rafmacalaba/datause-encoder-data huggingface dataset:
activities prolonged the life **of the agreement** by perhaps six months. They also resulted in substantial losses to London metals traders,<sup>12</sup> and, for a time, called into question the integrity of the London Metal Exchange (the LME), the major international market for non-ferrous metals.

Even if the 6th ITA had been adequately financed and the BSM had confined himself to spot market support, the agreement would have run into much more serious problems than those experienced by the first **five ITAs.** Figure 1 shows the ti;. price more than doubling relative to the World Bank metals and minerals prices index during the latter half of the seventies."3 This rise in prices was the result of declining tin content in the increasingly exhausted Malaysian alluvial deposits, Malaysia being the largest tin producer at that time. During this period, the price was generally above the stabilization range and this resulted in the ITA support range chasing the market price upwards. The consequence was that when metals prices peaked in 1980, the ITC was committed to a higher range than would ever have been contemplated five years earlier. At the same time, the rapid expansion of low cost tin production in Brazil (not a member of the ITA) combined **with the** capture of the beverages can market by aluminum resulted in a fall in the sustainable tin price with the consequence that the nominal dollar tin price is now little different from that in the mid nineteen seventies, prior to the major rise in prices. So although the immediate cause of the breakdown of the 6th ITA was insolvency due to futures market operations, the fundamental causes were lack of finance and a change in underlying market fundamentals.

**12** Kestenbaum (1991) chronicles their battle for compensation.

13 Sources: tin price - **World Bureau** of Metal Statistics, _World Metal Statistics_ (Ware, Herts., various issues); metal and minerals index - Commodity Policy and Analysis Unit, International Trade Division (Washington D.C.).

15
- Xe -

**_Uies_** I.C.19 LAND US UNDER POPULATION. INCOME AND WAGE GROWTH. 2015-19

<!-- Start of picture text --> Vitt, of Aem<br>sdo - ILO - ~ ~ ~ ~ ~ MII Lgs<br>n.0<br>VW -<br>10.0-<br>&o -<br>7.0<br>4.0-<br>3.0<br>2.0-<br>120a0 s0o IO 200<br>C Mm GM? C.4F/Kgl<br>0 ~~.ronnhoI * ~~Annual 0 Follow<br><!-- End of picture text -->
# **Figure 6. Notre Dame GAIN indicators of vulnerability to climate change**

<!-- Start of picture text --> A. Climate change exposure by region<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>55<br>50<br>45<br>40<br>35<br>30<br>EAP ECA LAC MENA SA SSA<br>Note: Regional median per period in blue. 25 th and 75 th percentiles are bottom<br>and top of box, respectively<br>B. Vulnerability index, 1995‐2017<br>60<br>55<br>50<br>45<br>40<br>35<br>Note: Country median per year. 75th and 25th percentiles in dotted lines<br>D. Readiness index, 1995‐2017<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>Notre Dame exposure index (0‐100, high)<br>Notre Dame Vulnerability Index (0‐100, high)<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>Notre Dame readiness index(0 ‐100, high)<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Note: Country median per year. 75th and 25th percentiles in dotted lines

- C. Vulnerability index by region

<!-- Start of picture text --> 60<br>55<br>50<br>45<br>40<br>35<br>30<br>EAP ECA LAC MENA SA SSA<br>Note: Regional median per period in blue. 25 th and 75 th percentiles are bottom and<br>top of box, respectively<br>Notre Dame Vulnerability Index (0‐ 100, high)<br>1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17<br><!-- End of picture text -->

- E. Readiness index by region

<!-- Start of picture text --> 65<br>60<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>EAP ECA LAC MENA SA SSA<br>Note: Regional median per period in blue. 25 th and 75 th percentiles are bottom and<br>top of box, respectively<br>Notre Dame readiness index (0‐100, high)<br>1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17<br><!-- End of picture text --> 31
# Bank Balance Sheets 1993-2000 Averages for 17 Countries (“cof GDP)

