rafmacalaba/datause-encoder-data huggingface dataset:
index (negative slope). However, only in the latter case the slope is statistically significant and, even then, the progress in conflict countries falls below the rate of improvement in the control group. On the other hand, ICRG’s index on law and order does show a marked and significant rate of progress in conflict countries, which is much larger than that in the control group. It seems, then, that in the aftermath of civil war, while political rights are slow to advance, police and judicial systems improve at an accelerated rate.

Regarding demographic variables, in the aftermath of war there is a continuation of the demographic transition in conflict countries: The old dependency ratio presents an increasing trend, while the young dependency ratio shows a declining one. The femalemale ratio, after increasing during the war, exhibits a statistically significant declining trend in the aftermath, revealing a gradual recovery of the male population from its losses during the war. Regarding the comparison with the control group’s behavior, the trend of the young dependency ratio is similar between the conflict and control groups. On the other hand, the trend slopes in the old dependency and female-male ratios are larger (in absolute value) in the conflict group than in the control group, with the difference approaching statistical significance.

Finally, regarding the conflict indicator, the incidence of terrorist attacks decreases significantly in the aftermath of internal wars, as implied by its estimated negative trend. When more complex, nonlinear behavior is allowed (not shown in the table) terrorist attacks seem to follow a quadratic trend with some increase early in the aftermath of war and a subsequent marked decline. The end of the civil war appears to eventually lead to pacification of other types of internal strife.

# **IV. Conclusions**

War has devastating effects, and its aftermath can be immensely difficult. Nevertheless, when the end of war marks the beginning of lasting peace, recovery and improvement are feasible realities.

This paper has not attempted to measure the cost of war in all its human and material dimensions; however, it finds evidence on the negative consequences of war in 13
|Sierra Leone - Reproductive and Child Health II Project - Second Ad|dIrina Aleksandra Nikolic|
|---|---|
|Sierra Leone - Reproductive and Child Health II Project - Second Ad|dIrina Aleksandra Nikolic|
|AGRICULTURE PRODUCTIVITY AND NUTRITION IMPROVEMENT PRO|Talaibek Torokulovich Koshmatov|
|AGRICULTURE PRODUCTIVITY AND NUTRITION IMPROVEMENT PRO|Talaibek Torokulovich Koshmatov|
|AGRICULTURE PRODUCTIVITY AND NUTRITION IMPROVEMENT PRO|Talaibek Torokulovich Koshmatov|
|AGRICULTURE PRODUCTIVITY AND NUTRITION IMPROVEMENT PRO|Talaibek Torokulovich Koshmatov|
|AGRICULTURE PRODUCTIVITY AND NUTRITION IMPROVEMENT PRO|Talaibek Torokulovich Koshmatov|
|AGRICULTURE PRODUCTIVITY AND NUTRITION IMPROVEMENT PRO|Talaibek Torokulovich Koshmatov|
|AGRICULTURE PRODUCTIVITY AND NUTRITION IMPROVEMENT PRO|Talaibek Torokulovich Koshmatov|
|AGRICULTURE PRODUCTIVITY AND NUTRITION IMPROVEMENT PRO|Talaibek Torokulovich Koshmatov|
|AGRICULTURE PRODUCTIVITY AND NUTRITION IMPROVEMENT PRO|Talaibek Torokulovich Koshmatov|
|AF GPE to Haiti Education for All Project - Phase II|Juan Baron|
|AF GPE to Haiti Education for All Project - Phase II|Juan Baron|
|AF GPE to Haiti Education for All Project - Phase II|Juan Baron|
|AF GPE to Haiti Education for All Project - Phase II|Juan Baron|
|AF GPE to Haiti Education for All Project - Phase II|Juan Baron|
|AF GPE to Haiti Education for All Project - Phase II|Juan Baron|
|Second Shared Growth Credit|Johannes Herderschee|
|Second Shared Growth Credit|Johannes Herderschee|
|Pernambuco Equityand Inclusive Growth DPL|Joana C. G. Silva|
|Pernambuco Equityand Inclusive Growth DPL|Joana C. G. Silva|
|Pernambuco Equityand Inclusive Growth DPL|Joana C. G. Silva|
|Pernambuco Equityand Inclusive Growth DPL|Joana C. G. Silva|
|China: Gansu Rural-Urban Integration Project|YangChen|
|China: Gansu Rural-Urban Integration Project|YangChen|
|China: Gansu Rural-Urban Integration Project|YangChen|
|China: Gansu Rural-Urban Integration Project|YangChen|
|China: Gansu Rural-Urban Integration Project|YangChen|
|China: Gansu Rural-Urban Integration Project|YangChen|
|China: Gansu Rural-Urban Integration Project|YangChen|
|China: Gansu Rural-Urban Integration Project|YangChen|
|China: Gansu Rural-Urban Integration Project|YangChen|
|China: Gansu Rural-Urban Integration Project|YangChen|
|China: Gansu Rural-Urban Integration Project|YangChen|
|BJ PRSC 9 PovertyReduction Support Cdt.|Boulel Toure|
|ImprovingLivestock Production and MarketingProject - A Pilot|Stephane Forman|
|Nigeria Post-Compliance I EITI|Masami Kojima|
|Nigeria Post-Compliance I EITI|Masami Kojima|
|AFCC2/RI-Central African Backbone SOP5|Marc Jean Yves Lixi|
|AFCC2/RI-Central African Backbone SOP5|Marc Jean Yves Lixi|
|AFCC2/RI-Central African Backbone SOP5|Marc Jean Yves Lixi|
|AFCC2/RI-Central African Backbone SOP5|Marc Jean Yves Lixi|
|AFCC2/RI-Central African Backbone SOP5|Marc Jean Yves Lixi|
|AFCC2/RI-Central African Backbone SOP5|Marc Jean Yves Lixi|
|AFCC2/RI-Central African Backbone SOP5|Marc Jean Yves Lixi|
|AFCC2/RI-Central African Backbone SOP5|Marc Jean Yves Lixi|
|AFCC2/RI-Central African Backbone SOP5|Marc Jean Yves Lixi|
of the variables we surveyed in each of three categories. Except for ATMs, debit cards, and POS terminals, all elements were asked separately for each institutional category.<sup>8</sup> Since many low-income individuals get deposit services through institutions which are not commercial banks (henceforth, we refer to bank-like institutions which are not commercial banks as non-bank financial institutions or non-banks for short), we ask for most of the data disaggregated into commercial banks as well as a number of non-bank categories.<sup>9</sup> This addition represents a major advantage of this study over others that have come before it. One difficulty in implementing this approach is that there are many types of financial institutions and each can be different from country to country. India, for instance, has commercial banks, Area Banks, Rural Banks, various types of Urban and Rural Cooperatives, and many other types of bank-like financial institutions, each of which differs in subtle ways from similar institutions in other countries.<sup>10</sup> To make the cross-country comparison of the different types possible and meaningful, regulated institutions are divided into four main categories and a catchall “other” category:

