rafmacalaba/datause-encoder-data huggingface dataset:
- Feldstein, Martin S. & Charles Y. Horioka (1980). “Domestic Saving and International Capital Flows”. _Economic Journal_ 90(358) (June): 314–329

- Ferderer, J. Peter (1993). “The Impact of Uncertainty on Aggregate Investment Spending: An Empirical Analysis”. _Journal of Money, Credit and Banking_ 25(1) (February): 30–48

- Gehlbach, Scott & Philip E. Keefer (2011). “Investment Without Democracy: Ruling-Party Institutionalization and Credible Commitment in Autocracies”. _Journal of Comparative Economics_ 39(2) (June): 123–139

- G ̈org, Holger & David Greenaway (2004). “Much Ado About Nothing? Do Domestic Firms Really Benefit from Foreign Direct Investment?” _World Bank Research Observer_ 19(2) (September): 171–197

- Greene, Joshua E. & Delano S. Villanueva (1991). “Private Investment in Developing Countries: An Empirical Analysis”. _IMF Staff Papers_ 38(1) (March): 33–58

- Hall, Robert E. & Charles I. Jones (1999). “Why Do Some Countries Produce So Much More Output Per Worker Than Others?” _Quarterly Journal of Economics_ 114(1) (February): 83–116

- Hall, Robert E. & Dale W. Jorgenson (1967). “Tax Policy and Investment Behavior”. _American Economic Review_ 57(3) (June): 391–414

- Hayashi, Fumio (1982). “Tobin’s Marginal q and Average q: A Neoclassical Interpretation”. _Econometrica_ 50(1) (January): 147–159

- Holmstr ̈om, Bengt R. & Jean Tirole (1997). “Financial Intermediation, Loanable Funds, and the Real Sector”. _Quarterly Journal of Economics_ 112(3) (August): 663–691

- Hon, Chu. Kam (2012). “The Feldstein-Horioka Puzzle and Spurious Ratio Correlation”. _Journal of International Money and Finance_ 31(2) (March): 292–309

- Hsieh, Chang-Tai & Peter J. Klenow (2007). “Relative Prices and Relative Prosperity”. _American Economic Review_ 97(3) (June): 562–585

- Hutchison, Michael M. & Ilan Noy (2005). “How Bad Are Twins? Output Costs of Currency and Banking Crises”. _Journal of Money, Credit, and Banking_ 37(4) (August): 725–752

- Jorgenson, Dale W. (1963). “Capital Theory and Investment Behavior”. _American Economic Review_ 53(2) (May): 247–259

- Kopcke, Richard W. & Richard S. Brauman (2001). “The Performance of Traditional Macroeconomic Models of Businesses’ Investment Spending”. _New England Economic Review_ 1(2) (Summer): 3–39

- Kraay, Aart C. (2012). “Instrumental Variables Regressions with Uncertain Exclusion Restrictions: A Bayesian Approach”. _Journal of Applied Econometrics_ 27(1) (January/February): 108–128

- Kraay, Aart C., Norman V. Loayza, Luis Serv ́en & Jaume Ventura (2005). “Country Portfolios”. _Journal of the European Economic Association_ 3(4) (June): 914–945

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21
were higher for India (Table 1).<sup>38</sup> Only about one-in-three adult women (and only one-half of adolescent girls) were able to read and write at the time India embarked on its current reform period; by contrast, when China embarked on its reforms 10 years earlier, over half of adult women and 70% of adolescent women were literate.<sup>39</sup> Over time, the gender gaps in education and literacy have been narrowing in India (Table 1).

India also lagged in its health attainments (Table 1). India’s infant mortality rate in 1990 was 80 deaths per 1,000 live births, more than twice that of China in 1990, and there was also an eight year difference in life expectancy (60 years in India as compared to 68 years in China).