60) <!-- Start of picture text --> ——<—————siarz<br>1993 2000 1993 2000<br>Assets Liabilities<br><!-- End of picture text --> <!-- Start of picture text --> 1993 2000<br>Liabilities<br>Lreditto Pub. Enter.<br>O Other<br><!-- End of picture text --> 8 Creditto Pvt. Sector & Credit to Goy, O CentralBank Liab. Mm Foreign
69 Table A4: Imports of Products Subject to Import Bans

|Year|Import share|Imports(in MM USD)|
|---|---|---|
|2017|17.18%|2,544|
|2018|18.64%|2,873|
|2019|15.69%|2,088|
|2020|12.41%|1,436|
|2021|10.19%|1,444|

_Notes_ : For each year, the table displays the total import value of the set of HS8 products whose imports were banned at some point starting in 2020. This variable is shown both in absolute terms (in MM USD) and as a % of total imports. The data comes from Sri Lanka’s Extraordinary Gazettes and the S&P Global Market Intelligence’s Panjiva data platform.

Appendix Table A5 displays pre-ban and post-ban summary statistics for some of our key variables of interest. Some of the ban’s effects are apparent even in these basic statistics: comparing the pre-ban to the post-ban period, we see that the average value of fertilizer imports plummeted from 2 million USD to only 491 thousand USD per importer firm per year. Similarly, the average value of agricultural exports fell from 1.62 million USD to 1.53 million USD per agricultural exporter firm per year.

Panel B of Appendix Table A5 focuses on the evolution of crop yields over time, as estimated by our remote sensing methodology. Again, we see large decreases in crop yields between the pre-ban (2021) and post-ban (2022) periods: the average rice yield across DSs falls from more than 4,000 kg/ha to less than 3,000 kg/ha, while average tea yields fall from 1,644 kg/ha to 1,404 kg/ha.

# **B Details on Estimation of Stylized Facts**

## **B.A Stylized Fact 1**

In Section IV.A, we estimate the fertilizer import ban’s effects on fertilizer imports through stateof-the-art dynamic DID regressions following De Chaisemartin and d’Haultfoeuille (2022). The regressions are estimated on a panel of HS8 products at the monthly level, with the treatment group consisting of fertilizers who had their imports banned from May 2021 onward, and the control group consisting of non-banned products. We exclude from the analysis products whose imports were banned at any point prior to May 2021. The estimating equation is: where _c_ indexes HS8 products and _t_ indexes time periods (months), _yct_ is the outcome variable, _ωt_ and _ωc_ are time and product fixed effects, respectively, and _εct_ is an error term. Our main variable of interest is _ban_<sup>_τ_</sup> _ct_<sup>, a dummy variable indicating whether an import ban on product</sup><sup>_c_was first imposed</sup>
# WPS3598

## **Quantitative Analysis of Crisis: Crisis Identification and Causality**

### Yoichiro ISHIHARA<sup>i</sup> World Bank

Email: yishihara@worldbank.org

### **Abstract**

Studies use different conceptual and operational definitions of crises. The different crisis identifications can lead to inconsistent conclusions and policy formulation even if the same analytical framework is applied. Also, most studies focus on only a few types of crises. This narrow focus on crises may not capture the multidimensionality of crises. Seven crisis types are analyzed; namely, (i) liquidity type banking crises, (ii) solvency type banking crises, (iii) balance of payments crises, (iv) currency crises, (v) debt crises, (vi) growth rate crises and (vii) financial crises. Crisis data are collected from 15 emerging economies in 1980-2002 on a quarterly basis. The crisis identification exercise finds that multidimensionality in which different crisis types occur in short-periods is one of the most important characteristics of recent crises. Further, Granger causality tests in five Asian economies (Indonesia, the Republic of Korea, Malaysia, the Philippines and Thailand) find that currency crises tend to trigger other types of crises, and therefore exchange rate management is essential.

### World Bank Policy Research Working Paper 3598, May 2005

_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 quick ly, 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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> i Ishihara, World Bank Office Jakarta. ¥Comments from Professor Shigeru Otsubo, Professor Hiroshi Osada and Associate Professor Yumiko Okamoto at the Department of International Development at the Graduate School of International Development at Nagoya University are gratefully acknowledged. Comments on this article should be addressed to Yoichiro Ishihara (yishihara@worldbank.org).

