on uncovering the different possible variables affecting the incidence of the 2008-2009 crisis (Blanchard et al., 2010; Claessens et al., 2010; Frankel and Saravelos, 2010; Lane and Milesi-Ferretti, 2010; Rose and Spiegel, 2010, 2011a, and 2011b; Rose, 2011). Moreover, Aizenman and Spiegel (2010) argue that being a commodity exporter might also have an impact on the incidence of crises.

19
Figure 3: Evolution of fertility and schooling in Kenya <!-- Start of picture text --> Education and fertility by cohort<br>40% 80%<br>35% 70%<br>30% 60%<br>25% 50%<br>20% 40%<br>No education<br>15% 30%<br>Primary incomplete<br>10% 20% Primary complete<br>5% 10% Secondary incomplete<br>Secondary complete<br>0% 0% Higher education<br>1956-1960 1961-1965 1966-1970 1971-1975 1976-1980 union before 19<br>cohorts first birth before 20<br>age 20<br>% of women<br>educational level<br>% of women in each<br>married/pregnant before<br><!-- End of picture text --> 21
where _wet_ and _wct_ are prices for energy and carbon respectively and μ _kt_ and μ _lt_ are the user costs corresponding with stocks of labor and capital stocks. Prices for carbon emission allowances represent the societal valuation of the impacts of carbon emissions on common property atmospheric resources implicit in the target level of allowable emissions and the corresponding allocation of permits. This specification is similar to the study conducted by Considine and Larson (2006) of sulfur dioxide pollution allowances.

For the empirical analysis below, _Kt_ and _Lt_ are unobservable. To specify an empirical model, therefore, requires assuming the existence of a weakly separable sub-aggregate of energy and carbon emissions within the variable cost function. In particular, the weakly separable model implies that substitution possibilities between fuels and carbon emissions are independent of substitution possibilities between labor and capital, which is likely a reasonable assumption within a short-run context. The cost minimization problem, therefore, is to minimize energy and carbon emission allowance costs subject to output levels.

In the context of the model, there are three types of energy aggregates: i) primary fossil fuels, including coal, petroleum, and natural gas; ii) nuclear fuel and hydroelectric energy; and iii) renewable energy resources, including wind, solar, and geothermal energy. As discussed, while market prices for primary fuels are observable, those for nuclear, hydroelectric, and renewable energy are not. To accommodate this, the study assumes the existence of a weakly separable subaggregate for primary energy and carbon emissions contingent upon levels of nuclear, hydroelectric, and renewable energy generation, levels of output and the state of technology. More specifically, this implies the following short-run restricted energy and emission allowance cost function: 7
tion, the closed cases allow us to observe the effect of “a shot across the bow” by the competition agency. That is, the effect of an investigation of potential legal conduct and a public announcement that accused economic agents were not guilty of illegal monopolistic practices. In principle, this effect could go either way. On one hand, firms might be deterred from illegal conduct once they realize the threat of investigation. On the other hand, illegal conduct could be emboldened if the decision to close the case is, on average, an error as in the example provided by Khan (2017).

# **4.1 Meta-Analysis of Investigations by the Federal Competition Commission of Mexico**

We use the SDID method to estimate the pair ( _θ_<sup>ˆ</sup> _j_<sup>_y,_ˆ</sup><sup>_σ_</sup> _j_<sup>_y_),where</sup><sup>_y_indicatestheoutcomeof</sup> interest, for instance (the log of) sales, (the log of) the wage bill, or (the percentage point) operating profit margin. To allow for a comparison in outcome changes across the same set of cases, we estimate the SDID for all variables using the same set of weights, which match treatment and control on (the log of) sales.<sup>14</sup> For robustness, we also report results using the DID and SC estimators, which weight control industries differently.