Sub-national differences in these and other inequalities also reveal their importance to poverty reduction. Across states of India, the differences in the impacts of non-farm economic growth on poverty reflect inequalities in a number of dimensions; low farm productivity, low rural living standards relative to urban areas and poor basic education all inhibited the prospects of the poor participating in growth of the non-farm sector (Ravallion and Datt, 2002). Interstate differences in initial levels of schooling appear to have been the dominant factor in explaining the subsequent impacts of non-farm economic growth on poverty. Those with relatively little schooling and few assets, or little access to credit, were less well positioned to take advantage of the new opportunities unleashed by market-oriented reforms. Sub-nationally, India’s disparities in literacy rates are driven more by the differences in female literacy, which has greater explanatory power for rate of poverty reduction (Datt and Ravallion, 1998b).

The potential for using income redistribution to address India’s poverty problem is far more limited than in China or (especially) Brazil. Repeating the hypothetical tax rate calculation made above for China and Brazil, it would be impossible to raise enough revenue from a tax on Indian incomes above the US poverty line to fill India’s poverty gap relative to the $1.25 a day line; the required marginal tax rate would exceed 100%.<sup>40</sup> Indeed, even at a 100% marginal tax rate, the revenue generated could fill only 20% of India’s aggregate poverty gap. Using the mixed method instead, the marginal tax rate still exceeds 100%, though only narrowly.<sup>41</sup>

> 38 For a discussion of the reasons for this gender gap in India, including its historical roots in Brahminical tradition as well as more current biases in the schooling system and parental behavior see Drèze and Sen (1995, Chapter 6).

> 39 The adolescent literacy rates are from Drèze and Sen, 1995, Table 4.2).

> 40 Recalling the earlier notation, the value of _PG_ (13) is 86.7% in India. With a value of _PG_ (1.25) of 10.51% and mean consumption of $1.76, the required marginal tax rate would be almost 500%!

> 41 The value of _PG_ (13) is 82.6% using the mixed method, while _PG_ (1.25) is 3.98% and mean consumption is

> $2.30.

20
# Figure 8: ECA: Investment prospects

# A. Foreign direct investment liabilities, by source, 2019-20

<!-- Start of picture text --> Percent of total FDI liabilitie s<br>Russian Federation<br>75<br>EU<br>50<br>25<br>0<br>Türkiye<br>ECA excl. RUS and UKR Central Asia Central Europe Eastern Europe South Caucasus Western Balkans<br><!-- End of picture text --> C. Investment growth, 2010-21, and 2022 forecasts for 2022-27 <!-- Start of picture text --> Percent<br>ECA Minimum-maximum range<br>30<br>0<br>-30<br>-60<br>E. ECA countries’ dependence on imports<br>from<br>Russia<br>Percent of partner's total imports<br>60<br>Interquartile range<br>Average<br>40<br>20<br>0<br>Total Fuels Wood Metals Chemicals<br>2010 2012 2014 2016 2018 2020 2022 2025 2027<br><!-- End of picture text -->

# B. Political risk in 15 ECA countries and policy

<!-- Start of picture text --> uncertainty in Russia and Poland<br>Index ICRG Index<br>500 Economic policy uncertainty 67<br>Political risk (RHS)<br>66<br>400<br>65<br>300 64<br>200 63<br>62<br>100<br>61<br>0 60<br>D. ‘Well-governed transition’ indicator<br>(EBRD<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> assessment) <!-- Start of picture text --> Score<br>2016 2021<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>ECA CA CE EE WBK SCC<br><!-- End of picture text --> <!-- Start of picture text --> F. Deviation of investment from pre-<br>pandemic<br>projections<br>Percent<br>ECA ECA excl. RUS, BLR, UKR<br>0<br>-5<br>-10<br>-15<br>2019 2020 2021 2022 2023<br><!-- End of picture text --> Sources: Baker, Bloom, and Davis (2016); Consensus Economics; EBRD; Haver Analytics; International Country Risk Guide (ICRG); International Monetary Fund; national sources; Winkler, Wuester, and Knight (2022); World Bank.