1
# **Table A8.2: Multiyear analysis for countries with a medium QFD (1-2 percent of GDP in the reference year)**

QFD as percent of GDP <!-- Start of picture text --> QFD as percent of GDP QFD as percent of revenues<br>0.0% 50.0% 100.0% 150.0% 200.0% 250.0%<br>0.0% 0.5% 1.0% 1.5% 2.0% 2.5%<br>MEDIUM QFD AND IMPROVING<br>MEDIUM QFD AND IMPROVINGBurkina Faso_2011Burkina Faso_2012 1.2%1.4% Burkina Faso_2011Burkina Faso_2012 64.0%70.9% H More than 50 percent of units generated are from hydro<br>Burkina Faso_2013 1.0% Burkina Faso_2013 50.4%<br>Burkina Faso_2014 1.0% Burkina Faso_2014 47.5%<br>MEDIUM QFD AND STEADY MEDIUM QFD AND STEADY<br>Burundi_2012 0.9% H Burundi_2012 119.1%<br>Burundi_2013 1.0% Burundi_2013 139.7%<br>Burundi_2014 1.0% Burundi_2014 154.5%<br>Togo_2011 1.7% Togo_2011 35.6%<br>Togo_2012 1.7% Togo_2012 34.8%<br>Togo_2013 1.6% Togo_2013 31.9%<br>Mauritania_2012 1.6% Mauritania_2012 80.9%<br>Mauritania_2013 1.5% Mauritania_2013 73.5%<br>MEDIUM QFD AND MIXED MEDIUM QFD AND MIXED<br>Mali_2011 1.0% Mali_2011 51.8%<br>Mali_2012 1.3% Mali_2012 70.4%<br>Mali_2013 1.4% Mali_2013 72.1%<br>Mali_2014 1.3% Mali_2014 61.7%<br>Sudan_2011 0.8% Sudan_2011 76.5%<br>Sudan_2013 1.5% H Sudan_2013 220.1%<br>Sudan_2014 1.4% Sudan_2014 233.3%<br>MEDIUM QFD AND WORSENING MEDIUM QFD AND WORSENING<br>Cape Verde_2011 1.3% Cape Verde_2011 29.5%<br>Cape Verde_2012 1.6% Cape Verde_2012 34.1%<br>Côte d'Ivoire_2013 1.5% Côte d'Ivoire_2013 62.8%<br>Côte d'Ivoire_2014 1.9% Côte d'Ivoire_2014 73.6%<br>Zambia_2011 0.9% Zambia_2011 38.8%<br>Zambia_2012 1.1% H Zambia_2012 47.0%<br>Zambia_2014 1.2% Zambia_2014 47.3%<br>Swaziland_2011 0.9% Swaziland_2011 30.8%<br>Swaziland_2012Swaziland_2013 1.0% 1.2% H Swaziland_2012Swaziland_2013 37.0%42.6%<br>Swaziland_2014 1.2% Swaziland_2014 39.8%<br><!-- End of picture text --> _Source_ : World Bank staff calculations using utility data.

97
25 only). Second, we used some non-nested tests to select between "competing" models: a spreads only model vs. a flows only one. Appendix 7 presents details of the model selection procedures, but we find no compelling evidence for the more general model, except for the cases of Colombia and Mexico, and probably in Brazil. Future versions of this paper that allow for different specifications should take this into consideration.

# b) _What Accounts for Real Interest Rate Variability ?_

Looking at the real interest rate variance decomposition (Appendix 6), several things are worth noting. First, this set of variables explains reasonably well real interest rate variability in the sample countries. In Argentina, Mexico and Peru, shocks to the external capital markets (external spreads) were the main factor explaining real interest rate variability.

The second most common source of real interest rate variability was the real exchange rate. While in Argentina the importance of this variable quickly fades away, in Mexico, Peru and Brazil its relative weight is stable and of significant magnitude.

In Chile and Colombia the "own" shocks to interest rates are the most important factor explaining interest rate variability. In these two cases, this variable can be considered largely exogenous with respect to the rest of the variables in the model. This finding might be related to the fact that these two countries imposed some type of capital controls during this period.

