rafmacalaba/datause-encoder-data huggingface dataset:
_Africa’s Macroeconomic Story_ higher commodity prices tend to appreciate the real exchange rate in resource-rich countries (IMF 2012a). On the broader issue of diversification, Papageorgiou and Spatafora (2012) contend that a lack of diversification in low-income countries increases their exposure to adverse external shocks. Their cross-country evidence shows that less diversified low-income countries tend to experience more volatility in their terms of trade and output. However, they do not find a systematic relationship between export diversification and growth.

# **_Policy responses to external shocks_**

Collier (2008) highlights differences in fiscal policy challenges facing resource-rich economies, as well as countries emerging from conflict. Policy makers in resource-rich economies need to manage volatile revenues and have a longer-term plan that takes into account declining revenues from resource depletion. On this point, Page (2009) notes that “in contrast to the unchecked spending in the past, windfalls from oil revenue are increasingly being saved”. For resource-scarce economies, Collier notes that over the past two decades policy makers have been gradually rectifying two inherited fiscal errors: 1. the accumulation of excessive fiscal deficits, resulting in unsustainable levels of indebtedness and high inflation; and 2. excessive reliance on trade taxes which amounted to the heavy taxation of export agriculture (p. 43). In post-conflict situations, policy makers are typically confronted with unsustainable fiscal positions, high inflation and an institutional framework that has many major shortcomings.

With regard to exchange rate regimes, a common view in the literature is that flexible regimes can play a stabilizing role to the extent that flexibility helps avoid overvaluation and facilitates adjustment to shocks. However, large exchange rate fluctuations are also a major source of instability. The empirical literature on the issue is mixed. It is difficult to find a robust relationship between exchange rate regimes and growth outcomes in developing countries (Rogoff et al 2004) or in Africa specifically (Masson and Pattillo 2005). Berg, Ostry, and Zettelmeyer (2008) find that longer growth spells tend to occur during periods that have fewer large exchange rate depreciations but the relationship breaks down when other variables are included in the analysis.

On the whole, most studies attribute the improved macroeconomic environment in Africa to avoiding major policy errors and responding to external shocks effectively. Or as put by an IMF (2008) study: “growth seems to have responded to the specific circumstances in each country in terms of the external environment it faced and the policy choices it made” (p. 42).

8
**Interestingly, TFP growth in most industries is attributable to** **_new entrants_ .** This is indicated by the results of the Foster et al (2001) decomposition method (see Figure 5), which shows that with the exception of a few industries (i.e. beverages, coke and refined petroleum, chemicals, basic pharmaceuticals, and motor vehicles manufacture) reallocation of capital and labor play a key role in productivity growth (which, given the structure of the dataset, also includes old firms i.e. registered before 2003, but for whom data became available between 2003 and 2008).<sup>6</sup>

# **(iii) Productivity and Firm Characteristics**

**The results of the empirical analysis of firm characteristics are statistically strongly significant;** they are also broadly consistent with earlier analyses of theoretical and empirical literature using the Russian data (e.g., Alam et al. 2007). All independent variables, except measure of the industry structure, are statistically significant at the 1 percent level). Results are summarized in Table 3.

The following principal findings are of particular interest.

- First, **firm size** matters for productivity, with all size variables statistically and strongly significant (relative to micro-sized firms) and large firms being the most efficient, followed by medium-sized firms, indicating the existence of economies of scale and scope.

- Second, the **“primary city effect”** of Moscow, reflecting agglomeration effects of Moscow city on productivity, is statistically and quantitatively significant although the effect is weaker for medium-size and large firms. In other words, it matters more for productivity if a firm has grown over a threshold size than that it is located in Moscow per se (Figure 6). Intuitively, this finding means that once a firm grows to a certain size, locating in the capital—while certainly an advantage––becomes less critical to its productivity.

- Third, **younger firms** tend to be more efficient than older ones, suggesting that firm old age makes knowledge, abilities, and skills obsolete and induces organizational decay (as in Agarwal and Gort, 1996 and 2002). There is a quadratic effect in the relationship between age and productivity i.e., there is a _decline_ in average TFP from the first quintile of the age quadratic until the fourth quintile (the threshold), then _rising_ sharply to the fifth quintile.

> 6 Results when outliers are included are slightly different (See Figure A2 in the Annex).

