rafmacalaba/datause-encoder-data huggingface dataset:
"...drought [in "rice countries"] is, perhaps, scarce ever so universal as necessarily to occasion a famine, if the government would allow a free trade."–Adam Smith (1776, IV.5.45)

# 1 Introduction

Random ‡uctuations in weather may have severe adverse e¤ects on agricultural producers and consumers. How can policy makers mitigate these impacts? Recent empirical research suggests that greater openness to trade alleviates the adverse e¤ects of weather shocks on domestic consumers. Using district-level panel data for colonial India in 1875-1919, and exploiting the construction of the railroad network to identify the e¤ects of increased openness, Burgess and Donaldson (2011) document that the arrival of railroads dramatically constrained the ability of rainfall shortages to cause famine.<sup>1</sup> But while this evidence points to the existence of a causal link between openness and weather-related famine, import tari¤s on agricultural goods remain high in many nations, and relatively little is known about the extent to which countries use trade policy strategically to mitigate the impacts of weather shocks on domestic welfare.<sup>2</sup> In this paper, we examine whether and how rainfall shocks a¤ect tari¤ setting in the agricultural sector. To identify key mechanisms at play, we ...rst set up a model of international oligopoly in which domestic and foreign agricultural producers compete in the home market. International trade is potentially costly due to import tari¤s optimally set by the domestic policy maker. A rainfall shortage increases the marginal costs of domestic producers, thus generating a shortfall in agricultural output that foreign producers have an incentive to meet. Consequently, the shock a¤ects the marginal e¤ects of import tari¤s on tari¤ revenue, domestic pro...ts and the consumer surplus, and thereby the optimal policy response.

We ...nd that the impact of a rainfall shock on optimal import tari¤s is not clear-cut. A rainfall shortage leads to a higher volume of agricultural imports and therefore higher marginal tari¤ revenues. All else equal, this increases the optimal tari¤. On the other hand, by making home production more costly, the shock reduces incentives for using tari¤s as an instrument to shift rents from foreign to domestic producers. This leads, ceteris paribus, to a lower optimal tari¤. Finally, a rainfall shortage also reduces domestic consumers’surplus due to a lower total supply of the agricultural good. This increases (reduces) the negative e¤ect of import tari¤s on domestic consumers if food demand is

> 1 This evidence is consistent with Donaldson (2013), who shows that railroads contributed to reduce the exposure of agricultural prices and real incomes to rainfall shocks.

> 2 Gibson et al. (2001) emphasize that high protection for agricultural commodities in the form of tari¤s continues to be the major factor restricting world trade.

2
Policy Research Working Paper 7014

# **Abstract**

Conventional wisdom holds that Sub-Saharan African farmers use few modern inputs despite the fact that most growth-inducing and poverty-reducing agricultural growth in the region is expected to come largely from expanded use of inputs that embody improved technologies, particularly improved seed, fertilizers and other agro-chemicals, machinery, and irrigation. Yet following several years of high food prices, concerted policy efforts to intensify fertilizer and hybrid seed use, and increased public and private investment in agriculture, how low is modern input use in Africa really? This paper revisits Africa’s agricultural input landscape, exploiting the unique, recently collected, nationally representative, agriculturally intensive, and cross-country comparable Living Standard Measurement Study-Integrated Surveys on Agriculture covering six countries in the region (Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda). The study uses data from more than 22,000 households and 62,000 plots to investigate a range of commonly held conceptions about modern input use in Africa, distilling the most striking and important findings into 10 key takeaway descriptive results.

This paper is a product of the “Agriculture in Africa-Telling Facts from Myths” project managed by the Office of the Chief Economist, Africa Region of the World Bank, in collaboration with the African Development Bank, the Alliance for a Green Revolution in Africa, Cornell University, Food and Agriculture Organization, Maastricht School of Management, Trento University, University of Pretoria, and the University of Rome Tor Vergata. Co-funding by the African Development Bank is gratefully acknowledged. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author of the paper may be contacted at mbs282@cornell.edu, the task-team leader of the overall project at lchristiaensen@worldbank.org.