# _c) Determinants of Real Exchange Rate Variability_

Variance decomposition of real exchange rates (Appendix 6) shows that in most countries, shocks to the external capital markets are the main explanatory variable (besides the own shocks) of real exchange rate variability. This contrasts with the Roldos-Hoffmaister (1997) result according to which domestic shocks are the main drivers of real exchange rate variability.

# VI. **Conclusions**

Based on the country-specific Generalized Variance Decompositions we found that during 1992-1998 output variability in most Latin American countries was associated with domestic factors. These results coincide with other work (Hausman-Gavin (1995), Hoffmaister-Roldos (1997) and Ahmed (1999). In four of the eight countries (Brazil, Chile, Colombia, Mexico) the joint effect of shocks to domestic factors (real interest rates and real exchange rates) on output variability was greater than the combined effect of shocks to external variables (terms of trade and external spreads). In Argentina, Brazil and Mexico, shocks to external spreads played a major role, while in Chile and Colombia real interest rates shocks were the principal sources of output variability. In the remaining three countries, Ecuador, Peru and Venezuela, terms of trade shocks were
They find that linear regression would overstate the income gain to migration by 27 to 35 percent, which is consistent with migrants being positively selected on unobservables (the authors find positive selection on observables). Using non-experimental methods like differencein-differences or propensity score matching reduces this overstatement a little, but still results in an overstatement of around 20 percent in the income gain from migration. The only nonexperimental method that gets close to the experimental estimate is a good instrumental variable.

Migration policies typically limit which other family members can migrate along with the principal migrant, often restricting this just to the spouse and dependent children of the migrant. This policy rule can be used alongside a migration lottery to deal with a second form of selectivity – selectivity into which household members move and which remain in the home country. Gibson et al. (2009) use the combination of the policy rule and the lottery to look at the impact of migration on remaining household members in Tonga, finding evidence that remaining household members appear worse off in the short-term, with lower per capita incomes and consumption.

The above studies relied on the use of administrative data to track winners and losers in the Pacific Access Category. It requires considerable effort and enlightened policymakers in order for researchers to obtain access to such data. An alternative approach would be to attempt to locate households with lottery winners and losers in a survey in the migrant-sending country. Gibson et al. (2010) provide an example of this approach, using a representative survey of Samoan households to identify households which entered the Samoan Quota lottery, which allows 1,100 Samoans to migrate to New Zealand each year. Since Samoa’s population is small, the lottery had been in place for several years, and there were 5,000-7,000 applications for the lottery each year, a random sample of households was able to identify sufficient numbers of lottery winners and losers for experimental analysis of the impact of migration through this category. In contrast to the Tongan results, the Samoan experiment finds migration to have reduced poverty and increased household incomes among remaining household members. However, there is suggestive evidence that this positive effect may be short-lived, with remittances and home production falling with time spent abroad of migrant members.

5
**Table S4: Effects of Insurance Enrollment on Expenditures Among Parents**

||All Providers|Pharmacy|Public Facilities|Private Facilities|
|---|---|---|---|---|
|Panel A: Ever Spend|(1)|(2)|(3)|(4)|
|Parent Enrolled|-0.074|-0.080|-0.017|-0.035|
||(0.057)|(0.062)|(0.012)|(0.072)|
|Constant|1.054***|1.175***|0.012|-0.419*|
||(0.191)|(0.201)|(0.035)|(0.222)|
|Observations|1,614|1,614|1,614|1,614|
|R-squared|0.014|0.024|0.000|0.055|
|Panel B: Inv. Hyperbolic Expenditures|(1)|(2)|(3)|(4)|
|Parent Enrolled|-0.417|-0.465|-0.041|-0.150|
||(0.425)|(0.421)|(0.049)|(0.447)|
|Constant|4.413***|5.519***|0.081|-2.772**|
||(1.370)|(1.334)|(0.145)|(1.399)|
|Observations|1,614|1,614|1,614|1,614|
|R-squared|0.036|0.036|0.005|0.061|