12
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|||**App**|**endix Table II**||||
|---|---|---|---|---|---|---|
|||**You**|**ng people: total**||||
|**l**|**unemployment**<br>1|**employment**|**not in labor**<br>**force**|**enrollment**|**idleness**|**jobless**|
|**unempoyment**<br>|112<br>*||||||
|**employment**|-0.6674<br>112|1<br>112|||||
|**not in labor force**|0.3601*|-0.9293*|1||||
||112|112|112||||
|**enrollment**|0.2705*<br>|-0.3277*<br>|0.2772*<br>|1<br>|||
|**idleness**|98<br>-0.146<br>112|98<br>-0.2487*<br>112|98<br>0.3747*<br>112|99<br>-0.5670*<br>99|1<br>118||
|**jobless**|0.4620*<br>112|-0.5721*<br>112|0.4839*<br>112|-0.4493*<br>98|0.7206*<br>112|1<br>112|
|||**You**|<br>**ng people: males**||||
|**lt**|**unemployment**<br>1|**employment**|**not in labor**<br>**force**|**enrollment**|**idleness**|**jobless**|
|**unempoymen**|112||||||
|**employment**|-0.6616*<br>112|1<br>112|||||
|**not in labor force**|0.3413*<br>112|-0.9251*<br>112|1<br>112||||
|**enrollment**|0.1806<br>|-0.3465*<br>|0.3444*<br>|1<br>|||
|**idleness**|98<br>-0.0957<br>112|98<br>-0.2707*<br>112|98<br>0.3835*<br>112|99<br>-0.4043*<br>99|1<br>117||
|**jobless**|06065*|-05949*|04429*|-03239*|06332*|1|
||.<br>112|.<br>112|.<br>112|.<br>98|.<br>112|112|
|||**Youn**|**g people: females**||||
||**unemployment**|**employment**|**not in labor**<br>**force**|**enrollment**|**idleness**|**jobless**|
|**unemployment**|1<br>||||||
|**employment**|112<br>-0.6215*<br>112|1<br>112|||||
|**not in labor force**|0.3546*<br>112|-0.9459*<br>112|1<br>112||||
|**enrollment**|0.3259*<br>|-0.2939*<br>|0.2118*<br>|1<br>|||
|**idleness**|98<br>-0.112<br>112|98<br>-0.3657*<br>112|98<br>0.4875*<br>112|99<br>-0.5910*<br>99|1<br>117||
|**jobless**|0.3519*|-0.6043*|0.5800*|-0.4900*|0.8181*|1|
||112|112|112|98|112|112|
|||**You**|**ng people: urban**||||
||**unemployment**|**employment**|**not in labor**<br>**force**|**enrollment**|**idleness**|**jobless**|
|**unemployment**|1<br>||||||
||94||||||
|**employment**|-0.5784*|1|||||
|**not in labor force**|94<br>0.2015<br>94|94<br>-0.9085*<br>94|1<br>94||||
|**enrollment**|0.0826<br>81|-0.2007<br>81|0.1927<br>81|1<br>82|||
|**idleness**|-0.1159<br>94|-0.2726*<br>94|0.3955*<br>94|-0.5940*<br>82|1<br>99||
|**jobless**|0.4903*<br>|-0.5084*<br>|0.3615*<br>|-0.5776*<br>|0.6712*<br>|1<br>|
||94|94|94|81|94|94|
|||**You**|**ng people: rural**||||
||**unemployment**|**employment**|**not in labor**<br>**force**|**enrollment**|**idleness**|**jobless**|
|**unemployment**|1<br>||||||
|**employment**|93<br>-0.7000*|1|||||
|**not in labor force**|93<br>0.4508*<br>|93<br>-0.9463*<br>|1<br>||||
||93<br>*|93<br>*|93<br>*||||
|**enrollment**|0.3152<br>|-0.4057<br>|0.3859<br>|1<br>|||
|**idleness**|80<br>-0.1236<br>93|80<br>-0.2604*<br>93|80<br>0.3679*<br>93|81<br>-0.4509*<br>81|1<br>98||
|**jobless**|04704*|-06118*|05397*|-03130*|07368*|1|
||.<br>93|.<br>93|.<br>93|.<br>80|.<br>93|93|
|**Note: * correlation si**|**gnificant at the 5**|**% level.**|||||

25
to injure, destroy, or prevent the establishment of an industry in the United States or to restrain competition.

This law is still on the books, but John J. Barcelo (1991), in his review of antidumping laws and actions, found that despite the attractive lure -- of triple damages, only one serious private suit was brought under the law a 1970 suit by Zenith Radio Corporation against Matsushita Electrical Industry Company. The suit was dismissed on summary judgment when Zenith did not provide facts to support a plausible theory of predatory dumping.

