rafmacalaba/datause-encoder-data huggingface dataset:
54.9%; Warid, 14.7%; Orange, 14.0%; and Smile, 1%.<sup>44</sup> Airtel is an Indian based company and MTN is headquartered in South Africa (although MTN Uganda is a subsidiary of MTN International, which is headquartered in Mauritius. Warid was acquired in 2013 by Airtel.<sup>45</sup> Orange is owned by France Telecom. Finally, Smile is owned by companies from Saudi Arabia, Capital Works (a consortium of domestic and international institutional investors) and Venere, whose nationality is unknown.<sup>46</sup> Based on the owners of Smile, we assign 0.4% of the market to nationals of Saudi Arabia, 0.1% to nationals of the United States, 0.25% of the market to Ugandan nationals, 0.10% to nationals of COMESA, 0.10% to nationals of Kenya and 0.05% to nationals of Tanzania.

For the regions in our model, this means that we have the following ownership shares: **SADC= 54.9%; ROW= 30.5%;United States, 0.1%,; Uganda, 0.25%; COMESA, 0.1%; Kenya, 0.1%; and Tanzania, 0.05%.**

# **South Africa--SADC**

We take South Africa as representative of SADC. Five companies provide mobile telephone services. The five cellular providers and the number of their subscribers in millions are Vodacom, 29.3; MTN, 25; Cell C, 11.7;, 0.5; and Telkom Mobile, 1.54.<sup>47</sup> Regarding fixed line telephone services, in 1997, the South African telecommunications, was partly privatized. Telkom has a reported 3 million fixed line subscribers. A Second Network Operator was licensed to compete with Telkom across its spectrum of services and operates

> 44 Data are from: Uganda Communications Commission (2012), “ Annual Post, Broadcasting and Telecommunications Market Review 2011/2012.”  Available at : http://www.ucc.co.ug/files/downloads/2011%2012%20Annual%20post%20and%20Telecom%20market%20review %20report-Final.pdf

> 45 Saturday Monitor , “Airtel Buys Warid Telecom,”  April 23, 2013.  The article notes that MTN claims to have 7.7 million subscribers as of  December 2012 and claims a 51 per cent market share while Airtel says it will have 7.4 million after adding Warid’s 2.8 million customers. We base our market share calculations on the asset data, which is complete.

> 46Smile shareholders are listed on the company’s website at:  http://www.smilecoms.com/~smlcoms/pages/aboutus/shareholders.php

> 47 See http://en.wikipedia.org/wiki/Telecommunications_in_South_Africa; market share data are calculated from data on subsribers available in Qunton Bronkhorst, “Mobile subscribers in SA: who’s eating whose lunch?” _Business Tech_ , August 14, 2013. Available at:

> http://businesstech.co.za/news/mobile/44164/mobile-subscribers-in-sa-whos-eating-whose-lunch/

107
**_--**

# A. Summary

The rural sector in Pakistan accounts for more than 70 percent of total employment, and about two-thirds of rural employment is in agriculture. A healthy and well-functioning rural finance system can help in achieving two policy objectives: accelerating agricultural growth and reducing poverty. In Pakistan today, however, existing credit institutions are not helping to attain these objectives. Investment in agriculture is low, and productivity growth in agriculture therefore is stagnant.

A picture of rural finance is available from the 1985 credit survey and other previous surveys. According to these surveys, many households have been credit constrained, and no credit institutions target the poor. Less than one-third of rural households have obtained loans, and only 10 percent of these borrow from institutional sources. Among informal sources, family and friends dominate, and landlords and moneylenders account for a small and declining portion. Because average loan size from formnal sources is much larger than that from informal sources, formal sector credit accounts for a larger portion of agricultural debt than its share of households. Although the informal sector share is declining, it remains high in comparison with other Asian countries.

Virtually all institutional credit is for production and investment purposes, whereas about half of informal credit is for consumption. Both the formal and informal sectors disburse mostly short-term loans (78 percent and 96 percent, respectively), but more than half of formal sector loans are long term in value terms.

