rafmacalaba/datause-encoder-data huggingface dataset:
Table 3 continued

||Year|Work force<br>in<br>agriculture<br>(°/0)|Work force<br>in<br>industry<br>(%)|Work force<br>in<br>services<br>(%)|Working age<br>population<br>in 1995<br>(thousands)|Source|Income<br>group|
|---|---|---|---|---|---|---|---|
|liberia|1990|72.3|5.6|22.2|1,531|i|low|
|Central African Rep.|1990|80.2|3.5|16.3|1,750|i|low|
|Togo|1990|65.6|10.1|24.4|2,116|i|low|
|Sierra Leone|1990|67.4|15.3|17.2|2,463|i|low|
|Benin|1990|63.5|8.1|28.4|2,692|i|low|
|Burundi|1990|91.7|2.7|5.6|3,217|i|low|
|Guinea|1990|87.2|1.9|10.9|3,370|i|low|
|Chad|1990|83.2|4.2|12.6|3,371|i|low|
|Rwanda|1990|91.7|3.4|4.9|3,985|i|low|
|Senegal|1990|76.7|7.5|15.8|4,403|i|middle|
|Niger|1990|89.9|3.9|6.2|4,499|i|low|
|Zambia|1990|74.6|8.5|16.9|4.622|i|low|
|Somalia|1990|75.3|8.2|16.4|5,069|i|low|
|Burkina Faso|1990|92.4|1.8|5.8|5,383|i|low|
|Mali|1990|85.8|2.0|12.2|5,399|i|low|
|Malawi|1990|86.6|4.9|8.4|5,454|i|low|
|Angola|1990|74.5|8. 1|17.4|5,533|i|middle|
|Zimbabwe|1990|68.2|8.3|23.5|6,071|i|low|
|Cameroon|1990|69.7|8.8|21.5|6,958|i|nmiddle|
|Cole dlvoire|1990|59.9|9.6|30.5|6,967|i|low|
|Madagascar|1990|78.2|6.8|15.1|7,277|i|low|
|Mozambique|1990|82.7|8.0|9.3|8,486|i|low|
|Ghana|1990|59.3|13.0|27.7|9,045|i|low|
|Uganda|1990|84.5|4.7|10.7|9,970|i|low|
|Kenya|1990|79.5|7.3|13.2|13,845|i|low|
|<br>Sudan|1990|69.5|8.5|22.1|15,238|i|low|
|Tanzania|1990|84.4|4.9|10.7|15,242|i|low|
|Zaire|1990|67.8|13.4|18.8|21,485|i|low|
|South Africa|1990|13.5|32.0|54.5|24.994|i|middle|
|Ethiopia|1990|86.2|2.1|11.7|29,401|i|low|
|<br>Nigeria|1990|43.0|6.9|50.1|63,9X8|i|low|

Note: Working age population for countries of the forrmer Yugoslavia, former Czechoslovakia, and former Soviet Union are calculated by a forward projection of the 1980-90 growth rate, see text for details.

Sources: c,l,h,o= Census survey, Labor force survey, Household survev and Official Estimates from ILO various years; i=New ILO estimates for 1990; s=Country sources, see text for details.

29
# **5. Further analysis**

# **_5.1. Potential mechanisms and projected impacts under future climate change_**

Having demonstrated strong evidence of temperature effects on poverty and inequality at the subnational level, we further explore agriculture as a potential mechanism. Agriculture plays an important role in poverty reduction for various reasons. The majority of the global poor live in rural areas where agriculture is the predominant form of economic activity and agricultural growth is more effective at reducing poverty than non-agricultural growth; moreover, poorer households are more vulnerable to increases in food prices (Hertel and Rosch, 2010; Hallegate _et al._ , 2016). We analyze the global dataset of historical yields from Iizumi and Sakai (2020), which provides actual crop yields at 0.5° resolution for the period 1981-2016. Using the panel FE and long differences models as with Equations (1) and (2), we find negative effects of higher temperature on different crop yields including rice, maze, and soybean, as shown in Table A11 (Appendix A). Again, we find the long differences model estimates to be smaller than the panel FE model estimates, which are in line with previous studies showing potential adaptation in the long run (e.g., Chen and Gong, 2021). We also document the heterogenous effects of hotter temperature and discuss the results in Appendix D.<sup>9</sup> We next provide projections of the effects of future temperature on poverty to better understand potential effects under different scenarios. We focus on different climate change scenarios taken from the recent released Coupled Model Intercomparison Project Phase 6 (CMIP6).<sup>10</sup> Table A13 (Appendix A) provides a summary of the projected changes for

> 9 As another potential mechanism, we explore subnational migration flow between the period 2005 and 2010. Using a simple OLS regression, we find some suggestive evidence that hotter temperature could lead to more migration (Appendix A, Figure A8). However, since migration could help households obtain better economic opportunities and escape poverty, our estimation results could be considered as the net impacts of hotter temperature (after factoring in the beneficial effects of migration on poverty reduction).