# _The U.S. Tariff Commission study of 1919_

In 1916, the U.S. Tariff Commission, at its own initiative, began investigations of foreign competition in the U.S. market and of Canada's experiences with its antidumping law. The key questions in its investigation of foreign competition sought "personal knowledge of unfair competition through the selling in the United States of articles of foreign origin at less than the fair market value when sold for home consumption in the country of origin" (U.S. Tariff Commission 1919, 12). The commission contacted 562 U.S. business enterprises directly. In addition, thirteen producers or traders associations circulated the commission's questions to their membership. Thus every enterprise in the United States against which imports provided some degree of competition was informed of the investigation and had the opportunity to respond. (The responses are tabulated in table 2.1.) The commission's survey of virtually every business enterprise in the United States found twenty-three that claimed knowledge of foreign dumping. Aimost six times as many reported that they had no knowledge of unfair foreign competition or dumping. Of the complaints of foreign competition, the commission classified by far the largest share as "severe competition." A few examples demonstrate the tenor of these complaints: Patent leather. After the enactment of the last tariff bill, Germany began to ship in grain-finished patent leathers made from cowhides and kid skins and these leathers were sold at a lower

- 13 -
14

**Table 4. Concentration and Diversification Indices for Sub-Saharan Africa and Other Groups of Developing Countries: 1962-64 to 1991-93.**

||Share of T<br>Produ<br>Total Exp|hree Largest<br>cts in<br>orts (%)*|Pro<br>Divers<br>Ind|duct<br>ification<br>ex**|Prod<br>Concent<br>Index|uct<br>ration<br>***|
|---|---|---|---|---|---|---|
|Country Group|1962-64|1991-93|1962-64|1991-93|1962-64|1991-93|
|All Sub-Saharan Africa*|36.5|62.3|0.71|0.77|0.20|0.49|
|Low Income Africa|39.2|62.9|0.72|0.79|0.22|0.50|
|Middle Income Africa|43.9|74.3|0.76|0.80|0.24|0.60|
|Low Income Asia|30.4|34.5|0.61|0.53|0.17|0.20|
|Middle Income Asia|38.5|30.8|0.74|0.44|0.21|0.15|
|North Africa|63.0|68.7|0.74|0.73|0.44|0.43|
|Latin America & Caribbean|38.9|23.8|0.62|0.40|0.22|0.13|
|Middle East|92.0|91.0|0.84|0.84|0.82|0.79|
|Other Europe & Central Asia|26.1|25.6|0.50|0.44|0.13|0.12|
|High Income Non-OECD|41.0|40.8|0.68|0.49|0.25|0.22|
|All Non-OECD excluding<br>Sub-Saharan Africa|35.5|30.4|0.46|0.34|0.21|0.15|
|OECD Countries|12.8|23.1|0.17|0.14|0.05|0.11|

v The share of the three largest three-digit SITC products in total exports. "*The diversification index for country j (Gj! is defined as, Gj = [E I hj - hi] ÷ 2 where hj is the share of commodity i in the exports of country j, and h, is the share of the commodity in world trade. ***The Hirschman index for country j (Hi) is measured by, where xj is the value of j's exports of commodity i and X is j's total exports. Both indices range between 0 and 1 with the higher values reflecting increased concentration. The hirschman index discriminates more finely between countries which are relatively more concentrated in their export structure while the global index discriminates more finely between countries which are relatively more diversified.

Source: Computed from United Nations Series D Trade Records.
# Quantifying Vulnerability to Poverty in the Drought-Prone Lowlands of Ethiopia

## **Emmanuel Skoufias* Katja Vinha Berhe Mekonnen Beyene**

## **JEL Classification** : Q54, C63, R11, R5, I3

**Keywords:** <u>Vulnerability, Drought, Poverty</u>

_*_ _<u>Corresponding author:</u>_ Emmanuel Skoufias, The World Bank (MSN: J5-503), 1818 H Street NW, Washington DC 20433-USA. tel: (202) 458-7539. fax: (202) 522-3134. e-mail: <u>eskoufias@worldbank.org.</u>
Table D.13. Effects of Guerrilla Territorial Control on Political Attitudes

|||_Total Sum of Questio_|_ns per Item/Scope_||
|---|---|---|---|---|
||Political|Engagement with|Non-Democratic|Trust in|
||Participation|Politicians|Engagement|Institutions|
||(1)|(2)|(3)|(4)|
|Guerrilla control|1.449|-0.380**|0.181|-4.112***|
||(1.098)|(0.184)|(1.183)|(1.403)|
|Observations|242|248|172|241|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|
|Dependent mean|12.960|0.383|4.780|11.720|