The informal sector has a far superior record in loan repayment, and most recovery are made from regular sources of income. Distress sales are uncommon. Most formal loans are land mortgages, whereas virtually all informal loans are backed by a personal guarantee. Informal sector interest rates are higher and more vanable than formal rates. The informal sector relies on its own funds, while the formal sector is funded predominantly by the State Bank of Pakistan (for the Agricultural Development Bank of Pakistan and the cooperatives) and by deposits (in the case of the commercial banks). Formal sector loans go mostly to large farmers, despite targets for small-farm lending. These targets are easily evaded by proxy loans and one-man cooperatives.
**Poy, R_Enwkmxftml Afa**

**Trade, Finance, and Industry**

WPS 590 This paper-aproductof theTrade, Finance, and Industry Division,Teclnical Department, Latin America and the Caribbean Regional **Office -** was undertaken as part of **the** World Bank's comparative study, Macroeconomic Policies: **Crisis** and Growth in the Long Run. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Margaret Kienzle, room 14-058, extension 30733 (55 pages).

Athukorala and Rajapatirana developed a framework for analyzing the relationship between domestic financial markets and the effects of trade liberalization and applied it to Sri Lanka's experience between 1977 and 1987. They found that the domestic financial market siirniricantly affects the outcome of trade liberalization.

Because Sri Lanka deregulated its interest rates when it undertook the trade liberalization, this allowed those eaming more from trade liberalization to hold financial assets rather than nontradables. The availability of savings and time deposits at attractive interest rates prevented the premature appreciation of the exchange rate, thus helping to maintain the competitiveness stimulated by trade liberalization. By reforming interest rates, removing credit ceilings, and increasing competition among banks, Sri Lanka helped increase private sector savings - which could be reallocated to the tradable sector.

have increased private savings in financial institutions, raised economywide financial intermediation ratios, and expanded credit to the private sector.

More important, Athukorala and Rajapatirana find a statistically significant relationship between the financial intermediation ratio and the real exchange rate.

Credit to the private sector had increased after reform of the financial sector, but its reallocation was inhibited by large fiscal deficits, inconsistent monetary policies, and increased intervention in the financial market. Through their negative effect on the real exchange rate, these interventions offset some of the gains in competitiveness achieved through trade liberalization.

Athukorala and Rajapatirana find no evidence of financial crowding out in Sri Lanka.

Unlike earlier studies on financial reform in Sri Lanka, this one finds that financial reforms The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Rcsearch, and Extemal AffairsComplex. An objective of theseries is to getthese findings out quickly, even if presentations arc less than fully polished. Thc findings, nterpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by PRE Editorial Services
Based on a full-income approach that values deaths using the VSL, we estimated the economic burden of TB deaths in Africa between 2006 and 2015 to be $519 billion (95% CI, $475–$563) in a scenario with no DOTS coverage anywhere. Of this figure, $418 billion (95% CI, $386–$450) is attributable to the burden in countries with a high level of HIV co-infection and $101 billion (95% CI, $89–$114) to the burden in countries with a low level of HIV co-infection. The present discounted benefit of moving from no DOTS to the scenario of sustained DOTS at 2005 coverage levels is $129 billion (95 % CI, $113–$146) and to a Global Plan strategy (from no DOTS) is $218 billion (95% CI, $200–$235). In comparison, the costs of implementation are $12.2 billion for a sustained DOTS program and $22.2 billion for the Global Plan strategy. These estimates represent a ten to one ratio of benefits to costs for sustained DOTS coverage and roughly nine to one for Global Plan implementation in the Africa region, relative to the baseline of no DOTS. A similar benefit-cost ratio is estimated for the Global Plan relative to sustained DOTS.

