rafmacalaba/datause-encoder-data huggingface dataset:
& Spulber 1994, p.427).<sup>3</sup> In the context of our empirical application, the relevance of this assumption is questionable. Because details on the mechanisms for adjusting and revising tariffs are typically specified in project contracts, regulatory discretion is in many cases very limited.

# **2.1 The Model**

Consider a setting in which the regulator commits ex ante to set the price _p_ according to a price setting rule of the form p= a+ (1 −b)C, where C is expected cost and the parameter b∈[0,1] represents the power of incentives. When _b_ is close to 1, the rule comes close to a fixed price contract, i.e. a highpowered regulatory rule, while the smaller _b_ , the bigger the fraction of expected cost that is reimbursed by the regulator, as in a cost plus contract.

The firm faces a unit demand function and its cost is affected by a random shock _c_ uniformly distributed over [0, c̅], capturing either cost (input prices) or technology contingencies. The firm must invest an amount _K_ , for which it issues a level of debt _D_ and equity _E._ Hence the firm’s investment constraint is given by: Ex post, _D_ must be repaid using the net payoff p−c. If this falls short of _D_ , the firm suffers a cost of financial distress _T_ , assumed fixed for simplicity. As in Cambini & Spiegel (2011) we assume that external investors (debt holders and new shareholders) are eventually paid in full, and so existing shareholders are responsible for bankruptcy costs _T_ in the case of financial distress. Denote by φ(p, D) c̅ the probability that the firm finds itself in such a situation, so that its expected cost is C= + φ(p, D)T. 2 Accordingly, this implies that: The timing is as follows. An exogenous price setting rule of the form p= a+ (1 −b)C is announced at stage 0. In stage 1, the regulated firm invests _K_ and chooses its capital structure by issuing an amount of debt _D_ and equity _E_ . At stage 2, given the pre-announced price rule, the firm’s cost is revealed, and output and payoffs are realized.

As in Spiegel and Spulber (1994) and Cambini and Spiegel (2011) we assume that the firm’s management acts so as to maximize the payoff of existing shareholders. Hence, the management’s objective is to choose the mix of debt _D_ and equity _E_ so as to maximize:

> 3 Cambini and Spiegel (2011) provide examples from US electricity and UK water regulation to support this view.

5
timing of reforms and characterize the nature of post-reform market structure. More particularly, we differentiate between monopolies on the one hand and three types of liberalized markets on the other hand. Monopolistic markets characterized all SSA cotton markets pre-reform in the period we examine. In some countries where no reform has been adopted yet, markets remain monopolistic. In such markets, parastatals or marketing boards have a monopsony on the purchases of raw cotton from small farmers and a monopoly on selling cotton on the international market. Where reforms have been adopted, markets evolved into three broad categories: regulated sectors (with several firms active in the sector but no direct competition), moderately competitive sectors (where a small number of firms exert price leadership) and strongly competitive markets. Second, we construct precise indices of weather conditions at the level of cotton cultivation zones based on the dataset provided by the Climatic Research Unit of the University of East Anglia (2011).

The cotton sector is the focus of this paper because of its particularly interesting institutional history. A large number of countries in SSA have had very similar cotton market structures for decades (a legacy of colonial policies – see the Annex of this dissertation) but have chosen reform options that differ in several dimensions. This situation thus offers a privileged testing set-up for examining variations in post-reform performance and identifying the reasons for such divergence. Besides, the policy implications of our results should be of widespread interest in SSA: cotton remains at the core of vivid policy debates as it is the main source of cash revenue for more than two million poor rural households and a major source of foreign exchange for about fifteen countries on the continent (Tschirley _et al._ , 2009).