_Note:_ The table presents the results of equation 1 for our outcomes related to political discontent and distrust. Column 1 shows the political participation scope, which includes questions that measure whether the citizen votes, attends protests, and attends government meetings. Column 2 reports the engagement with politicians’ scope, which measures the extent to which citizens contact state authorities and/or bureaucracies to solve issues and attend government/political meetings. Column 3 shows the nondemocratic engagement scope, which measures the extent to which citizens approve the use of alternative or violent means to engage in politics. Column 4 reports the trust in institutions item, which measures the extent to which citizens trust different types of Salvadoran institutions, including the police, the powers of state, and local government. The table uses the simple sum of questions by each item as dependent variables. The unit of observation in all Columns is the census tract. The information was obtained from the Latin American Public Opinion Project survey (LAPOP). Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table D.14. Effects of Guerilla Territorial Control on Distance to Police Stations and Incarcerations

||Distance to|Incarcerations|
|---|---|---|
||Police Stations|(1992-1999)|
||(1)|(2)|
|Guerrilla control|0.0198|0.0193|
||(0.0614)|(0.0137)|
|Observations|3,652|3,681|
|Bandwidth (Km)|2.266|2.266|
|Dependent mean|1.850|0.0580|

_Notes:_ This table shows the effects of guerrilla control on the distance to the closest local police station (Column 1) and the number of incarcerations per segment between 1992 and 1999 (Column 2).

87
# **Estimating the short-run poverty impacts of the 2010-11 surge in food prices**

Global food prices rose rapidly between June 2010 and early 2011, taking the World Bank Food Price Index above its peak of the 2008 ―food price crisis‖ by early March. Like the 2008 price spike (Ivanic and Martin, 2008), this rise in food prices seems likely to have increased poverty substantially. There is clearly a need for timely-estimates of the severity of the impacts of such price surges on poverty as a guide for policy responses. A key purpose of this study is to explain the approach used to prepare the indicative estimate of 44 million additional people thrown into poverty first released in the World Bank‘s (2011) March issue of _Food Price Watch._ A key difference from the 2008 food crisis is that the more recent price surge is much more broadly-based across food groups, which implies a different impact on poverty relative to the 2008 episode, since commodities such as rice have seen more moderate price increases while prices of items such as edible oils have increased considerably more than in 2008. Another potentially important difference between the two episodes is in the extent to which changes in world prices of key staples have been transmitted into domestic markets—an issue on which we have much better information than in 2008 (World Bank, 2011).

The poverty and nutritional implications of even short-lived price spikes can be serious as poor people spend very large fractions of their incomes on food, and hence are likely to experience large declines in their real income levels, and poor producers do not have time to increase their output in response to the price change. Even short run price spikes both reduce calorie intake and compromise dietary diversity (Skoufias, Tiwari and Zaman, 2011), and may have adverse long run consequences especially when infants are affected (Alderman, Hoogeveen and Rossi, 2006).

In 2010, a confluence of weather shocks in large producing countries, followed in some cases by export restrictions, curtailed supply and contributed to world wheat prices more than doubling between June 2010 and year end. Global maize prices rose about 73 percent in the six months after June 2010, driven by a range of factors including: a series of downward revisions of crop forecasts; the positive relationship between maize and wheat prices; and the use of maize for biofuels, which both increased demand this year and had contributed to lowering stock levels in previous years. Global rice prices have increased at a slower rate than other grains. The export price for Thai rice (5 percent broken grade) increased by 8 percent between October 2010 and 2
**Figure 3 Predicted Occupational Placement Improvement (ΔÔP) versus Predicted Occupational Placement (ÔP), 1975 arrival**