The discounted burden in the countries with the highest burden of TB when a noDOTS strategy is implemented between 2006 and 2015 is estimated to be more than $3 trillion and ranges from $3.3 billion (95% CI, $3.07–$3.58) for Zimbabwe to $1,175 billion (95% CI, $1,074–$1,277) for China. The discounted benefit of maintaining DOTS at the 2005 coverage levels or implementing the Global Plan strategy between 2006 and 2015 for the 22 high-burden countries is more than $1.6 trillion and $1.9 trillion, respectively, relative to a no-DOTS strategy. Benefit-cost ratios for a sustained-DOTS scenario (relative to no DOTS) typically exceed those for moving from sustained DOTS to a Global Plan strategy, except in Afghanistan, Russia, and South Africa.

A full-income approach based on changes in the GDP growth rates related to changes in life expectancy found that implementation of either type of TB-control strategy (sustained DOTS or Global Plan) does not make a significant difference to full-income growth rates ─ differences are on the order of 0.002–0.015 percentage points.

Our estimation of economic benefits depends critically on epidemiological projections of mortality reductions attributable to DOTS and to the value of a statistical life in lowincome countries. Further work is needed to develop more reliable estimates of 24
# **REFERENCES**

Acemoglu, D.; Johnson, S.; Robinson, J.; Thaicharoen, Y. (2003). Institutional causes, macroeconomic symptoms: volatility, crises and growth. Journal of Monetary Economics, Volume 50, Issue 1.

Barrett, C. 2002. “Food security and food assistance programs” in Handbook of Agricultural Economics. Elsevier. 2(2): 2103-2190.

<mark>Cagan, Philip, 1956, “The Monetary Dynamics of Hyperinflation,” in Studies in the Quantity Theory of Money, ed. by Milton Friedman (Chicago: University of Chicago Press), pp. 25–117.</mark> <mark>Cameron, A. C. & Miller, D. L. (2015). A</mark> Practitioner’s Guide to Cluster-Robust Inference **.** Journal of Human Resources <mark>, University of Wisconsin Press, vol. 50(2), pages 317-372.</mark> Coomer, J.; Gstraunthaler, T. (2011). THe Hyperinflation in Zimbabwe. Quarterly Journal of Austrian Economics, Vol. 14 (3).

Dahlhamer J., Dixon J., Doyle P., Eargle J., Griffin D., and McGovern P. (2003). Quality at the Item Level: Terms, Methods, and Guidelines for Cross-Survey Comparisons. Unpublished manuscript, available from the Bureau of Labor Statistics, Washington, DC.

Education Management and Information systems (EMIS). _(2016). Ministry of General Education and Instruction (MoGEI)_ Fajardo, J.; Dantas, M. (2018). Understanding the impact of severe hyperinflation experience on current household investment behavior, Journal of Behavioral and Experimental Finance, Volume 17.

Fischer, Stanley, Ratna Sahay, and Carlos A. Vegh, 2002, “Modern Hyper- and High Inflations,” Journal of Economic Literature, Vol. 40 (September), pp. 837–80.

Garner T., McClelland R. and Passero W. (2009). Strengths and Weaknesses of the Consumer Expenditure Survey from a BLS Perspective. Unpublished manuscript, available from the Bureau of Labor Statistics, Washington, DC.

GoSS, 2011: South Sudan Development Strategy, 2011-13 – Realising freedom, equality, justice, peace and prosperity for all. Ministry of Finance and Economic Planning, Juba, August 2011.

He, L. (2017). Hyperinflation: A world history. Routledge.

Heltberg, R., N. Hossain and A. Reva. 2012. “Living Through Crises. How the Food, Fuel and Financial Shocks Affect the Poor.” World Bank.Washington, DC Jolliffe D., Seff I. and Fuente A. (2016). Food Insecurity and Rising Food Prices: What do we learn from experiential measures? Draft Manuscript.

15
country. For SSA countries it is a critical function as duties and taxes collected in ports are a very important contributor to state revenues<sup>3</sup> . The efficiency of customs clearance in ports is then closely monitored with a focus mostly on revenue collection performance. There is however a growing awareness of the importance of customs clearance time efficiency to facilitate international trade. In theory the time to perform import clearance formalities starts much before containership arrival and is not therefore strictly related to cargo dwell time.