_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 quickly, 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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Making a Market: **Mass** Privatizatlon In the Czech and Slovak- Republics

# Table of Contents

|I.|In|tro<br>d u ctio<br>n<br>........................................|1|
|---|---|---|---|
|11.|l<br>he|Coupon Scheme in Context|2|
|m.|The|Supply Side: WhatWas Sold? .........................|4|
||A.<br>|Project Preparation: Encouraging Competition ....<br>......<br>|...<br>4|
||B.<br>|The Coupon Scheme: What was on Offer? ...............<br>|6|
||D.|Characteristics of Enterprises ........................|7|
|IV.|The|Demand Side: Who Bought What? .........<br>..<br>...........|<br>12|
||A.<br>|Citizens ...........<br>..........................<br> <br>|12|
||B.<br>|InvestmentlPrivatization<br>Funds .......................<br>|13|
||C.<br>|Pricing Policy .................................<br>|14|
||D.|An Assessment of the Bidding Rounds ..................|15|
|V.|Conc|lusions<br>.....<br>...................................|19|
||A.<br>|Speed .......................................<br>|20|
||B.<br>|Equity .. .<br>...................................<br>|21|
||C.<br>|Corporate Governance .23<br>||
||D.|Concluding Remarks ...........|27|
|Ann|exes|||
||A.|Preparation of Enterprises for Privatization .30||
||B.|Data on Enterprise Characteristics<br>.34||
||C.|Bidding Dynamics .41||
|||Round 0: Power to the IPFs .41<br>||
|||Round 1: Bidding Bonanza .42||
|||Round 2: Investment Pause and Quality Focus .44||
|||<br>Round 3: Market Divergence and Unrequited<br>||
|||Bargain Hunting .46||
|||<br>Round 4: An Emerging Equilibrium .47<br>||
|||Round 5: Locking into the Market Before it<br>is Too Late .49||
# **3.3 Reform Scenarios**

For purposes of comparison with the Doha scenarios, two benchmark simulations are developed. The first is the baseline or reference scenario, described above; the second is a scenario of complete merchandise trade reform that provides the maximum potential gain from eliminating import protection (and export subsidies and domestic support in agriculture). To provide a more complete picture of the impacts of multilateral trade reform, the global reform scenario is decomposed into partial shocks. The decompositions look at the sources of gain by broad sector—agriculture and food, textile and clothing, and other manufacturing—and by broad income classification—high-income countries versus developing countries.<sup>15</sup> The remaining scenarios all pertain to some version of a possible Doha accord. The central Doha scenario is the same one explored throughout this book and outlined in detail in Anderson and Martin (2005). Some attention is also devoted to an alternative scenario, Doha-all, in which developing countries and LDCs cut their bound tariffs at the same rate as for industrial countries. This is of particular interest, because it represents a concrete instance of deeper developing-country commitments in the Doha Round.

These two Doha scenarios entail relatively modest reform, as can be seen by the average cut in protection in table 2.7 (columns 3 and 4) in Anderson and Martin (2005). For high-income countries, the average cut for both Doha scenarios is 1.3 percentage points (from 2.9 percent to 1.6 percent). This is largely driven by the relatively small tariffs in other manufacturing and the large weight this sector represents in total trade for high-income countries. The cuts in

> 15. For most purposes, this paper uses the World Bank’s definition of developing countries. One exception is the EU-accession countries that are incorporated into the EU aggregate. Another is that this paper also reports the WTOaccepted definition of developing countries that includes the newly industrialized economies of East Asia.