Note: BLR = Belarus; CA = Central Asia; CE = Central Europe; EE = Eastern Europe; FDI = foreign direct investment; ICRG =
# **References:**

Ahmed, Hanane and Ibrahim Elbadawi (2008), “Assessing the Peace Process in Darfur,” unpublished mimeo, The Development Economic Research Group, The World Bank, Washington DC.

Ali, Ali A.G. (2007),”On the Implementation of the Wealth Sharing Agreement in PostConflict Sudan,” unpublished mimeo, The Arab Planning Institute, Kuwait.

Ali, Ali A.G., Ibrahim A. Elbadawi and Atta El-Batahani (2005), **“** The Sudan's civil War: Why has it prevailed for so long?” Chapter 10, In P. Collier and Nicholas Sambanis, eds. <u>Understanding Civil War: Evidence and Analysis. Volume I: Africa. Washington</u> DC: World Bank.

Bodea, Cristina, and Ibrahim Elbadawi (2007), " Riots, Coups and Civil War: Revisiting the Greed and Grievance Debate." Policy Research Working Papers 4397. World Bank, Washington D.C.

Bodea, Cristina and Ibrahim Elbadawi (2006), “Political Violence and Underdevelopment.” Unpublished mimeo, presented at the Plenary Session of the AERC bi-annual Research Workshop: Nairobi, December.

Collier, Paul and Anke Hoeffler (2006),”Military Spending in Post-Conflict Societies.” <u>Economics of Governance.</u> CPA Monitor (2007), “Monthly Report on the Implementation of the CPA”, prepared by United Nations Mission in Sudan – available at <u>www.unmis.org</u> Deng, Luka B. (2007a),” CPA Implementation Progress: Status and Challenges.” Power point presentation to Sudan Consortium, Juba, Sudan: <u>www.gurtong.org.</u> Deng, Luka B. (2007b), “Speech Delivered before the Southern Sudanese Community in Australia”: <u>www.gurtong.org.</u> Fearon, James, (1995), “Rationalist Explanations for War,” _International Organization_ , 49, no.3, pp 379-414 (summer).

Flint, Julie and Alex de Waal (2005), Darfur: A Short History of a Long War. New York: Zed Books.

Freedom House (2007), “Freedom in the World.” Available at http://www.freedomhouse.org.

Gehlbach, Scott G. and Keefer, Philip (2007), "Investment Without Democracy: RulingParty Institutionalization and Credible Commitment in Autocracies" Available at SSRN: <u>http://ssrn.com/abstract=1080169</u> 50
**<u>Table A2: Summary statistics and logit regression of NREGS participation, using 2006 data</u>**

|||**Sample **|**Means**|||**Logit R**|**egressions**||
|---|---|---|---|---|---|---|---|---|
||**Pha**|**se 1**|**Phase**|**s 2 &3**|**Phase**|**1**|**Phases 2 &**|**3**|
||**Part.**|**Nonp.**|**Part.**|**Nonp.**|**Coeff.**|**t-stat.**|**Coeff.**|**t-stat.**|
|Household lives in hamlet|0.34|0.36|0.39|0.30|-0.0591|(-0.61)|0.341**|(2.66)|
|Household is POP|0.43|0.36|0.40|0.39|0.402**|(3.09)|0.0410|(0.23)|
|Household is Poor|0.33|0.26|0.38|0.29|0.490***|(3.86)|0.253|(1.48)|
|Prim occ. casual labor|0.72|0.56|0.77|0.60|0.432***|(4.09)|0.568***|(3.91)|
|Household is SC|0.34|0.20|0.33|0.14|0.251*|(2.16)|0.978***|(5.84)|
|Household is ST|0.09|0.08|0.28|0.24|0.0280|(0.16)|0.604***|(3.66)|
|Household is OC|0.13|0.27|0.13|0.24|-0.309*|(-2.23)|-0.104|(-0.54)|
|Somebody can write|0.81|0.76|0.75|0.72|0.377**|(3.00)|0.146|(0.94)|
|Household size|4.27|4.03|4.09|3.93|0.00304|(0.09)|-0.00276|(-0.06)|
|Head female|0.08|0.12|0.14|0.20|-0.602***|(-3.71)|-0.270|(-1.46)|
|Leader in VC or SHGs|0.13|0.10|0.10|0.07|0.330*|(2.30)|0.425|(1.95)|
|Consumption p.c. (Rs/year)|8,549|9,972|8,297|9,177|-0.0000257*|(-1.98)|-0.00000096|(-0.06)|
|Non-financial assets p.c. (Rs)|3,038|4,446|2,633|2,900|-0.00000547|(-0.75)|0.0000057|(0.57)|
|Energy intake p.c. (Kcal/d)|2,246|2,446|2,239|2,324|-0.000197|(-1.08)|0.0000320|(0.16)|
|Protein intakep.c.(g/day)|46.66|50.03|45.20|47.67|0.00791|(0.81)|-0.00500|(-0.47)|
|Number of observations|998|1,344|340|1,295|2,342||1,635||
|Pseudo R-squared|||||0.076||0.083||