But in fact the bulk of formalities are still performed after ship arrival in most SSA ports despite trade facilitation initiatives. Customs clearance and cargo dwell time are therefore closely related. Customs take (sometimes rightly) a large share of the blame for long delays, but they are not alone. For some operations, customs clearance is efficiently managed by shippers and C&F agents, and transactional dwell time is not a major contributor to total dwell time. For others however the time lost in the clearance process because of missing documents, errors in the declaration or simply lack of anticipation is so important that it is explains an important proportion of long delays. Moreover, customs administrations are just one agent among others who manage official formalities. Some of them are public (sanitary services), others are para-public and have a delegated monopoly on public missions (Terminal Operating company, Port authority, Inspection company). Besides, some official processes may be intertwined, others not. This distinction among public and para-public agents and the analysis of the dependency links between them may be relevant when analyzing the causes of delays and/or proposing new procedures. Nevertheless, in terms of dwell time, customs processes still usually “mark” the beginning and the end of most of the processes. We then define a **transactional dwell time** that would be the administrative counterpart to the operational dwell time defined above and that extends from containership arrival to the issue of the port exit note by customs administration.

# **Supply chain and logistics perspective**

A third perspective on the role of ports, actually complementary to the first two ones, is the supply chain and logistics perspective. Here we are interested by the storage function of container terminals and the linkages with production or distribution strategies of cargo owners. In theory transit storage in the container terminal is not decided by the cargo owner but is rather as explained above a consequence of either the discontinuity of physical transfers or the waiting time before completion of customs clearance formalities. But in reality a significant proportion of containers that

> 3 In Cameroon, Customs duties accounted for 27% of state revenues in 2009 (Source: Cameroon Customs)

4
54 trips/vehicle * vehicles, (1) where Pi is total emissions of pollutant i. By this formulation, emissions of a pollutant i are summed over t vehicle types (i.e., t = auto-gasoline diesel-bus, gasoline-bus, etc.) and k age classes. The variables in (1) each may be different for each t and k. Emissions per mile is itself dependent on the emissions standards, on actual driving conditions, on characteristics of the fuel being used, and on deterioration of and tampering with emissions control equipment and other components of the vehicle.

To convert emissions per mile (by t and k) into total emissions, estimates of vehicle miles traveled (VMTs) are needed. However, the use of VMT measures obscures complex relationships that have policy implications. Hence, this term is divided into four terms, i.e., all the terms on the right-hand side of identity (2) except "emissions per mile." Miles per gallon depends on the various vehicle characteristics and fuel type (which can be affected by fuel efficiency standards, as well as driving and vehicle condition. Gallons per trip is inserted to highlight the effects of congestion on mileage. Trips per vehicle can be disaggregated further into trips per person multiplied by occupancy rates (persons per vehicle) to take into account the occupancy rates of different types of vehicles and the possibility that such rates can be modified by policy.

Uith the emissions per mile term, emissions standards on vehicles inspection and maintenance programs, and targeting high emitters can be modeled. The miles per gallon term permits consideration of a gas guzzler tax, congestion tax, gas tax, and use of fuel efficiency standards. The gallons per trip variable permits consideration of residential and work location decisions (where commuting trips are concerned). The trips per
due probably to privatization. The number of pensioners also increases while the overall number of dependants decreases resulting in a decline of the economically inactive pool. Other categories are very small, including the unemployed on benefits (less than 0.5% of the sample).