The results are reported in the meta regression where _Si ∈{_ 0 _,_ 1 _}_ indicates a sanction is imposed by the Commission. The term _τ_ 0<sup>_y_isthe</sup> average outcome change after an investigation that does not lead to a sanction, and the term _τ_ 1<sup>_y_istheadditionaloutcomechangewhenthereisasanction.Undertheassumptionthat</sup> news of each decision is unanticipated, we interpret _τ_ 1<sup>_y_as the causal effect of sanctions within</sup> the set of industries where illegal conduct is suspected. This effect is measured controlling for any unique characteristics of industries where sanctions may occur.

Vita and Osinski (2016) suggest antitrust analysis should account for sampling variance in analysis of difference-in-difference price changes after mergers. We incorporate such variance into our analysis. Measurement error _σ_ ˆ _j_<sup>_y_isestimatedinthefirststageandincludedinthe</sup> meta regression in the term _ε_ ˆ<sup>_y_</sup> _j_<sup>_∼N_(0</sup><sup>_,_ˆ</sup><sup>_σ_</sup> _j_<sup>_y_).Insomespecifications,theregressionallowsfor</sup> heterogeneity in the difference-in-differences due to differences across industries through the term _u_<sup>_y_</sup> _j_<sup>_∼N_(0</sup><sup>_, γ_</sup> _j_<sup>_y_).Thisiscalledarandomeffects(RE)meta-analysisframework,usedin</sup> the medical sciences literature (Borenstein, Hedges, Higgins and Rothstein, 2021). The fixed effect (FE) meta-analysis framework would set _γj_<sup>_y_= 0 and</sup><sup>_uy_</sup> _j_<sup>= 0</sup><sup>_∀j_, assuming that the effect</sup>

> 14 Matching on other outcome variables produced qualitatively similar results.

20
# Foreword

Nearly 25 years ago, I interviewed industrial workers and their employers in SenegaL Industrial Labor in the Reoublic of Senegal (Praeger, New York, 1968) tells the story. Labor market imperfections, mostly inherited from French colonial legislation and institutions but also rooted in the extended family system, were so egregious as to stifle development. The replacement of expatriates by African workers was proceeding at a snail's pace, government and union wage policies were curbing employment growth, vocational schools were turning out graduates unsuitable for industrial employment. Wage and other regulations intended to advance the workers' welfare failed to achieve their objectives. The extended family system obliged workers to support a multitude of unemployed hangers-on, all the more numerous owing to polygamism, and this undercut incentives to strive for better pay and to save more. Labor market distortions encouraged enterprises to use capital-intensive methods of production rather than to expand employment. And a parallel 'informal sector" had sprung up of firms small enough to evade labor legislation. I Like the Three Musketeers, TerreUl and Svejnar have revisited the Senegalese labor ma*et 20 years or so later. Undoubtedly, some things have chaige;d. Fewer expatriates are now working in SenegaL But the striking picture which emerges is that mostly, things have remained unchanged. Labor regulations are still pervasive and largely self-defeating. Labor productivity remains low. The supply of skilled workers remains limited. And the burden of the extended family on wage-earners has become even greater. The authors document this unhappy situation very carefully with the help of interviews and detailed surveys, and ask themselves to what extent Senegal's uncommonly poor growth performance can be traced to labor market imperfections. They draw policy implications and make recommendations that address some of the root causes of stagnation. These should be of great
together with other 7 that lack pre-war information are used for evaluation of post-war only. Three variables, i.e., military expenditures, law and order, and terrorist attacks are examined only in the event years after the war due to their lack of available data in the pre-war period.<sup>4</sup> Altogether, we work with 41 countries involved in internal wars (15 from Africa, 17 from Asia, 3 from Europe, and 6 from Latin America) during the period 1960-2003. Among these countries, six (Burma, Cambodia, Iraq, Liberia, Sri Lanka and Sudan) were entangled in _two_ internal conflicts.

# **III.  Results**

We carry out two complementary analyses. In the first, we evaluate and compare the central tendency of each variable before and after the war, and with respect to the typical country in the control group (Table 1). In the second, we estimate the average slope (or rate of change) of each variable during the post-conflict period, examine its sign, and determine whether it differs from the corresponding slope in the control group (Table 2). We provide two sets of figures as complements to the tables. Figure 1 plots the median in each event year (seven years before the war and seven years after) for the conflict countries and the control group.<sup>5</sup> Figure 2 plots the medians in each event year after the war (this is not repetitive of Figure 1 because the sample for post-conflict analysis is larger than that for the post-pre war comparisons).