- 1) Commercial banks - banks with a full banking license. In some countries, majority government/state owned banks are included in this category to the extent that they perform a broad set of commercial banking functions rather than a specialized development role.

- 2) Institutions with a mutual ownership structure (or mutually owned financial institutions: _MOFIs_ **_)_** , such as cooperatives, credit unions or mutual banks.

- 3) Specialized state owned financial institutions (SSFIs) or extensions of the government whose main purpose is to lend to support economic development and/or to provide savings, payment, and deposit services to the public. This group includes postal banks, government savings banks, SME lending facilities, agriculture banks, development banks, etc.

> 8 World Bank (2008b) presents more detailed data on the status of national payment and securities settlement systems worldwide.

> 9 In CGAP Occasional Paper No. 8, (2004) the authors refer to these non-bank financial institutions as AFIs for “Alternate Financial Institutions”.

> 10 The survey asked respondents to list the types of retail financial institutions active in their jurisdiction. Table 1 gives a fictitious example of the variety of institutions that might be reported (the table was used as an example for respondents.)

7
19 Although all the numbers in the right column of Table 7 suggest a low rate of stability of the groups participating in ATP 1, this can be explained by the relatively early stage of the program (second year), and by the inappropriate procedure for selecting the farmers in the various groups. However, the high level of farmer and group substitution in ATP I is associated with the high transaction costs of establishing new groups and re-assigning of new farmers to existing groups. These transaction costs are to be borne by the extensionists who assemble the groups. These factors should be taken into account when analyzing ATP l's performance.

Table 7: Continuation rate of groups participating in ATP 1 between 1995 and 1996.

|**Region**|**Number of Groups**|**Groups Continuing**|**% of Groups Continu-**|
|---|---|---|---|
|Al|43|8|18.6|
|A2|101|22|21.8|
|B3|94|35|37.2|
|B5|32|3|9.3|
|C6|41|2|4.8|
|Total|311|69|22.2|

Source: Garcia (1996) Recent data for seven ATPI extension-providing companies allows a three-year perspective on the stability of producer-participation (INTA, 1998). Of the total 15,587 producers contracted for 1997/98, 26% are three year veterans, 39% are two years veterans, and 35% are new participants in the program. These figures indicate a better long-term stability of producer participation. Indeed, such analysis should be done, using long-term and aggregated data in order to provide sensible results.