> 10 The CMIP6 models capture future trends in climate change under alternative scenarios of human activities. It has been bias-corrected and downscaled to ERA-5 to address deficiencies identified in earlier models like Global Meteorological Forcing Dataset (GMFD) and NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). This data set aligns with the emissions scenarios of the 2021-2023 IPCC report, rendering it more current and relevant than previous models which were based on the scenarios of the 2013 IPCC report (IPCC, 2021).

18
Table A13: Relationship between energy management and energy intensity in quantity terms (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)<br>High EI|(2)<br>Moderate EI|(3)<br>Low EI|(4)<br>High KI|(5)<br>Low KI|
|---|---|---|---|---|---|
|Energy|0.447<sup>_∗_</sup>|0.088|0.015|0.223<sup>_∗_</sup>|0.152|
||(0.245)|(0.113)|(0.178)|(0.116)|(0.162)|
|General|0.178|0.371<sup>_∗∗_</sup>|0.966<sup>_∗∗∗_</sup>|0.214|-0.248|
||(0.367)|(0.177)|(0.297)|(0.164)|(0.280)|
|No. Employees|0.492<sup>_∗∗_</sup>|0.307<sup>_∗∗∗_</sup>|0.076|0.420<sup>_∗∗∗_</sup>|0.550<sup>_∗∗∗_</sup>|
||(0.217)|(0.117)|(0.178)|(0.117)|(0.179)|
|Capital Stock|0.339<sup>_∗∗∗_</sup><br>(0.083)|0.157<sup>_∗∗∗_</sup><br>(0.043)|0.250<sup>_∗∗∗_</sup><br>(0.081)|||
|Log(Sales)|-0.735<sup>_∗∗∗_</sup>|-0.633<sup>_∗∗∗_</sup>|-0.482<sup>_∗∗∗_</sup>|-0.636<sup>_∗∗∗_</sup>|-0.568<sup>_∗∗∗_</sup>|
||(0.145)|(0.076)|(0.129)|(0.083)|(0.118)|
|3-digit sector|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|
|Observations|381|1016|410|712|441|
|Adjusted _R_<sup>2</sup>|0.376|0.320|0.357|0.334|0.293|
|df<br>~~m~~|43|47|46|43|48|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 55
Tabte 1040 EFFECTS OF FIVE YEAR SUSTAINED INCREASE IN FINANCIAL FLOWS ON OUTPUT AND DOMESTIC ABSORPTION OF THE INDUSTRIALIZED AND DEVELOPING COUNTRIES

||U.S|.|GERN|ANY|JAP|AN|Large I<br>Countri|ndust.<br>estLI)|Develo<br>Countr|ping<br>ies|
|---|---|---|---|---|---|---|---|---|---|---|
|PERIODS|(1)|(2)|(1)|(2)|(1)|(2)|(1)|(2)|(1)|(2)|
|1988|.27|0.11|.43|0.22|.43|0.20|.43|0.21|.02|0.75|
|1989|.33|0.16|.49|0.34|.49|0.30|.50|0.33|.05|0.66|
|1990|.31|0.15|.41|0.29|.42|0.26|.38|0.24|.05|0.58|
|1991|.23|0.11|.28|0.17|.30|0.17|.22|0.11|.04|0.49|
|1992|.11|0.05|.14|0.05|.20|0.09|.13|0.02|.03|0.42|

*Column (1) shows the growth rates of real GNP, and Column (2) shows the absorption growth rates.

*Column (1) is repeated from Table 9.

*Column (1) for DC region are real GWP growth rates.
America”-type provisions in national stimulus packages or export subsidies) can be use to force changes in trade flows. But they often lead to retaliatory measures from trading partners. In May 2009 for instance, the US decided to impose new tariffs on steel pipe imports from China. A few weeks later, China retaliated by slapping new barriers on U.S. and European Union (EU) exports of some industrial chemicals used to make nylon and polyester resin. This prompted the EU in turn to restrict imports of steel pipe from China... It appears that the count of newly imposed protectionist policies has been on the rise recently.17 Moreover, in a country with a flexible exchange rate system such as the US, the theoretical reduction in the trade deficit that might be gained from trade restrictions would be offset by an appreciation of the dollar in reaction to trade restrictions.