9
# Table 6.10 (Con't.)

||Bas<br>in|ed on Value-Added<br>Domestic Prices|Based<br>in|on Value-Ad<br>World Prices|ded|
|---|---|---|---|---|---|
||1|2<br>3|4|_5_|6|
|% Exports|-1.492<br>(1291)|-1284<br>0.171<br>(1.159)<br>(0.316)|-1.528<br>(1295)|-1.233<br>(1.159)|0.192<br>(0318)|
|Inouts:||||||
|In(Senegalese<br>Skinled<br>and Unsilled)|12??<br>(0.646)||1.262<br>(0.648)|||
|In (Other African<br>Skilled and Unskiled)|-3.9E4<br>(631E-4)||-3.6E-4<br>(6.3E-4)|||
|In (Non-AficanSkilled<br>and Unskilled)|-3213-4<br>(52E.4)||-3.41-4<br>(5213-4)|||
|In (Senegalese Middle<br>and Upper Management|-7.2E-5<br>_(SAE-4)_||-9.SE1S<br>(5.4E3l)|||
|In(Other Afrcan Middle<br>and Upper Management)<br>In (Non-African Middle<br>andUpper Management)|**_S.31-5_**<br>(45EM4)<br>-0.002'<br>(43-4)||_42E-S_<br>(45SE-4)<br>-.002'<br>_(4AP-4)_|||
|In (Unsklled Wodcers)||036S<br>(0.423)<br>0.906!<br>D<br>_(0360)_||0382<br>(0422)<br>J|_0849_<br>(0.362)|
|In (Skilled Workers)||4.6B14<br>(8.4134)||4.7E.4<br>_(8.4E4)_||
- Andrews, M., Pritchett, L., Woolcock, M., 2013. Escaping capability traps through problem driven iterative adaptation (PDIA). World Development 51, 234 – 244.

- Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58 (2), 277–97.

- Arellano, M., Bover, O., 1995. Another look at the instrumental variable estimation of error-components models. Journal of Econometrics 68 (1), 29–51.

- Bambaci, J., Saront, T., Tommasi, M., 2002. The political economy of economic reforms in Argentina. Journal of Policy Refom 2 (2), 75–88.

- Bazzi, S., Clemens, M. A., 2013. Blunt instruments: Avoiding common pitfalls in identifying the causes of economic growth. American Economic Journal: Macroeconomics 5 (2), 152–86.

- Besley, T., Persson, T., 2011. Pillars of Prosperity. Princeton University Press, Princeton and Oxford.

- Blum, J. R., 2014. What Factors Predict How Public Sector Project Perform? A Review of the World Bank’s Public Sector Management Portfolio. World Bank Policy Research Working Paper 6798, World Bank, Washington DC.

- Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87 (1), 115–143.

- Bond, S. R., Hoeffler, A., Temple, J., 2001. GMM estimation of empirical growth models. C.E.P.R. Discussion Papers 3048, Center for Economic Policy Research.

- Boone, P., 1996. Politics and the effectiveness of foreign aid. European Economic Review 40 (2), 289 – 329.

- Brautigam, D. A., Knack, S., 2004. Foreign aid, institutions, and governance in Sub-Saharan Africa. Economic Development and Cultural Change 52 (2), pp. 255–285.

- Brinkerhoff, D. W., 2000. Assessing political will for anti-corruption efforts: An analytic framework. Public Administration and Development 20 (3), 239–252.

- Bun, M. J. G., Windmeijer, F., 2010. The weak instrument problem of the system GMM estimator in dynamic panel data models. Econometrics Journal 13 (1), 95–126.

- Bunse, S., Fritz, V., 2012. Making public sector reforms work. Policy Research Working Paper 6174, The World Bank.

- Burnside, C., Dollar, D., 2000. Aid, policies, and growth. The American Economic Review 90 (4), 847–868.

- Cashel-Cordo, P., Craig, S. G., 1990. The public sector impact of international resource transfers. Journal of Development Economics 32 (1), 17 – 42.