In calculating the cost of extension provision in ATP 1, we use actual data available for technical assistance programs in INTA (Estrada-Rizo and Garcia, 1997). These estimates are presented in Table 8.

It appears from Table 8 that the annual average cost per extensionist in the technical assistance programs (ATPb and ATP 1) is $3612. Figure 6 suggests that about 70% of these costs
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In practice model (5) will be estimated first by Ordinary Least Squares (OLS) in order to obtain the residuals _u_ ˆ _it_ , which are then used in place of _uit_ in model (8). In the second stage, model (8) is estimated using the pooled Bernoulli quasi-Maximum Likelihood Estimator (QMLE), which corresponds to maximizing the pooled probit log-likelihood.<sup>11</sup> Because of the panel form of the data one may want to allow for any form of serial dependence across _t_ .<sup>12</sup> Finally, the two-stage estimation procedure should correct for biases in the standard errors. Bootstrap methods are used in the empirical application.

The parameter estimates can then be used to perform several specification tests. In particular, rejection of the null hypothesis ρ = 0 will indicate that _yit_ is endogenous; while the joint significance of the ξ parameters will confirm that the observed time-varying explanatory variables and the cluster-specific unobserved effect are correlated.

All parameter estimates are scaled by the same factor which can be calculated as follows:

> where φ( ) . is the density of the normal distribution (Papke and Wooldridge, 2008). This scale factor is unique and corresponds to the scale effect averaged across all time periods and all cross-sections. Hence the average partial effects of the explanatory variables in **x** it (that is, the partial effects averaged across the population) are obtained by multiplying the scale factor from (9) by the corresponding estimated parameters **β**<sup>ˆ</sup> .

# **6. Empirical analysis**

In what follows we describe the estimation of model (8) using data for 405 clusters over four years (1993-94, 1998-99, 2003 and 2008). The dependent variable is the average proportion of girls aged 5-15 attending school in the community/cluster.

> 11 The Bernoulli log-likelihood function is given by _li_ ( _b_ ) ≡ _yi_ log [ _G_ ( **x bi** ) ] + ( 1 − _yi_ ) log [ 1 − _G_ ( **x bi** ) ] for 0 < _G_ ( ) . < 1; see Papke and Wooldridge (1996).

> 12 This model can be estimated using the _glm_ command in Stata and the _cluster_ option can be used to obtain standard errors robust to any form of serial correlation.

12
http://www.whitehousedrugpolicy.gov/publications/economic_costs/economic_costs. pdf

- Fajnzylber, Pablo, Daniel Lederman, and Norman Loayza (2002). “What Causes Violent Crime?” European Economic Review, 46(2002), 1323-1357.

- Fryer, Jr., Roland G., Paul S. Heaton, Steven D. Levitt, and Kevin M. Murphy (2005). “Measuring the Impact of Crack Cocaine.” NBER Working Paper 11318.

Godfrey, Christine, Gail Eaton, Cynthia McDougall and Anthony Culyer (2002). “The economic and social costs of Class A drug use in England and Wales, 2000.” Home Office Research Study 249. Great Britain: Home Office Research, Development and Statistics Directorate (July). http://www.homeoffice.gov.uk/rds/pdfs2/hors249.pdf

- Godinho, Joana and Jaap Veen (2005).  “Drug policy and its impact on the HIV empidemic in Europe.” Draft policy note, The World Bank.

- Gossop, Michael, John Marsden and Duncan Stewart (2001).  “NTORS After Five Years:  The National Treatment Outcome Research Study.  Changes in substance use, health and criminal behaviour during the five years after intake.”  London:  National Addiction Centre.

http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsPolicyAn dGuidance/DH_4084908

- Grossman, Michael (2004). “Individual Behaviors and Substance Use: The Role of Prices.” NBER Working Paper 10948.

Gruber, Jonathan and Botond Köszegi (2001). “Is Addiction ‘Rational’? Theory and Evidence.” Quarterly Journal of Economics, November, 116(4), 1261-1303.

- International Crisis Group (2008). “Latin American Drugs I: Losing the Fight.” Latin American Report No. 25.

- Kugler, Maurice, Thierry Verdier, and Yves Zenou (2005). “Organized Crime, Corruption and Punishment.” Journal of Public Economics, 89(9/10), 1639-1663.

- Lee, Li Way (1993). “Would Harassing Drug Users Work?” Journal of Political Economy, 101(5), 939-959.