The second possibility, which is to induce a switching of expenditures through a real depreciation of the exchange rate, seems more realistic. But there are several possible ways to proceed, each carrying its own risks. The typical textbook recommendation would be to reduce the value of the dollar through a mix of macroeconomic policies (easing of monetary policy and tightening of fiscal policy, compensated by expansionary fiscal policies abroad). In theory, this could lower the value of the dollar without impacting negatively the pace of growth in the US, and lead to an improvement in the trade and current account balance. But besides the difficulty of orchestrating such a delicate coordination of policies across boundaries, the economic and political context in the US is not propitious to fiscal adjustment— especially one based on expenditure restraint. In order to battle recession and avoid a slump like Japan suffered in the 1990s, the US authorities have implemented a very aggressive fiscal policy, with large and costly new economic stimulus and financial stability plans. The trajectory of fiscal policy is unlikely to change soon given the size of entitlement programs, the projected cost of the healthcare reform and other programs under consideration such as a one-time payment to Social Security recipients and the extension of the home buyers tax break.

When fiscal correction is difficult to implement, the only option left is that of real exchange rate depreciation mainly through market mechanisms. There has always been a lively debate on whether a depreciation of the dollar is actually needed to achieve a substantial reduction of the US current account deficit in a world characterized by integrated capital markets. To this day, many economists and policymakers support the popular view that world imbalances could be remedied through increased demand in surplus countries and reduced demand in deficit countries without real exchange rate adjustment. That line of argument is

> 17 Bown (2009) examines newly available data from the Global Antidumping Database tracking the worldwide use of trade remedies such as antidumping, countervailing duties, global safeguards and China-specific safeguards in recent months. He finds an increase in resort to these instruments.

18
Krishnan, Nandini, Sergio Olivieri, and Racha Ramadan. 2019. "Estimating the Welfare Costs of Reforming the Iraq Public Distribution System: A Mixed Demand Approach." _The Journal of Development Studies_ 91-106.

- Lechner, Michael. 2008. "A Note on the Common Support Problem in Applied Evaluation Studies." _Annales d'Économie et de Statistique_ no. 91/92: 217–235.

Perkins, Susan M., Susan M. Perkins, Wanzhu Tu, Wanzhu Tu, Michael G. Underhill, Xiao Hua Zhou, Xiao Hua Zhou, Michael D. Murray, and Michael D. Murray. 2000. "The use of propensity scores in pharmacoepidemiologic research." _Pharmacoepidemiology and Drug Safety_ 9 (2): 93-101. Accessed 9 2, 2019. https://ncbi.nlm.nih.gov/pubmed/19025807.

Phadera, Lokendra, Hope C Michelson, Alex E Winter-Nelson, and Peter D Goldsmith. 2019. "Do asset transfers build household resilience." _Journal of Development Economics_ 138: 205-227. Accessed 9 4, 2019. https://sciencedirect.com/science/article/pii/s0304387818304772.

Rahman, Andaleeb. 2016. "Universal food security program and nutritional intake: Evidence from the hunger prone KBK districts in Odisha." _Food Policy_ 63: 73-86. doi:https://doi.org/10.1016/j.foodpol.2016.07.003.

Rosenbaum, Paul R., and Donald B. Rubin. 1983. "The central role of the propensity score in observational studies for causal effects." _Biometrika_ 70 (1): 41-55. Accessed 8 30, 2019. https://academic.oup.com/biomet/article/70/1/41/240879.

Shrinivas, Aditya, Kathy Baylis, Ben Crost, and Prabhu Pingali. 2018. "Do staple food subsidies improve nutrition?" _Unpublished manuscript. Retrieved from http://barrett. dyson. cornell. edu/NEUDC/paper\_520. pdf._ Smith, Jeffrey A., and Petra E. Todd. 2005a. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?" _Journal of Econometrics_ 125 (1): 305-353. Accessed 9 2, 2019. https://sciencedirect.com/science/article/pii/s030440760400082x.

- Smith, Jeffrey A., and Petra E. Todd. 2001. "Reconciling Conflicting Evidence on the Performance of Propensity-Score Matching Methods." _The American Economic Review_ 91 (2): 112-118. Accessed 9 2, 2019. http://uh.edu/~adkugler/smith&todd.pdf.

Smith, Jeffrey A., and Petra E. Todd. 2005b. "Rejoinder." _Journal of Econometrics_ 125 (2005): 365–375.

- Svedberg, Peter. 2012. "Reforming or replacing the public distribution system with cash transfers?" _Economic and Political Weekly_ 47 (7): 53--62.