_Source:_ Authors’ computation from household survey and administrative data. _Notes: t_ statistics are in parentheses. Significance level: *: 10%, **: 5%, ***: 1%.

26
Burgherr and Hirschberg (2014), Figure 3 presents statistics of accidents and corresponding fatalities from energy sector activities occurred during 1970-2008 period.

**Figure 3: Energy sector accidents during the 1970-2008 period**

(excludes accidents less than 5 fatalities)

- (a) Number of accidents

<!-- Start of picture text --> OECD Non-OECD Total<br>Coal Oil Natural gas LPG Hydro Nuclear Biogas Geothermal<br>(b) Number of fatalities<br>OECD Non-OECD Total<br>Coal Oil Natural gas LPG Hydro Nuclear Biogas Geothermal<br>2,481<br>2,394<br>545<br>358<br>87 187 109 78 187 58 70 128 1 21 22 1 1 2 2 1 1<br>38,672 40,931<br>30,069 30,083<br>3,495 3,853<br>2,259 358 1,258 78 1,336 1,856 70 1,926 14 31 31 18 18 21 21<br><!-- End of picture text --> _Note: 85% of the total accidents and 70% fatalities in the coal sector in Non-OECD countries occurred in China. Moreover, 26,108 fatalities out of total 30,083 fatalities from hydropower accidents during the 1970-2008 period caused by a single accident in China’s Banqiao dam and Shimantan Reservoir Dam systems that occurred in August 1975._

**_Source: Burgherr and Hirschberg, 2014_**

9
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 17. Infrastructure spending needs in Angola, 2006–15**

|$ million per year||||||
|---|---|---|---|---|---|
|Sector|New<br>investments|Rehabilitation|Total capital<br>expenditure|Operations and<br>maintenance|Total needs|
|ICT|169|0|169|119|288|
|Irrigation|1|16|16|2|18|
|Power|558|50|608|177|785|
|Transport|107|156|263|160|423|
|Water supply and sanitation|233|128|361|213|574|
|Total|1,067|350|1,417|671|2,088|

_Source_ : Mayer and others 2009; Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others, 2009. Derived from models that are available online at www.infrastructureafrica.org/aicd/tools/models. _Note:_ WSS = water supply and sanitation; ICT = information and communication technology.

While Angola’s infrastructure spending needs are comparatively high in absolute terms, they look manageable relative to the country’s burgeoning GDP. Expressing infrastructure spending needs as a percentage of GDP gives a sense of the economic burden involved in providing adequate infrastructure. For Angola, this burden amounts to no more than 7 percent of GDP, much lower than most other African countries (figure 21). Infrastructure investment would absorb around 5 percent of GDP—only around one-third of what China invested in infrastructure during the mid-2000s.