MacCoun, Robert and Peter Reuter (2001). _Drug War Heresies: Learning from Other Vices, Times and Places._ New York: Cambridge University Press.

- Mejía, Daniel, and Carlos Esteban Posada (2005). “Cocaine Production and Trafficking: What do We Know?” Unpublished Manuscript, Brown University.

Miron, Jeffrey (2003). “Do Prohibitions Raise Prices: Evidence from the Market for Cocaine.” Review of Economics and Statistics, August, 85(3), 522-530.

Miron, Jeffrey and Jeffrey Zwiebel (1995). “The Economic Case Against Drug Prohibition.” 22
**Table 5. New Commodity Exporters**

|Dependent variable:|Fiscal balance/GDP|Total revenues/GDP|Total|
|---|---|---|---|
||(%)|(%)|expenditures/GDP<br>(%)|
|Constant|-7.26***<br>(-7.98)|-7.14***<br>(-4.81)|-0.276<br>(-0.18)|
|Dummy fuel > 25%|4.80***|9.51***|5.06***|
|exports|(10.53)|(14.93)|(9.07)|
|Dummy ores and|0.414|0.507|0.462|
|metals > 10%<br>exports|(1.36)|(0.98)|(0.86)|
|Hard peg dummy|1.40***|-2.72**|-4.17***|
||(2.91)|(-4.57)|(-8.19)|
|Political stability|0.0609***|0.470***|0.416***|
|(0 to 100)|(4.59)|(20.62)|(17.63)|
|New commodity|-11.91*|2.17|13.83|
|exporter dummy|(-1.93)|(0.23)|(1.35)|
|Political stability *|0.176*|-0.0551|-0.232|
|NCE dummy|(1.77)|(-0.42)|(-1.59)|
|Time trend|0.0184|0.316***|0.279***|
|(=0 in 2000)|(0.74)|(7.09)|(5.94)|
|n|1,378|1,392|1,378|
|R-squared|0.136|0.300|0.232|
|RMSE|4.74|8.03|7.97|

_Note_ : Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. The dummy for being a new commodity exporter is equal to one in the first five years of fuel exceeding 25 percent 12
# **1. Introduction**

An increase in sea surface temperature is strongly evident at all latitudes and in all oceans. The scientific evidence indicates that increased sea surface temperature will intensify cyclone activity and heighten storm surges.<sup>8,9</sup> These surges<sup>10</sup> will, in turn, create more damaging flood conditions in coastal zones and adjoining low‐lying areas. The destructive impact will generally be greater when storm surges are accompanied by strong winds and large onshore waves. Tropical cyclone Sidr<sup>11</sup> in Bangladesh (November 2007) and cyclone Nargis<sup>12</sup> in the Irrawady delta of Myanmar (May 2008) provide recent examples of devastating storm‐surge impacts in developing countries.

Some recent scientific studies suggest that increases in the frequency and intensity of tropical cyclones in the last 35 years can be attributed in part to global climate change (Emanuel 2005; Webster et al. 2005; Bengtsson, Rogers, and Roeckner 2006). Others have challenged this conclusion; citing problems with data reliability, regional variability, and appropriate measurement of sea‐surface temperature and other climate variables (e.g., Landsea et al. 2006). Although the science is not yet conclusive (IWTC 2006: Pielke et al. 2005), the International Workshop on Tropical Cyclones (IWTC) has recently noted that “[i]f the projected rise in sea level due to global warming occurs, then the vulnerability to tropical 8 A sea‐surface temperature of 28o C is considered an important threshold for the development of major hurricanes of categories 3, 4 and 5 (Michaels, Knappenberger, and Davis 2005; Knutson and Tuleya 2004).

> 9 A rising sea level as thermal expansion and ice cap melting continue will be another contributory factor.  The most recent evidence suggests that sea‐level rise could reach 1 meter or more during this century (Dasgupta, et al. 2009; Rahmstorf 2007), although the likelihood of that magnitude of increase remains uncertain.

> 10 _Storm surge_ refers to the temporary increase, at a particular locality, in the height of the sea due to extreme meteorological conditions:  low atmospheric pressure and/or strong winds (IPCC AR4 2007). 11 According to Bangladesh Disaster Management Information Centre (report dated Nov 26, 2007)

3,243 people were reported to have died and the livelihoods of 7 millions of people were affected by Sidr (http://www.reliefweb.int/rw/RWB.NSF/db900SID/EDIS‐79BQ9Z?OpenDocument ).