## **_Pre- and post-war comparisons_**

Visual examination of typical trends before and after the war can be illustrative and motivate more precise statistical analyses. For each indicator in turn, Figure 1 plots the cross-country median in each event year for the conflict and control-group countries. We can distinguish three types of behavior. Some variables (GDP per capita growth, polity2, civil and political rights, female-male ratio, and incidence of terrorism) exhibit a

> 4 For example, WDI started to collect military expenditures data (% as central government expenditures) in 1990; and ICRG provides ratings on law and order after 1984.

> 5 Since these are plots of the medians, they do not correspond exactly to Table 1 and 2, where the comparison is in the means.

8
Policy Research Working Paper 8897

# **Abstract**

This study finds that natural shocks—storms in particular—are a significant and often leading cause for power supply disruptions. This finding is based on 20 years of high frequency (i.e. daily) data on power outages and climate variables in 28 countries—Bangladesh, the United States and 26 European countries. More specifically: (1) Natural shocks are the most important cause of power outages in developed economies. On average, they account for more than 50 percent of annual outage duration in both the US and Europe. In contrast, natural shocks are responsible for a small share of outages in Bangladesh, where disruptions occur on a daily basis for a variety of reasons. (2) Outages due to natural shocks are found to last significantly longer than those due to non-natural shocks in—e.g. more than 4.5 times in Europe. Reasons include the challenge of locating wide-spread damages, and the sustained duration of storms. (3) Several factors can reinforce the adverse effect of natural shocks on power supply. In the US, forest cover is shown to significantly increase the risk of power outages when storms occur. (4) There are significant differences in network fragility. For instance, wind speeds above 35 km/h are found to be 12 times more likely to cause an outage in Bangladesh than in the US. This difference may be explained by a range of factors, including investments in infrastructure resilience and maintenance.

This paper is a product of the Global Facility for Disaster Reduction and Recovery, Climate Change Group. 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/ prwp. The authors may be contacted at jrentschler@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 8: Why don't more firms use the market for business services?**|Outsourced <br>Marketing|Outsourced<br>Accounting|HR services|
|---|---|---|---|
|_Information, trust, and search frictions_||||
|Do not know of any business service provider offering this service (i.e. they lack information about the market).|0.15|0.14|0.15|
|Do not know what kind of expertise their business needs (i.e. they can’t assess their internal needs).|0.17|0.18|0.11|
|Find it difficult to judge the skills of outsiders (i.e. they can’t assess the quality of the marketing service they’re buying).|0.21|0.21|0.21|
|Believe there is a lack of qualified workers (i.e. they just don’t think there is a supply of talent in the market).|0.11|0.09|0.15|
|Are not confident they will find someone they can trust (i.e., so they won’t be able to delegate responsibilities).|0.19|0.22|0.23|
|Worried it will take too much time or hassle to find a service provider (i.e., search costs are high).|0.20|0.21|0.24|
|**Any information, trust or search friction**|**0.47**|**0.49**|**0.59**|
|_Cost-benefit considerations_||||
|Think there is a low expected return from using service (i.e. they don’t think it will increase their business performance).|0.12|0.18|0.23|
|Think returns are possible but highly uncertain (i.e. Service can increase performance but the payoff is unpredictable).|0.28|0.31|0.35|
|Lack the money to pay for these services (i.e., they cannot afford the upfront costs or the cost to ‘test out’ a new service)|0.46|0.51|0.55|
|**Any cost-benefit reason**|**0.56**|**0.63**|**0.67**|
|_Other_||||
|Underpressure to hire family/friends instead<br>Notes: numbers shown are proportion of firms in first follow-up survey who think the reason is very likely or extremely like<br>the market for this professional business service.|0.18<br>ly as a reas|0.17<br>on more firms|0.21<br>do not use|

43
Notes for Tables 1 and 2: Source: IFS, World Currency Yearbook, and Currency Analysis (various issues) Data are quarterly end of period.