- World Bank. 2019. _Arrested Development : Conflict, Displacement, and Welfare in Iraq._ Washington DC: World Bank.

World Bank. 2010. _Confronting Poverty in Iraq._ Washington DC: World Bank.

- World Bank. 2018. _Damage and Needs Assessment of Affected Governorates._ Washington DC: World Bank.

- World Bank. 2018. _Iraq Economic Monitor: Toward Reconstruction, Economic Recovery and Fostering Social Cohesion._ Washington D.C.: World Bank.

22
# Multilateral Debt Relief through the Eyes of Financial Markets

Claudio Raddatz<sup>_∗_</sup> The World Bank Abstract The economic benets of debt relief for recipient countries have been the subject of arduous debate, at least partly motivated by the diculty of identifying the causal eect of debt relief on economic performance given that performance itself may drive the decision to grant relief. This paper conducts an event study to assess the economic consequences of multilateral debt relief for recipient countries that is robust to these reverse causality issues. It estimates the response of the stock prices of South African multinationals with subsidiaries in those countries to the announcement of debt relief initiatives, and shows that stock prices exhibit a signicant increase above those of other rms, especially around the launching of the recent Multilateral Debt Relief Initiative. The improvement in nancial markets' assessment of the value of these multinationals is consistent with lower expected levels of future taxation in the recipient countries. Overall, the results are consistent with the debt overhang argument for debt relief.

> _∗_ E-mail address: craddatz@worldbank.org. I am extremely grateful to Shinsuke Uchida for superb research assistance. I am also grateful to those South African multinational companies that gently provided information for this project: ABSA Group Ltd., Astral Foods Ltd., Bell Equipment Ltd., Blue Financial Services Ltd., Illovo Sugar Ltd., and MTN Group Ltd. Comments from seminar participants at the University of Chile, LACEA, and the World Bank are also gratefully acknowledged. All remaining errors are my responsibility. Financial support from the Japanese CTF is gratefully acknowledged. The views expressed in this paper are the author's only and do not necessarily represent those of the World Bank, its Executive Directors, or the countries they represent.
**Table 3: Distribution of benefits in South Africa**

|**Name of**<br>**case**|**Description**<br>**of case**|**N**<br>**beneficiaries**|**Percent**<br>**under-**|**N under-**<br>**privileged**|**“Underprivileged”**|
|---|---|---|---|---|---|
||**Health(all P**<br>|**rovision; allpr **<br>|**privileged**<br>**oduce indir**<br>|<br>**ect-policy eff**<br>|**ects)**<br>|
|Van Biljon|Treatment<br>for HIV+<br>prisoners|57600|100%|57600|Prisoner|
|TAC|PMTCT for<br>HIV+<br>pregnant<br>women|55000|69%|37950|<$132/mo<br>**household**income|
|Interim<br>procurement|Expedited<br>procurement<br>of ARVs|42500|69%|29325|<$132/mo<br>**household**income|
|Hazel Tau|Access to<br>ART<br>generics|119000|69%|82110|<$132/mo<br>**household**income|
|Total Health||274100|76%|206985||
||**Education(all**|**Provision; all**|**produce ind**|**irect-policy e**|**ffects)**|
|Premier<br>Mpumalanga|Subsidies<br>for poor<br>children|22500|100%|22500|Subsidies designed<br>for ―indigent‖<br>children|
|Watchenuka|School age<br>asylum<br>seekers|50625|100%|50625|Asylum seeker<br>pending decision|
|Total<br>Education||73125|100%|73125||
|**Total**||**347225**|**80.67%**|**280110**||

# _Indonesia_

Susanti (2008) identifies a total of seven right-to-health and five right-to-education cases in Indonesia since the beginning of the transition to democratic rule in 1999. Five of these did not have a measurable impact: two were efforts to sue for medical negligence (both were rejected), one was a claim to avoid the relocation of a school due to a land swap favoring developers (rejected by the court), one involved the student protests at a university (the students won the case in court, but it had no wider policy impact); and one was an abstract challenge to the new law on a national social security scheme (accepted by the court, but the scheme remains 25
several non-oil-exporting focus countries. Nevertheless, the competitiveness of the non-oil sector in major oil-exporting developing economies is often impeded by limited access to bank credit and other structural impediments, as well as overall Dutch disease effects. Countries with a commodities-dominated economic structure often have a shallow financial sector with a very limited role in financing non-oil economic activities (see, for example, IMF 2007a). A recent IMF surveillance mission in the CEMAC region observed that the expansion of Chad’s oil sector correlated with a decline in the competitiveness of other sectors (IMF 2007b).