Our findings confirm the intuition that the relation between market structure and performance is complex but that it is meaningful and significant once a number of elements are controlled for. The impact of the changes in market structure brought about by reforms notably appears to have been very different in Francophone WCA and in the rest of SSA. _Ceteris paribus_ , productivity was higher in all types of liberalized markets in ESA and non-Francophone WCA. However, highly competitive markets seem to have produced less than monopolistic sectors. We attribute these results (i) to the fact that competitive markets sectors tend to offer access to inputs more restrictively (hence causing the exit of the less experienced farmers and the less productive land) and (ii) to the fact that the positive effect of competition on producer prices has, on average, been relatively limited (in part because of subsidizing in a number of regulated and monopolistic sectors).

4
# **3.1 Historical Background of the Conflict**

The conflict in BiH took place against the backdrop of the disintegration of Yugoslavia. After President Tito died in 1980, power began to be held by an unstable collective rotating presidency shared among the leaders of six republics (Serbia, Montenegro, Bosnia and Herzegovina, Croatia, Slovenia, and Macedonia) and two autonomous regions (Kosovo and Vojvodina). Multi-party elections were held in BiH in November 1990. Disagreements on the reform of the Yugoslav federation eventually induced Slovenia and Croatia to seek independence in 1991. In a 1992 referendum, the people of BiH voted overwhelmingly for independence from Yugoslavia. Bosnia declared independence on March 5, 1992, and was recognized by the United States and the European Community in April.

Following the declaration of independence, all major Bosnian cities were blockaded by Bosnian Serbs, and the Serb-controlled Jugoslav National Army (JNA) established control over 70 percent of the country. Areas of eastern and northwestern Bosnia saw fierce fighting during 1992 and 1993 between all three major groups in the country: Bosniaks (mostly Muslim), Serbs (mostly Orthodox Christian) and Croats (mostly Roman Catholic). Descriptive accounts suggest that the main reasons to engage in conflict were strategic or ethnically motivated: both Serb and Croat individuals were convicted of planning ethnic cleansing in an attempt to create ethnically homogeneous States (Burg and Shoup, 1999). This motivation would suggest that the fighting was most intense in the most diverse areas; indeed, we find that the areas with greater pre-war ethnic diversity had higher conflict intensities (results available upon request).

In February 1994, the Bosniaks and the Croats signed a cease-fire agreement. In March 1994, US mediation produced an agreement between the Bosnian government and the Republic of Croatia to establish a federation consisting of all the territory controlled by the Bosniaks and the Croats, resulting in the creation of the Federation of Bosnia and Herzegovina (FBiH). The Srebenica massacre of July 1995 (where about 8000 Bosniaks were killed by Serb forces) led NATO to conduct a month-long bombing campaign against the Serbs. The war finally ended with the signing of the Dayton Peace Agreement on November 21, 1995. This agreement partioned BiH into the Federation of Bosnia and Herzegovina (FBiH), led by Bosniaks and Croats, and the Republika Srpska (RS), which was Serb-dominated. In 1996, the UNHCR estimated FBiH to have a population of 2.44 million and RS to have a population of 1.48 million. In our nationally representative survey, approximately 55 percent of the households are from FBiH.

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# **Table 4: Central bank prices for payment services** (United States, 1996)

|_Payment service_|_Price rang_<br>_(reflects averag_|_e per item_<br>_e variable cost)_|_Price per batch of payments_<br>_(reflects average fixed cost)_|
|---|---|---|---|
||(In U|S$)|(In US$)|
|**Check**||||
|Unsorted checks|.003 -|.080|1.50 - 9.00|
|Presorted checks|.003 -|.012|2.50 - 11.00|
|Returned checks|.100 -|1.110|1.50 - 8.00|
|Payor bank services:||||
|MICRinformation|.001-|.005|5.00- 30.00 minimum|
|Electronic presentment|.001 -|.0045|3.00- 14.00 minimum|
|Check truncation|.010-|.017|3.00-25.00<br>minimum|
|**ACH**|**_Origination_**|**Receipt**||
|Unsorted deposit|.01|.01|1.75 (per input file submitted)|
|Presorte**d**eposit|.009|.01||
|<br>Addenda record|.003|.003|25.00 (monthly account servicing fee)|
|Returned payment|.04|.04||
|**Funds transfer**||||
|Wire transfer|.5|.5||
|Net settlement|1.00|||
|Off-line surcharge|10.00|||
|<br>Telephone advice||10.00||
|**Book-entry securities transfer**||||
|On-line transfer|2.25||15.00 (monthly account servicing fee)|
|Off-line transfer|10.00|10.00||
|**Electronic connectionfees**||||
|Telephone dial up|||30.00 -450.00 per month|
|<br>Dedicated leased line|||<br>750.00 - 2,000.00 per month|
|Encryption certification|||0 - 8,000.00|