<!-- Start of picture text --> 30 30<br>Ecuador VietnamSouth KoreaFiji<br>252015105 PortugalHaitiHondurasDominican RepublicMexicoEl SalvadorLaosPolandCape VerdeGuatemalaColombiaGhanaYugoslavia (former)(Kampuchea)NicaraguaCosta RicaCambodia UruguayThailandNigeriaPhilippinesItalyPeruSpainBarbadosBelize/British Burma (Myanmar)ChJamaicaBoliviaTurkeyCubaArgentinaHondurasRussialeChinaGuyana/British GreeceHungaryEthiopiaTrinidad & TobagoAfghanistanIndonesiaIrelandIraqCzech Republic and GuianSyriaBangladeshRomaniaBrazilJordanVenezuelaPanamaSlovakiaPakistanEgypt/United Arab FranceLebanonMoroccoIrJapanRep.BelgiumTaiwanIndiaSwitzerlandIsrael/PalestineHong KongGermanyNetherlandsSwedenNew ZealandMalaysiaDenmarkCanadaUnited KingdomAustralia 252015105 MexicoGhanaDominicaBelize/British HaitiPortugHondurasNigeriaEl SalvadorLaosCosta RicaYugoslavia (former)GuatemalaPolandDominican RepublicNicaraguaPeruEcuadoThailandEthiopialColombiaItalyJamaica(Kampuchea)UPhilippinesCubaBarbadosKenyaCambodia uguayBurma (Myanmar)VenezuelaTrinidRussiaChinaVietnamChileSpainArgentinaGreeceHungaryBrazilJapanRomaniaCzech Republic and NetherlandsEgypt/United Arab LebanonIrelandPanamaAfghanistand & TobagoJordanTurkeyDenmarkGuyana/British IranIrGermanyHong KongSlovakiaGuianaRep.SyriaqSwitzerlandIndiaNew ZealandBelgiumTaiwanAustralia<br>Dominica Honduras South Korea CanadaUnited Kingdom<br>0 Kenya 0 Indonesia Pakistan BangladeshFranceMalaysiaIsrael/Pale stine<br>35 40 45 50 55 60 65 70 75 80 85 90 35 40 45 50 55 60 Cape Verde65 70 Bolivia75 80 Morocco85 90<br>-5 -5<br>-10 y = -0.3904x + 38.64 -10 y = -0.2746x + 24.397 Fiji Sweden<br>R 2 = 0.4485 R 2 = 0.1695<br>-15 -15<br>Predicted Occupational Placement in 1980 Predicted Occupational Placement in 1990<br>Predicted Improvement from 1980 to 1990 Predicted Improvement from 1990 to 2000<br><!-- End of picture text --> <!-- Start of picture text --> a. ΔÔP1980-1990 versus ÔP1980,<br>1975 arrival, OP=EDUCATION INDEX 1<br><!-- End of picture text --> <!-- Start of picture text --> b. ΔÔP1990-2000 versus ÔP1990,<br>1975 arrival, OP=EDUCATION INDEX 1<br><!-- End of picture text --> 37
3 in evidenced by the evolution of tax revenues during hyperinflation In the classical epi$odes hyperinflatLon Lnduced a collapse of tax revenues (as a result of the Olivera-Tansz effect). In contrast, Argentina and Brazil were able to limit the fLcal damage of hyperinflation.

The paper iL organiLad **as** follows. SectLon II presents some basic facts about the behavior of LnflatLon in the episodes that we study, and show that Brazil and Peru had **more** control over Lnflation than the other epLodes included in our study. SectLon III examines the wholeg process of hyperinflatLon and stabilizatLon ln the classic hyperinflations, wlth especial attentlon on the o livian **case.** Section IV will concentrate on the causes of the hyperlnflations in Argentina, Brazil and Peru. It is argued that the new opisodes **are** indeed of a different nature, mainly because they took place in countries with a tradition of high inflation. We of course recognize that there were clear differences within this group. Peru has more similarities with traditional **epLsodes** regarding the causes, though it managed to avold a full acceleration of inflation. In Argentlna and BrazlI the hyperinflation was triggered by different forces. Section V investigates Ln what respects the recent stabLilzation process in Argencina, Brazil and Peru can be considered as a de Parture from previous, less comprehensive stabilization attempts, and to what extent can we consider them as representing a change of regime. We also briefly examine the impact of these programs on inflation, and **discuss the** differences with the classical hyperinflations. **We** conclude in section VI with some final remarks.

# II. Basic Fgatures of InflatLon

Table illlustrates som of the differences between the classical and the new hyper$nflatlons.2 **We** used Cagan's criterion for determining the beginning and end of a hyperinflation. In his own words "I shall define hyperinflations as beginning ln **the** month the rise in prices exceeds 50 2 The tables A.1 and A.2 at the end of the paper provide more detailed data of the evolution of inflation.