_Notes:_ The sample is all parents with at least one child aged 15 and underat baseline (N=1614). Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status. The dependent variable in Panel A is whether or not the child had any health expenditures at various providers over the past year. The dependent variable in Panel B is the inverse hyperbolic sine of expenditures at various providers over the past year. Note that this specification could not be estimated for EMP expenditures because the sample mean for parents was zero. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

4
an idiosyncratic error term; we use the sandwich variance estimator (sometimes referred to as the Huber- or Huber-White robust estimator), which allows for correlation of the residuals within clusters (i.e., PSUs) and is therefore robust to violations of the assumptions of identically and independently distributed residuals.<sup>25</sup>

# _3.2.1  Dependent variables_

We examine the following measures of wellbeing: real monthly per capita food consumption; per capita daily calorie intake; per capita daily protein intake; household dietary diversity; and expenditure shares by broad food groupings. Finally, we examine the demand for wheat products (per capita intake) as a result of the price increases. Our priors are that the large increases in wheat prices will be associated with reductions in wellbeing in terms of total food consumption, total calorie intake, and diversity. In terms of wheat and protein consumption, it is not clear whether the substitution effect or income effect will dominate. When the price of one good increases, the relative price of the other goods declines, and the substitution effect implies that consumption of the relatively cheaper other goods will increase. The countervailing income effect is driven by the fact that as the price of one good increases, real income declines, which results in decreased consumption of normal goods. (For example, as wheat prices in Afghanistan increased, the price of lamb relative to wheat declined; this would suggest more consumption of meat. However, the increase in wheat prices also led to a decline in real income, suggesting less consumption of meat. Therefore the overall effect is an empirical question.) We also expect a change in the composition of food expenditures, with a general movement towards cheaper, lower quality foods.

Total food consumption is an informative measure of wellbeing in itself, but is also important because it is a core component of poverty indicators. To calculate the value of total monthly consumption on food (in Afghani), we map district price data to quantity data from the household consumption module. (Households are asked for the quantity of foods consumed over the past seven days; these quantities are multiplied by 4.2 to get monthly values.) Food consumption data include food bought, produced or obtained through other methods, e.g., food aid, gifts. Prices were matched by month, item, and district. Since not all food items were available in all district markets at all times of the year, we imputed the

> 25 See Kauermann and Carroll (2001) for details on the properties of this estimator.

10
somewhat slower pace than in periods 1-2. However, we also find a higher rejection rate in Test **I** and a somewhat reduced rejection rate in Test 2. On the whole, these results tend to suggest that the widening band makes verification more difficult.

In contrast, for periods 4-7, characterized **by** a currency basket and much wider bands, none of the estimates in Table **lb -** whether restricted or unrestricted **-** appears close to the announced values even after a reasonably large number of observations. Precision is much poorer than in the earlier periods, although the restricted estimates are in general substantially more precise than the unrestricted ones (see the last two columns in the table). In any case, both sets of estimates appear clearly biased; indeed, some point estimates are even negative. As a result, while Test 2 generally rejects the null of zero weights, Test **1** also rejects the announced weights in most samples, and this applies both to the restricted and unrestricted estimates.

These results can be more easily understood with the help of Figure **3,** which presents scatter plots of the observed exchange rate of the Chilean peso against the central parity. In the first three sub-periods, the points cluster along the 45-degree line, reflecting a relatively close match between the peso and the central parity. As the band widens in the last four periods, the peso can fluctuate further away from the central parity. This is particularly apparent in periods **5-7,** whose scatter plots display little or no clustering along the 45-degree line. Thus, it is not surprising that in the first three periods the basket weights defining the central parity can be estimated fairly precisely from the observed exchange rate data, while this is not the case in later periods.<sup>2 5</sup>

> 2' The scatter diagram, along with Figure **1,** also provides some clues for the relatively poorer verifiability of the third period vis-h-vis the first two. In the early part of the third period (approximately **_50_**

> observations), the exchange rate was practically pegged to the upper part of the band, but starting in early

> **1990** the peso started appreciating until it finally reached the lower band. This marked break in the

**_25_**