Figures in parentheses are t-statistice Variables: LPRE: ln(premium) LM20: ln(Money/Officlal Exchange Rate) LRESVD: ln(non-gold international reserves) RER: Deviation of real exchange rates from equilibrium DPREt change in ln(premium) DM20: change in ln(Money/Official Exchange Rate) DRESVD: change in ln(non-gold international reserves) DRER: change in RER Statistics: DF: Dickey-Fuller ADF: Augmented Dickey-Fuller DW: Durbin-Watson l: The cointegration equation for Bolivia included the following dumnies: DUMMYl: 3.41(10.30) for the period of the hyperinflation. DMIMY2: -1.97(-3.87) for the periof following the hyperinflation.
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.5 Growth in containerized and general-cargo traffic between 1995 and 2005**

||TE<br>|Cont<br>Us <br>|ainertraffic<br>Pe<br>|rcentage<br>| ̳000s <br>|Gene<br> tonnes<br>|ral-cargo traffic<br>Pe<br>|rcentage<br>|
|---|---|---|---|---|---|---|---|---|
||1995|2005|Overall<br>growth|Average annual<br>growth|1995|2005|Overall<br>growth|Average annual<br>growth|
|East Africa|505.1|1,395.0|+276|+10.7|13.8|38.4|+278|+10.8|
|North Africa|1,637.3|5,267.9|+322|+12.4|12.3|16.5|+134|+3.0|
|**Southern Africa**|**1,356.0**|**3,091.8**|**+228**|**+8.6**|**2.7**|**14.5**|**+532**|**+18.2**|
|West Africa|1,035.4|4,082.0|+394|+14.7|23.1|61.2|+265|+10.2|
|Total|4,533.8|13,836.7|+305|+11.8|52.0|130.7|+251|+9.7|

_Source:_ Ocean Shipping Consultants Limited 2010. _Note:_ TEU = twenty-foot equivalent unit.

Although southern Africa is endowed with several port options, Durban and Dar es Salaam are by far the most intensively utilized. Overall, due to rapid expansion of traffic, a few of the region’s ports are beginning to experience capacity constraints (figure 2.12). This is most notable in the case of Luanda (Angola) and Dar es Salam, where the general-cargo traffic handled significantly exceeds design capacity. Luanda, which is at almost 80 percent capacity, is on the threshold of facing a gridlock that would significantly undermine the efficiency of the port. Container traffic in Durban and Dar es Salaam exceeds 100 percent of their capacity. Cape Town also experiences challenges due to excess handling relative to design capacity, albeit on a smaller scale.

Durban struggles with handling South Africa’s own traffic and has experienced recurring berth congestion during the peak season. Shipping lines are threatening to reintroduce a surcharge for berthing delays that existed between 2002 and 2005. Although there are plans to bring significant additional capacity on stream, such as the new Pier One scheme, demand is strong and in the short and medium term could outstrip any additional planned capacity. Carriers are seeking additional new locations in the Indian Ocean (such as Mauritius), compounding Durban’s challenges.

**Figure 2.12 Ratio of current demand to reported capacity**

_Source:_ AICD Ports Database,2008 28
**Table A1. Profile by socioeconomic group, 1999, 2006 and 2011**