26
# **IV. The Benefits of Regionalizing Regulatory Policy**

Until the 1980s, conventional wisdom held that telecommunications was a natural monopoly in which competition was bound to be inefficient. Now this conventional wisdom is known to be incorrect. Even in less developed nations with low telephone penetration, substantial competition already exists in most telecommunications services. Nevertheless, in some components of telecommunications competition is weak, in part because of the inheritance of a former state-owned monopoly provider of wire-line access and backbone transmission services. Despite the irresistible spread of technology-driven competition, direct regulation of some aspects of telecommunications is necessary until the transition to a reasonably competitive sector is complete. Regulation can ensure fair treatment of customers who still lack the protection that comes from the availability of competitive alternatives. Regulation also can ensure nondiscriminatory access of wouldbe competitors to bottleneck telecommunications facilities that are controlled by the incumbent firms. If the incumbent telecommunications entities were to operate completely without regulatory restraint, they could use their control of bottleneck facilities to force rivals to bend to their will or to destroy those rivals altogether (box 1).

## **Box 1 Ghana Telecom**

In comparison with other Africa countries, Ghana has done relatively well in fixed line telephony. With respect to mobile telephony, Ghana’s performance is not nearly as positive. Mobile telephony in Ghana started early within an African context. However, the subsequent development has not been as fast as in many other African countries.

There is probably no simple explanation for this situation, but an obvious explanation for the relatively good performance in fixed access is the partial privatization of Ghana telecom and the accompanying requirements on extensions on the number of subscribers. With regard to the less positive performance in mobile communications, one explanation could be the unclear regulatory situation in Ghana, especially in the area of interconnection. All mobile operators, the second fixed operator Westel, and the rural operator Capital telecom have had great problems with Ghana Telecom over interconnection. And, the regulator has proved to be ineffective in solving the problem. A regulator was created in connection with the overall change in the telecom environment in Ghana in 1997, but it has never had any real strength to intervene in the market in order to create a more level playing field among the operators.

_Source_ : Frempong and Henten (2004).

32
# **Gender equality, poverty and economic growth**

Andrew Morrison Dhushyanth Raju Nistha Sinha This background paper was prepared as an input into the World Bank’s _Global Monitoring Report 2007_ . Helpful comments have been received from Mayra Buvinic, Monica Das Gupta, Elizabeth King and Sulekha Patel.

The views expressed here are those of the authors and should not be attributed to the World Bank, its Executive Directors or the governments they represent.
income non-poor households consume less than energy non-poor households, not just in terms of total energy but also individual sources (except for kerosene which do not vary). This is another indication that there is no one-to-one relationship between energy poverty and income poverty. Question is, how much cooking and lighting needs can possibly be satisfied with the level of energy that the energy poor households consume?

Cooking in rural Bangladesh is done almost exclusively using biomass (mostly fuelwood, sometimes other inferior fuels, for example, leaves, crop residue, etc.). To investigate households’ cooking energy need we consider an average rural household – that is, a household that has five members, uses fuelwood in traditional stoves for general purpose cooking, cooks on an average two meals a day, and those meals consist of rice, lentil, and one vegetable regularly, and fish or meat occasionally. The cooking energy need in the form of end-use energy for such a household appears to be 10.66 kgOE.<sup>19</sup> That is more than the biomass energy consumption of energy poor households of rural Bangladesh (8.64 kgOE per month). It thus seems that the energy poor households cook less than what is a normal practice for an average rural household in Bangladesh.

Speaking of the lighting energy need, there are typically two major sources – electricity for those who have it (with kerosene as a backup) and kerosene for those who do not have electricity. Among the energy poor households, those who have electricity consume 29.3 kWh of electricity per month, and those without electricity consume about 0.35 kgOE of end-use energy per month from kerosene, used mostly in wick lamps. For simplicity, we assume that these energy sources are used completely for lighting. The amount of electricity that the energy poor households consume (29.3 kWh) typically provides about 17 hours of lighting per day from a 60w incandescent bulb (which is

> 19 This figure is based on a study of 125 households in Nepal, which carefully measured the energy requirements for cooking of main meals in those households (Pokharel, 2004). It assumes that rice, vegetables and lentil are cooked twice a day everyday, while meat or fish is cooked once a week. For an average household of six members cooking in a traditional fuelwood stove the average annual useful energy requirement came out to be 6.664 Giga Joules. For a rural Bangladeshi household (which compares well with a Nepalese household) of five members this translates to 10.66 kgOE/month.