12 In Mayanmar, 100,000 people were reported to have died and the livelihoods of 1.5 million people were affected by Nargis (http://www.dartmouth.edu/%7Efloods/Archives/2008sum.htm )
examine the presence of offsetting effects on either side of the mean treatment effect we compare the distribution of the unadjusted (original) percentile rank statistic to a uniform distribution. In Section 5, we apply both mean-adjusted and unadjusted heterogeneity tests to all of our outcome variables, using the results from our benchmark specification of the SCM.

# **4.  Data, Summary Statistics and Implementation**

## **4.1. Data**

The raw data we employ track Serbian imports for consumption at the transaction level for the period 2010-2013. The data were provided confidentially by the Serbian national customs authority. The Serbian customs agency processes goods at the level of individual import declarations (each declaration may contain one or more transactions). The primary fields of interest used in this study are the registration time and the clearance time for each declaration (which allow us to calculate the clearance time in hours per declaration), a numeric identifier for the importing firm, a Harmonized System (HS) 10-digit commodity code for each transaction, the country of origin, a field indicating what type of inspection the declaration was subject to, and a field indicating whether the declaration was cleared under a special customs clearance procedure. This latter code which we designate as ‘special clearance’ includes transactions of goods that were cleared under the IHC program, but also includes other special clearance codes.<sup>33</sup> Our outcome variables of interest, calculated from the raw data aggregated to the firmmonth level, are a) the (log) median time necessary for the firm to clear customs for its import shipments in a month, b) the (log of the) 75<sup>th</sup> percentile of customs clearance times in a month, c) the (log of the) 90<sup>th</sup> percentile of customs clearance times in a month, d) the rate of inspections of the firm’s import declarations in a month, and e) the (log) value of imports of the firm in a month. We use locations in the distribution of clearance time (percentiles), rather than moments of the distribution, and we apply the log function in order to reduce the month-to-month volatility.<sup>34</sup> Volatility greatly reduces computational speed as well as the performance of the SCM estimator,

> These offsetting effects across firms might lead the mean percentile rank statistic test to accept the null hypothesis of no effect of the IHC program on any firm. The tests for heterogeneous treatment could at the same time find evidence of program impact. 33 Other than the code that identifies transactions cleared under the IHC program, we are not privy to the meaning of these codes. Our use of a generic special code dummy presupposes that those codes offer information about firm types that is useful for matching treated and donor firms in the pre-treatment period. 34 The exception is the inspection rate, which we do not log.  For IHC-adopting firms, inspection rates are quite low throughout the sample and logging values quite close to zero seems inappropriate for inference.

22
Figure S3 shows COVID-19 shutdown effects, which may be compared with Figure S2 to get an idea where reductions were greatest. Viewing the world all at once results in less clear takeaways than individual nations, due to different orders of magnitude across countries. Figures 3-5 in the main text provide closeups of China, Europe, and the US within this figure, and provide for clearer intuition and takeaways.

# **A.4 COVID-19 Lockdown Stringency and NO** 2

A basic premise underlying some of our results is that COVID-19 lockdowns immediately caused declines in economic activity that were then measurable in NO2. In Figure S4, these declines are first clearly apparent in Hubei province (containing Wuhan) by simply comparing the twoweek difference before and after the first lockdown (Panel A). Similar outcomes are obvious subsequent to other quarantines, including in northern Italy (Panel B).

To underscore the fact that lockdowns lead to immediate, measurable declines in NO2, we compared the World Bank’s measure of COVID-19 lockdown stringency versus changes in NO2 for the countries in our sample.<sup>1</sup> Figure S5 provides a graphical depiction of the obvious negative correlation we found between stringency and NO2 VCDs in China, Italy, the US, and France. In each case, increases in stringency are associated with declines in NO2, while reductions in stringency are associated with increases, underscoring the scientific literature (Gkatzelis et al., 2021).

# **A.5 US Political Landscape and Collective Inaction**

There seems to be a geographic correlation in the United States between political affiliation and extent of COVID-19 lockdowns, as proxied by changes in NO2. There is good reason to think that there may have been less forceful shutdowns in Republican-leaning districts, and less adherence to lockdown orders specifically amongst those who also exhibit stronger support for the Republican Party between 2000 and 2016 (Bazzi et al., 2021). As a basic test of this “collective inaction” thesis, we regressed the percentage change in NO2 from 2019 Q2 to 2020 Q2 on the margin, of Democratic victory in the 2016 Presidential election at the county level. If the thesis is correct, we should obtain a negative sign on the effect, since this would mean more Democratic-leaning counties were associated with stronger lockdown measures. We obtain the following estimates (robust standard errors in parentheses),

> 1Availability of the stringency index: `https://ourworldindata.org/grapher/covid-stringency-index`

S3