_Source: Board of Governors of the Federal Reserve System (1997), various tables. Two additional priced services are: the collection of definitive securities (bond coupon collection, etc.) and special cash services (provision of wrapped coin, special packaging of currency, and cash deposits/withdrawals above a standard number per month)._ In order for revenues to cover all costs, the central bank needs to forecast the growth in the demand for its various services and set prices accordingly. First, cost projections (including imputed expenses) are made for total variable cost (TVC) and total fixed cost (TFC) for the coming year. Second, the projected TVC for each product or service is divided by an estimate of the volume of payment items expected to be processed and cleared, giving an estimate of what the per item fee must be to have revenues cover all variable costs. The volume changes experienced for each payment service, which differ somewhat from projections made earlier, are shown in Table 5. Third, the projected TFC is divided by an estimate of the number of batches of payments expected
# **The Internet and Chinese Exports in the Pre-Alibaba Era***

## **Ana M. Fernandes**<sup>**a**</sup> **Aaditya Mattoo**<sup>**b**</sup> **Huy Nguyen**<sup>**c**</sup> **Marc Schiffbauer**<sup>**d**</sup>

## **JEL Classification codes** : F14, O33.

**Keywords:** internet, information and communication technology, export growth, firm-level data, China.

> a Ana Margarida Fernandes. The World Bank. Email: afernandes@worldbank.org.

> b Aaditya Mattoo. The World Bank. Email: amattoo@worldbank.org.

> c Huy Nguyen. International Monetary Fund Email: hnguyen4@imf.org.

d Marc Schiffbauer. The World Bank. Email: mschiffbauer@worldbank.org.

* This paper was prepared as a background paper for the World Development Report 2016 _Digital Dividends_ . The authors would like to thank Michael Minges, Rajendra Singh, Tim Kelly, Indhira Santos, Uwe Deichmann, Shawn Tan, Deepak Mishra, and Maggie Chen for helpful discussions, to Michael Ward for sharing data, and to Changjiang Li at CNNIC for help with access to data on ICT in China. Research for this paper has in part been supported by the World Bank’s Multidonor Trust Fund for Trade and Development and the Strategic Research Program on Economic Development. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank or its member countries.
appeal to the common timing of the IHDS data with the NSS data for 2004/05 and 2011/12, and the representative sampling design of both data sources, to suggest that the method is also likely to work well for mobility comparisons based on the NSS rounds.

Our validation exercise works with the consumption measure that is available in the IHDS surveys. As noted above, most distributional analysis conducted with the IHDS surveys has been based on a per-capita income measure of well-being. The IHDS surveys are the only nationally representative surveys that contain such income information. The IHDS consumption data, in contrast, are highly abbreviated in comparison with consumption data from the NSS surveys, and are rarely used for distributional analysis as a result. However, for our purposes it is appealing to validate our synthetic panel methods using the IHDS consumption variable. This is because implementation of the procedure will then resemble more closely its subsequent application with the NSS data.

Since the IHDS surveys are panel surveys, we split the IHDS panels into two randomly drawn subsamples (each representing half of the total sample). Call these subsamples A and B. Then we can use subsample A in the first round and subsample B in the second round as two repeated cross-sections to which we apply our method. We can then compare the mobility results obtained from using subsample A to impute round 1 values for subsample B against the results we would get by using the genuine panel for subsample B. We use panels with the same household heads only for the genuine panels. As is common in pseudo-panel-based analysis, we restrict our attention to households whose head (in 2004/05) is in the 25–55-year age range.