27
|**Table 5 a**|||**Correlatio**|**ns**||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||**Mandatory**<br>**offer range**|**Range**<br>**where**<br>**intention to**<br>**control is**<br>**known**|<br>**Range where**<br>**intention to**<br>**take private**<br>**is known**|<br>**Off-**<br>**exchange**<br>**price**<br>**disclosed**|**Rules apply**<br>**to non-listed**<br>**firms**<br>**Fai**<br>**mi**|**r price for**<br>**nority**|<br>**Fair price**<br>**for all**<br>**classes**|<br>**Offer**<br>**disclosure**<br>**index**|**Fair price fo**<br>**non-**<br>**tendering**<br>**investors**|**r**<br>**Appraisal**<br>**rights after a**<br>**merger**|<br>**Sell-out**<br>**provisions**|
|Range – control|52%<sup>a</sup>|||||||||||
|Range –<br>take private|53%<sup>a</sup>|56%<sup>a</sup>||||||||||
|Off-exchange<br>||||||||||||
|price discl.|64%<sup>b</sup>|69%<sup>a</sup>|91%<sup>a</sup>|||||||||
|Rules apply to<br>||||||||||||
|non-listed|20%|13%|31%|61%<sup>b</sup>||||||||
|Fair price for||||||||||||
|minority<br>Fair price for all|52%<sup>a</sup>|40%|32%|37%|25%|||||||
|classes|33%|24%|30%|52%|41%|16%||||||
|Mand. offer||||||||||||
|disclosure<br>Fair price for|57%<sup>a</sup>|45%<sup>c</sup>|62%<sup>a</sup>|82%<sup>a</sup>|61%<sup>a</sup>|34%|33%|||||
|nontendering<br>Appraisal rights|33%<br> <br>21%|17%<br>42%|26%<br>25%|49%<br>65%<sup>b</sup>|53%<sup>a</sup><br>39%|13%<br>15%|28%<br>26%|48%<sup>b</sup><br>36%|27%|||
|Sell-out||||||||||||
|provisions<br>Anti-takeover|15%|6%|21%|38%|43%|32%|28%|37%|33%|17%||
|tactics|43%|28%|46%<sup>c</sup>|58%<sup>c</sup>|38%|21%|29%|56%<sup>a</sup>|32%|37%|54%<sup>a</sup>|

_Note: Starred correlation coefficients are Bonferroni significant as follows: 1% (a), 5% (b), 10% (c)._

|**Table 5 b**||**C**|**orrelations**||||||||
|---|---|---|---|---|---|---|---|---|---|---|
||**Listed firms**<br>**to million**<br>**population **|**Market cap of**<br>**stock exchange**<br>**to GDP**|**Traded value of**<br>**stock exchange**<br>**to GDP**|**Median**<br>**control value**<br>**(D-Z04)**|**Estmates**<br>**control value**<br>**(N 03)**|**Takeover**<br>**premium**<br>**(R-V 04)**|**Takeover**<br>**volume**<br>**(R-V 04)**|**Hostile**<br>**takeovers**<br>**(R-V 04)**|**Rule of**<br>**law**|**Investor**<br>**Protection**|
|Takeover<br>Index|36%|56%<sup>a</sup>|63%<sup>a</sup>|-35%|-53%|-23%|55%<sup>b</sup>|53%<sup>b</sup>|66%<sup>a</sup>|52%<sup>b</sup>|