In all of the focus countries, the level of financial intermediation, measured by total private credit by banks and nonbank financial institutions as a percentage of GDP, is significantly below that of South Africa, where the ratio stands at 145 percent (table 2.5). Cape Verde and Namibia have the next-highest ratios, at 64 percent and 62 percent.<sup>15</sup> South Africa’s significantly higher ratio largely reflects its sophisticated, highly developed nonbank financial subsector.

As well as being limited in size, bank lending to the private sector tends to be short in tenor for all but the most select bank clients. That said, maturities vary considerably by client, bank lender, and lending purpose.<sup>**16**</sup> In Benin, Burkina Faso, Côte d’Ivoire, Niger, and Senegal, maturity terms for infrastructure project loans vary greatly depending on the type of infrastructure financed, with some maturities in excess of 10 years.<sup>17</sup> Loans arranged by a syndicate of banks, international and local, generally have longer maturities. Syndicated lending to the focus countries had grown in recent years, but still remains relatively limited, except to borrowers in South Africa (see chapter 3).

> 15 Private credit by nonbank financial institutions data are only available from IMF IFS for Kenya (2006), Malawi (2006), South Africa (2005), Ethiopia (2006), and Chile (2006). Because claims on the private sector by nonbank financial institutions is not compiled by IMF IFS for many of the AICD countries, and given the generally small size of most of these countries’ nonbank financial subsectors, in these cases the value for private credit by deposit money banks can be used as a rough approximation for private credit by deposit money banks and other financial institutions.

> 16 In Lesotho, for example, mortgage loans carry the longest maturity terms available for bank loans (maximum 20 years), followed by vehicle finance loans, with a maximum of five years (Central Bank of Lesotho).

> 17 According to the Banking Commission of the West African Economic and Monetary Union’s regional central bank, BCEAO (Banque centrale des états de l’Afrique de l’ouest).

15
added. Foreign portfolio investment can also be used (reflecting capitalization), but the association is arguably looser.

Indicators of improved management for non-divested SOEs focus on profitability and efficiency. Measuring profitability is relatively straightforward, as the difference between earnings and total operating costs. Interpretation should be cautious because, for several reasons (distributive justice, political constraints), SOEs may be required only to recover costs and not to generate profits. The customary indicators of efficiency are average labor productivity, defined as value added divided by employment. An indirect measure of increased efficiency is the observed reduction in relative prices for goods and services (e.g., lower tariffs) or quality improvements (e.g., shorter waiting lists). _Infrastructure_ Measuring the outcome of the reforms in infrastructure use the same set of indicators for public firms. As documented in World Bank (1994), these indicators capture the dramatic changes in efficiency and profitability as a result of reforms in telecommunications, transport, water provision, and electricity in several developing economies. Indicators of the impact of reforms in the availability of infrastructure typically consider measures such as kilometers of roads, kilometers of railway lines, number of telephones, electricity generating capacity, access to potable water and sewerage, irrigation and waste disposal (see Canning, 1998). These indicators can be supplemented with some measures of infrastructure quality to provide a richer assessment of the reforms (e.g., percentage of roads in poor condition, percentage of local phone calls that are unsuccessful, percentage of availability of diesel locomotives, and percentage of electricity lost from the system).

# **G.  LEGAL AND REGULATORY FRAMEWORK**

**_1. General Principles_ .** The legal and regulatory framework comprises the laws, regulations, and legal institutions needed to facilitate dynamic economic development and environmental protection in a decentralized economy. In the absence of market failures, unregulated private initiative leads to the creation and exploitation of profitable opportunities through market activities. The legal framework should provide the basic institutions for a minimum-cost operation of the market, including the protection of property rights, the provision of expedite conflict resolution mechanisms, and the

33
**When the Bureaucrats Move out of Business:** A Cost-Benefit Assessment of Labor Retrenchment in Chinal

Yi Chen<sup>2</sup> Ishac Diwan<sup>3</sup> The World Bank Classification code: JO, J2, LO, J4, L3 5 key words: china and labor, china and state owned enterprise, china labor market, china and labor productivity I We would like to thank Pieter Bottelier, Gary Jefferson, Martin Rama, and Yan Wang for their comments and suggestions. We also like to thank Xibo Fan for his expert assistance in the statistical part of the analysis.

2 Yi Chen, 1818 H Street, N.W. Washington, DC 20433, Email address: Ychen3CWworldbank.org. Tel. 202-4733949.

3Ishac Diwan, 1818 H Street, N.W. Washington, DC 20433, Email address: idiwan@worldbank.org. Tel. 202-4733120