**Figure 4.1 Labor market changes**

<!-- Start of picture text --> Wage Vs. Non-Wage labor Agriculture Vs.Non-Agriculture Labor<br><!-- End of picture text --> <!-- Start of picture text --> 100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>Not employed Not employed<br>50% Non Wage labour 50% Non Agriculture labour<br>Wage labour Agriculture labour<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>1998 1999 2000 2001 2002 1998 1999 2000 2001 2002<br><!-- End of picture text -->

**Table 4.4 Structure of the Population by Source of Income**

||**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|---|
|Private agriculture|5|6|10|12|13|16|
|Agric public sector|3|1|1|0|0|0|
|Agric private sector|12|12|12|11|9|7|
|Entrepreneur|1|1|1|1|1|1|
|Non agric. Public sector|15|12|11|9|12|9|
|Non agric. Private sector|6|9|10|11|11|12|
|Professional|0|0|0|0|0|0|
|Unemployment benefits|0|0|0|0|0|0|
|Stipend|1|1|0|0|0|0|
|Pension|21|22|22|23|25|24|
|Social assistance|2|1|1|1|1|1|
|Dependent|34|34|31|30|27|27|
|Other|1|1|1|1|1|2|
|**Total**|**100**|**100**|**100**|**100**|**100**|**100**|

Source: Moldova HBS In table 4.5, we look at the flows of employed across employment categories closing our sample to the households included into the panel over the period 1998-2002. We calculate the transition probabilities for all the possible flows of workers between four different labor market states: Agricultural Employees (AE); Non Agricultural Employees (NAE); Farmers (F) and Self-Employed (SE) taking two subsequent years at the time (Markov transition probabilities). We also indicate the growth and recession years to see whether labor market flows change following output performances.

Flows into AE are all declining over the period while flows into F are all increasing indicating a major reallocation of workers into individual farming. These flows are larger from AE but they also become very large from NAE showing a very significant flow of people from non agricultural activities into individual farming. Flows into NAE are higher on average but remain stable from AE and decline from F and from SE. These flows are also confirmed by data on agricultural 29
The thirty-three equations listed solve for thirty-three endogenous varlables. Consumer prices are determined as a functlon of producer pricos for only two Ilvestock commoditles, beef and pork. For all other livestock commodities, a time serles of producer prices was not available. In these cases, the market clea&ing equation solves for a consumer price. The notation L[.] Indlcates a lagged value of the variable sh:wn inside the brackets.

Model parameters were estimated using multivarlate linear regresslon methods (OLS and 2SLS) using data from the Mexican Ministry of Agriculture and Water Resources (on crop and livestock production, prices, stocks, Imports and exports), the Mexican Central Bank (price indices), Ministry of Programming and Budgeting (natlonal accounts), the Mexican National Institute of Statistics and Geography, and the Foreign Agricultural Service of the US Department of Agriculture.

The economic Interprstatlon of model equations Is straightforward. The crop productlon equatlons are econometric supply functlons which specify crop supply as a function of output and Input prices. The feed demand equations specify food demands as a functlon of crop price guarantees and animal stocks. Human crop demand (for malze) Is specified as the product of population and per capita demand, where the latter Is determined by total per capita consumption, relr e price index for food, and the r etall price of tortillas, which Is a function of the price guarantee for maize. The supply adjustment equations determine the quantities of Imports or exports required to sustain the fixed guarantee. In brief, the equations relating to fle3ld crops embody the government supply adjustment process for market equilibration described above.
# **Deliberative Democracy in India**<sup>**1**</sup>

Ramya Parthasarathy Department of Political Science Stanford University Vijayendra Rao Development Research Group The World Bank JEL Codes: D70, D71, O10, D72 Keywords: Deliberation, Democracy, India

> 1 This paper is forthcoming in the Oxford Handbook of Deliberative Democracy.  We are indebted to André Bächtiger, John Dryzek and David Laitin for valuable comments on a previous draft.
# **Everything You Always Wanted to Know about WTO Accession (But Were Afraid to Ask)**

_Olivier Cattaneo and Carlos A. Primo Braga, The World Bank_

## **JEL:** F13, F15, F40, F59, K33, P33

**Key words:** WTO accession, WTO membership, international trade, WTO-plus, WTO-minus, accession protocol, trade adjustment, trade negotiations, non-market economy status, transition economies, market access, WTO commitments