**Figure 21. Angola’s infrastructure spending needs are comparatively low relative to GDP**

<!-- Start of picture text --> Estimated infrastructure spending needed to meet targets, as percentage of GDP<br>SSA<br>LIC-Fragile<br>LIC-NoFragile<br>ECOWAS<br>Resource-Rich<br>MIC<br>Liberia<br>Congo, Dem. Rep.<br>Zimbabwe<br>Ethiopia<br>Kenya<br>Mali<br>Ghana<br>Uganda<br>Cote d'Ivoire<br>Nigeria<br>South Africa<br>Capex O&M<br>Cape Verde<br>Botswana<br>Angola<br>Gabon<br>% of GDP 0 10 20 30 40 50 60 70 80<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009.

_Note:_ LIC = low-income country; MIC = middle-income country; ECOWAS = Economic Community of West African States; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure.

36
19 farm handling and storage. This is also what most PHL interventions have traditionally focused on (World Bank, 2011b).

These estimates are necessarily also based on many assumptions, including extrapolation from purposively sampled (and often older) case studies, which may inadvertently zoom in on areas where PHL is largest. A representative picture of the diverse contemporary postharvest systems of durable crops such as cereals, which are the key staples and caloric providers in eastern and southern Africa, remains lacking (Stathers, Lamboll, and Mvumi, 2013). This paper has begun addressing this void using a completely reduced form approach, i.e. by drawing on-farmers’ reported estimates of their PHL of maize, collected in four comprehensive nationally representative LSMS-ISA household surveys from three countries of eastern and southern Africa (Uganda, Malawi and Tanzania).

It is estimated that on average between 1.4, 2.9-4.4, and 5.9 percent of the national maize harvest is lost in Malawi, Tanzania and Uganda respectively. These estimates are substantially lower than the 8 percent cereal loss from on-farm handling and storage in SSA (FAO, 2011), even though maize is more prone to post-harvest loss than the other cereals (World Bank, 2011b) and abstracting from the fact that the self-reported losses may also include losses incurred during marketing and field operations. Further analysis of the Tanzania experience shows that the self-reported estimates are broadly consistent with the key proximate determinants of PHL identified in the agronomic literature, pointing to internal consistency and providing confidence in this reduced-form approach. They are highest in Uganda (with warmer and more humid AEZs) and lowest in Malawi (where sub humid and semi-arid AEZs are equally represented). They also increase with storage duration and the harvest share marketed, are further exacerbated with the number of market visits (suggesting that they may also incorporate some losses during marketing), but decline with the use of improved storage technologies (even though not with the use of crop protection such as spraying or smoking, suggesting lower effectiveness of these techniques when applied by households in practice).
# Can Duty-Drawbacks have a protectionist bias? Evidence from Mercosur*

Olivier Cadott Jaime de Melot Marcelo Olarreaga§ _JEL classification numbers:_ Fll, F13, F15 Keywords: Duty-drawbacks, Political-economy MERCOSUR, New regionalism,

*We are grateful to Gustavo Bittencourt, Eric Bond, Caroline Freund, Kishore Gawande, Jaime Granados, Bernard Hoekman, Pravin Krishna, Phil Levy, John McLaren, Garry Pursell, Maurice Schiff, Isidro Soloaga, T.N. Srinivassan, David Tarr, Mark Thomas and participants at the "Leitner Conference on Political and Economic Aspects of Regional Integration", Yale University, April 7-8, 2000 and a Trade Seminar at the World Bank for very helpful comments and suggestions. We also thank Lili Tabada for excellent assistance.

tUniversity of Lausanne, Switzerland and CEPR; email "Olivier.Cadot@hec.unil.ch"; $University of Geneva, Department of Political Economy, 102 bd Carl Vogt, 1211 Geneva 4, Switzerland, CERDI and **CEPR;** email: "deMelo@ibm.unige.ch".

§The World Bank, 1818 H St., Washington, DC 20433, USA and **CEPR,** UK; email "molarreaga@worldbank.org".
41 Scenario 2: This scenario deals with a debt reduction scheme carried out by reducing the DC stock of debt by $160 billion at the beginning of the simulation period.<sup>25</sup> This reduction is implemented in the model irrespective of the method of its financing?o By assumption, the critical level of the "debt interest payments to exports' ratio, which is the 'measure' of the ability of developing countries to service debt in MULTIMOD, remains unchanged at 3 percent for the entire simulation period.2<sup>7</sup> Initially, the reduction in the stock of debt entails a decline in the net wealth of the private sector in the creditor countries. But, because of the increase in exports of these countries, they enjoy a rebound in economic activity: real GNP increases, current account improves. and the initial loss in private net wealth is more than offset in the long-run.