_Note: Starred correlation coefficients are Bonferroni significant as follows: 1% (a), 5% (b), 10% (c)._ _Note: Data in Panel B from World Bank World Development Indicators, Dyck and Zingales (2004), Nenova (2003), Rossi and Volpin (2004), La Porta et al (1998)._ 35
than their static counterparts. This effect of the idiosyncratic shocks is mitigated by the yearon-year differences in weather shocks explained earlier. As a result, the expected poverty gap is closer to the poverty gap in 2016 (IHS 4) than it is in 2010 (IHS 3).<sup>17</sup> Figure 5 shows vulnerability to poverty and static poverty at the district level in 2016/17. In some districts, vulnerability is higher than static poverty. The reverse is true in other districts.<sup>18</sup>

**Table 5: Vulnerability to Poverty and Static Poverty**

||2004/05 (IHS 2)|2010/11 (IHS 3)|2015/16 (IHS 4)|
|---|---|---|---|
|**Static Moderate-Poverty**||||
|Headcount poverty|52.9|51.0|53.7|
||(0.5)|(0.5)|(0.5)|
|Poverty gap|18.1|19.1|18.3|
||(0.2)|(0.2)|(0.2)|
|Severity of poverty|8.2|9.4|8.3|
||(0.1)|(0.1)|(0.1)|
|**Dynamic Moderate-Poverty**||||
|Expected headcount of poverty|52.5|53.8|52.0|
||(0.4)|(0.4)|(0.4)|
|Pr(Moderate poverty)_>_0.5|53.1|54.8|52.8|
||(0.5)|(0.5)|(0.5)|
|Pr(Moderate poverty)_>_0.29|58.6|59.3|58.5|
||(0.5)|(0.5)|(0.5)|
|Expected poverty gap|20.9|23.6|20.6|
||(0.2)|(0.2)|(0.2)|
|Expected severity of poverty|11.5|14.0|11.5|
||(0.2)|(0.2)|(0.2)|
|Observations|11,281|12,271|12,447|

Note: The poverty measures displayed on this table are based on the authors’ calculation. They rely on consumption without food and cash aid. Therefore, they are not the official statistics. The vulnerability results are based on 4000 simulations in which weather and idiosyncratic shocks were randomly drawn. Drought and excess rainfall were drawn independently.

> 17 We present ultra-poverty results and their decomposition by regions and rural/urban in the annex. The results are available upon request.

> 18 District-level maps for the other years are also presented in the annex and are available upon request.

23
20 Table 5: Selected Estimates of Cross Elasticities

||(A|ggregate Data)||
|---|---|---|---|
|Authors|Modes|Cros Elasticities<br>|Remarks|
|Oum (1979a)|Ril-Truck<br>Truck-Rail<br>Rail-Waterway<br>Waterway-Rail<br>Thuck-Waterway|-0.10to +0.14<br>-0.88 to +0.13<br>+0.15 to +0.20<br>+0.61 to +0.86<br>-0.23 to +0.03|Aggregate freight transport<br>demand in Canada,**cross elas-**<br>ticities reported for selected<br>years between 190S-1974.|
||Waterway-Tuck|-0.12 to +0.13||
|Oum and|Air-Bus|.0.02 to -0.01|Aggregate intercity passenger|
|Gillen (1983)|Air-Rail|+0.01 to +0.04|transport demand in Canada,|
||Bus-Air|*0.12 to -0.05|cross elsticities reported|
||Bus-Rail|-0.47 to 40.21|for selected years between|
||Rail-Air|+0.08 to +0.51|1961-1976.|
||Raii-Bus|_-I.18_to -0.17||
|Oum (1989)|Rail-Truck'|-0.18 to +0.50|Interregional freight transport|
||Truck-Rail'|-0.62 to +0.84|demand in Canada.|
||Rail-Truck6|-0.47 to +0.48||
|||Tuck-Railb|-0.26 to +0.35||
|:Aggregate co|mmodities.|||
|'Fruits, vegeta|bles and other edibl|e foods.||