||1999||||2006||||2011||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**<$2.5**|**$2.5-$5**|**$5-$10**|**$10+**|**<$2.5**|**$2.5-$5**|**$5-$10**|**$10+**|**<$2.5**|**$2.5-$5**|**$5-$10**|**$10+**|
|Share of the population|77.6|16.3|4.3|1.7|23.4|43.9|27.1|5.6|9.6|45.6|38.6|6.2|
|_Demographics and location:_|||||||||||||
|Household size (no. of members)|3.8|3.1|3.5|2.9|4.2|3.6|3.0|2.6|4.3|3.5|3.1|2.2|
|Household depend. rate (<14 & >65)|71.3|49.4|56.4|60.2|81.2|64.5|50.4|35.5|83.0|65.7|45.6|25.2|
|Household depend. rate (>65)|21.3|19.3|15.3|24.2|25.9|24.3|20.7|12.5|25.0|29.0|17.3|9.4|
|Household depend. rate (< 14)|49.9|30.1|41.0|36.0|55.2|40.1|29.7|23.0|58.0|36.7|28.2|15.8|
|Rural|66.5|47.1|47.4|78.4|63.9|59.5|44.2|41.0|83.9|68.1|40.0|23.9|
|_Labor market (household level):_|||||||||||||
|Household empl. rate|63.8|62.2|70.6|60.9|61.6|62.7|61.9|68.5|72.7|66.2|67.0|69.7|
|Household empl. rate in agriculture|35.9|20.8|27.6|37.2|37.6|29.1|18.8|14.7|53.4|37.4|18.9|10.3|
|Household empl. rate in manufacturing|9.3|13.7|18.2|5.2|4.0|5.9|5.9|6.7|2.2|4.4|6.6|5.1|
|Household empl. rate in services|18.6|27.7|24.9|18.5|20.0|27.7|37.2|47.2|17.1|24.4|41.5|54.4|
|_Education - share hh members (15+):_|||||||||||||
|Primary education|22.0|13.5|12.9|21.3|30.8|16.8|10.0|6.1|39.5|21.1|8.2|3.5|
|Secondary education|61.8|55.6|54.4|51.1|55.9|59.9|54.7|45.9|48.6|58.0|57.8|33.9|
|Tertiary education|7.3|20.5|23.5|18.4|4.8|12.7|23.9|37.5|3.0|7.9|24.4|47.7|
|_Share of households receiving:_|||||||||||||
|Pensions|9.2|14.0|8.4|4.1|26.5|22.9|19.3|14.7|27.8|30.5|22.7|15.2|
|Social benefits|2.2|2.9|3.1|0.0|13.0|10.1|9.4|7.4|11.9|14.8|10.9|10.0|
|Wages|29.8|45.9|46.6|13.3|34.2|38.5|45.9|50.8|27.5|33.7|54.1|67.1|
|Remittances|..|..|..|..|15.5|21.3|26.7|38.1|16.0|24.6|26.3|33.7|

_Regions:_ 30
period, we only use those cases that are initiated in and after June 2008 until September 2009, net of anti-dumping duties that were removed during the same period. In other words, we only measure the change in anti-dumping duties during the two-year period, and we are not capturing the level of anti-dumping for each of the two years. This is an important point, because many anti-dumping duties in 2008 and 2009 are due to cases ...led in the 1990s. As long as these duties were not removed from the second quarter of 2008 onward, they do not a¤ect the change in level of protectionism. Only those new cases and the removal of old duties are factored in the calculations.

Table 2 presents some summary statistics on the countries that have added anti-dumping duties since the second quarter of 2008.<sup>7</sup> For the most part, changes in anti-dumping duties only a¤ect less than 1% of imports, ranging from US$8.5 billion in the EU to US$350 thousand in Chile. Nevertheless, given that some countries cannot unilaterally increase their tari¤s without violating WTO agreements, AD may well be one of those few legitimate channels to increase trade protection during the crisis period.

# 4 Results

Table 3 presents the OTRIs and their changes from 2008 to 2009. Four sets of results are presented for each country. First, is the calculation of the OTRI of each country based on its MFN tari¤s (OTRI_M). Next, is the calculation of the OTRI based on bilateral tari¤s of each country with its trading partners. Here we have two versions –one uses import demand elasticities directly from Kee, Nicita and Olarreaga (2008) that are country and product speci...c, but common across trad-

> 7 In addition to the 13 countries listed in Table 2, Global Anti-dumping database also have information for 5 more countries of the 135 present in our dataset: Pakistan is not included because we have no data on its 2009 tari¤ schedules; we also have no trade ‡ow data for South Korea and South Africa at tari¤ line level; we fail to match the AD data with trade data for Indonesia and Peru due to tari¤ reclassi...cation.

10