In addition, lower debt enables the developing countries to save on their interest payments. Lower interest payments improves the 'measure" of their debt servicing ability or qualifies them for more borrowing, which, net of interest payments, is used to finance additional imports. Higher imports stimulate higher production in the industrialized countries (e.g., real GNP in the U.S. increases by 0.23 percent on impact). But the rise in output in the industrialized countries does not lead either to a significant increase in the demand for the developing countries' exports or to higher commodity prices. As a result, the developing countries face widening current account deficits 2%160 billion is equivalent to a quarter of outstanding total external debt, both short-term and long-term,of all developing countries to the private creditors in 1987. Source: World Debt Tables, 1988-89 edition.

> 2 lHowever, according to some analysts, a reduction in outstanding debt to private creditors by $160 billion would bring it close to its true market value.

2ZFlows of financing between industrial ar.d developing countries in MULTIMOD are assumed to depend on the ability of developing countries to service their debt. The measure of servicing ability used is the ratio of debt interest payments, corrected for inflation, to exports. Expectations are assumed to be formed for that ratio: if it exceeds a threshold level imposed on the model, then additional financing will not be demanded or supplied. If it is less than the threshold, then financing will be available in an amount that depends on the difference between the expected level and the threshold level."
16 USA (PPP$). But two factors are worthy of notice. First, as Figure 4 shows, there is some way to go in per capita income levels before the resource intensity of growth declines. Based on the historic resource intensity of demand for aluminum, copper and steel in Korea, Japan, the EU-12 and the US, it seems unlikely that China’s (and India’s) demand for minerals and metals will decline in the foreseeable future, despite rapid economic growth and rising per capita incomes.

Second, both China and India (as we have seen) are very large economies. Thus, in analyzing their impact on global trade we have to suspend the small country assumption that no single economy’s trade pattern will shift the structure of global trade or the prices at which products are traded. As Table 9 shows, China accounts for a rapidly-growing share of global consumption of key base metals and meat, and this has led some commentators (including ourselves – Kaplinsky, 2006 and 2009; Farooki, 2009) to conclude that at the least this helped explain the boom in commodity prices between 2001 and 2008, and perhaps may also play a historicallysignificant role in promoting a structural shift in the global commodities-manufactures terms of trade in favor of commodities

**Table** **<u>9: China’s Share of Global Consumption of Base Metals and Meat</u>**

|**1990**<br>**2000**<br>**2007 **|
|---|
|**Base Metals ( % Share of World Demand)**<sup>**~~1~~**</sup>|
|Aluminum<br>5<br>13<br>33|
|Zinc<br>8<br>15<br>31|
|Lead<br>7<br>10<br>31|
|Iron Ore<br>4<br>16<br>48|
|Copper<br>7<br>12<br>26<br>|
|**Food Products (% Share of World Consumption)**<sup>**~~2~~**</sup><br><br><br> <br>|
|Poultry<br>9<br>18<br>17|
|Pork<br>35<br>47<br>46|
|Beef<br>2<br>10<br>12|
|Soybeans<br>40|

Source:1 Macquarie Commodities Research (2008). 2 Conceicao and Mendoza (2008).

# **_4.2. The Nature of Import Demand_**

Thus, we have observed that Chinese growth has led to a sharp rise in its share of global demand for commodities and perhaps also for a structural upward shift in the relative global price of commodities. But there is more that we can observe about China’s demand for commodities which is of relevance to global commodity value chains feeding into the Chinese economy. The key relevant factors are the demandpreferences of low-income consumers, the consequent relative insignificance of standards in value chains, and the preference for the importation of relatively unprocessed products.