It is useful to briefly note the estimation of ρ before discussing estimation results. As in Dang and Lanjouw (2018), we form cohorts by interacting household heads’ age with a dummy variable indicating whether they belong to scheduled castes. The partial correlation coefficient ρ is estimated to be 0.39 and strongly statistically different from zero. This value is not 17
and Gambia, the main rationale for state intervention in the groundnut sector has been to safeguard the viability of state-owned processing mills. Consequently, the share of the export price to groundnut farmers has consistently been less than 60 percent in these two countries. This policy has been counter-productive for the state-owned enterprises, since farmers have bypassed large public processing companies, creating excess capacities and financial difficulties.

With regard to trade policies, there are wide differences among African traditional groundnut exporters. Senegal and Malawi apply high tariffs on processed groundnuts, to encourage in-country processing (oil production in the case of Senegal) (Table 14). In contrast, Gambia and Nigeria have a liberal trade policy, with no export taxes or import tariffs. South Africa’s tariff structure exhibits a slight escalation, with processed groundnuts subject to a tariff of 6 percent while unprocessed groundnuts enter duty free. In Senegal and Gambia however, unofficial cross-border trade is significant, with farmers frequently crossing the border to and from Senegal, depending mainly on respective producer prices and domestic supply levels. Oil imports face a 20 percent tariff in Senegal, South Africa and Malawi.

**Table 14. Tariffs on groundnut products in major African producers (%)**

|**Country**|**Product**|**1999/2002**|
|---|---|---|
||Raw edible groundnut|5|
|Senegal|Processed edible groundnut<br>Groundnut meal<br>groundnut oil(refined)|20<br>0<br>20|
||Edible groundnuts|0|
|Nigeria|Groundnut meal<br>groundnut oil(refined)|0<br>0|
||Edible groundnuts|0|
|The Gambia|Groundnut meal<br>groundnut oil(refined)|0<br>0|
||Raw edible groundnut|0|
|South Africa|Processed edible groundnut|6|
||Groundnut meal|0|
||groundnut oil(refined)|20|
||Raw edible groundnut|5|
|Malawi|Processed edible groundnut|25|
||Groundnut meal<br>groundnut oil (refined)|0<br>20|

Source: WTO Database. Note: Raw edib. gr. =raw, not roasted or cooked, in shell or shelled gr. Processed groundnuts = Bleached, preserved or otherwise prepared gr., incl. Roasted, salted. gr. butter The EU has harmonized country regulations of maximum permissible level of aflatoxin in 1998, and has fixed maximum levels of aflatoxin to the lowest possible level (e.g., 0.002milligram for B1 type aflatoxin for edible groundnuts). Technical processes exist to reduce aflatoxin contamination (e.g., through ammoniac as used in Senegal for groundnut meal) but the best method is to improve farm practices through use of the best quality and resistant seeds, proper management of farms, and appropriate storage to avoid exposure to high temperature and humidity. African countries also face difficulties in meeting stricter product and quality standards. In Senegal and Gambia, groundnut varieties were originally selected for oil production, which can accommodate lower quality seeds and raw groundnuts. A seed variety in Malawi proved successful in better yields, but lacked commercially viable characteristics. These problems transpire in the international prices of African origin, which are discounted compared 20
challenge. However, cost is a barrier because staple crop farmers often face poor relative returns on fertilizer, often with a “value to cost ratio” of 1 or less (e.g., Wortmann and Kaizzi 1998; de Jager et al. 2003; Kaizzi 2002; Matsumoto and Yamano 2009).

Uganda’s internal geographic heterogeneity underscores the disparate range of farming systems across Africa. Ruecker and colleagues (2003) identified seven categories of agricultural potential across the country based on permutations of four climate variables. First, annual precipitation cycles affect the extent of water availability throughout the year. The northeastern section of the country has unimodal rainfall, while the southern and central areas, which are closer to the equator, have bimodal rainfall. Second, the length of growing period is measured as the period over which mean monthly rainfall exceeds half the mean potential evapotranspiration. This ranges from less than 5 months in the northeastern districts to 10 or more months in the central region and in the southwestern highlands. Third, the actual level of annual precipitation varies tremendously throughout the country and changes at a steep gradient, particularly in the “crescent” around Lake Victoria. Fourth, extreme temperatures constrain agricultural productivity. The range of growing conditions results in significant variations in the concentration of staple crop by region.

# **iii. Other Key Sectors**

Cash crops, especially coffee, have historically been a major driver of Uganda’s growth and poverty reduction. However, fewer than 10 percent of the country’s farm households grow coffee, and the commodity’s share of exports has declined significantly (Kappel et al. 2005; Bussolo et al. 2006). As of the mid-2000s, cotton, tea and tobacco had increased in volume and total value exported, while flower exports had also been introduced. Fisheries have overtaken coffee in overall export value, but only a small share of the population is engaged in that activity. In any case, all the export shares need to be considered in the context of Uganda’s low overall export/GDP ratio, which ranged between 15-24 percent from 2006-2015 (World Bank 2017).

Manufacturing remains a small element of the economy, historically accounting for less than 10 percent of GDP, as indicated in Table A1. Table A2 shows that the sector still employed less than 4 percent of the labor force as of 2002. The service sectors—including wholesale and retail 60
intervention, and in the types of firms receiving them. For example, the Zambia matching grant program gave out 63 grants, of which 23 were for less than $5000, 33 between $5,000 and $50,000, and 7 above $50,000; 39 percent of the firms were in manufacturing, 30 percent in services, 14 percent in agriculture, 6 percent in tourism, and the rest in other sectors.<sup>4</sup> In other programs the range of the grants offered can be smaller, but there is usually still large heterogeneity in the sectors. For example, the matching grants under the South Africa Black Business Supplier development project were capped at US$17,000, and restricted to firms with fewer than 20 employees. Even given these restrictions which make the firms more homogenous, a survey of 50 participating enterprises showed full-time employment with a mean of 9.9, standard deviation of 8.0, and 2004 revenues with a mean of 983,000 rand and standard deviation of 2,726,000 rand: that is, like the enterprise survey data, the standard deviation of sales is several times its mean. The Kenya project listed in Table 3 is notable as an exception, and an illustration that projects focused on microenterprises may serve many more firms than those focusing on SMEs.

Table 4 summarizes a half-dozen projects on SMEs being undertaken by the International Finance Corporation (IFC) in Africa. These projects are typically directed at firms in a particular sector, such as firms in, or linked to, the mining industry. As a result, the firms in these projects appear to be typically smaller in number, but more homogeneous, than those in the World Bank projects. In addition, the IFC offers business training through its _Business Edge_ program<sup>5</sup> in a number of African countries. Table 4 shows that numbers of firms participating in these programs in the last year are small in most countries (e.g. 16 in South Africa, 19 in Senegal), but in a couple of countries get up to a size of around 300-400 (Nigeria and Kenya).

Numbers are also often small for other organizations working to generate firm growth in African countries. Technoserve is one of the better known NGOs, which runs business plan competitions in different countries. In 2006 and 2007 they trained 120 people in Ghana, with 11 businesses launched; in Kenya in 2007 and 2008 they had 38 businesses launched, and in Swaziland between 2006 and 2009 they had 87 businesses launched.

> 4 Source: World Bank Implementation Completion Report.

> 5 <u>http://www.ifc.org/ifcext/africa.nsf/Content/SMED_BusinessEdge. In many of these training sessions there are</u> several individuals per firm being trained, so the total number of people trained exceeds the number of firms trained. But for the purpose of assessing firm outcomes, the number of firms trained is the more relevant metric.

6