rafmacalaba/datause-encoder-data huggingface dataset:
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 10. Performance of the electricity sector in select African countries**

|Country|**Botswana**<br>**(2006)**|Lesotho<br>(2009)|Madagascar<br>(2009)|Malawi (2006)|Mauritius<br>(2009)|Mozambique<br>(2007)|Namibia<br>(2006)|South Africa<br>(2007)|Zambia (2007)|Average|
|---|---|---|---|---|---|---|---|---|---|---|
|Number of power outages in a typical month|**1.7**|7.2|13.7|6.4|3.6|3.1|1.7|2.2|4.2|4.9|
|Average duration outages (hours)|**2.5**|5.5|2.3|2.3|3.2|4.3|2.7|4.5|2.9|3.4|
|Lost due to outages (% of sales)|**1.4**|6.7|7.7|22.6|2.2|2.4|0.7|1.6|3.7|5.4|
|Percentage of firms owning or sharing<br>generator|**16**|31|29|49|24|13|13|18|14|23|
|Percentage of electricity from generator|**18**|—|19|3|3|11|6|11|19|11.3|
|Delay in obtaining an electrical connection<br>(days)|**25**|14|92|98|19|13|9|16|97|42.6|
|Percentage of firms identifying electricity as<br>a major constraint|**7**|44|55|60|43|25|6|21|12|30.3|

Source: Enterprise Survey Database (www.enterprisesurveys.org). _Note:_ Year of the survey is in brackets.

In this context, the BPC was able to attain high efficiency and profitability while providing low-cost service. Collection-to-billings ratio has been nearly one to one. System losses have been also quite good—barely above the 10 percent efficient power utility benchmark. Tariffs are somewhat below cost recovery levels with the gap having widened slightly in recent years (table 11).

The BPC is one of the most efficient utilities in southern Africa, as measured by the hidden costs of its inefficiencies in past years (box 2). These inefficiencies represent about 14 percent of the BPC’s revenues, just around one-seventh of those of Zambia (figure 12a).

**Table 11. Evolution of hidden costs associated with the Botswana Power Corporation (BPC)**

||Volume of<br>electricity<br>produced /<br>purchased|System<br>losses|Collection<br>ratio|Average total<br>cost|Average<br>effective tariff|Total hidden<br>costs|Total hidden<br>costs|
|---|---|---|---|---|---|---|---|
||(GWh/year)|(%)|(%)|($/kWh)|($/kWh)|($ million/year)|(% revenues)|
|2004|2,642|10.4|100|0.049|0.052|517,923|0.4|
|2005|2,731|11.5|100|0.048|0.051|1,923,289|1.5|
|2006|2,917|10.0|98|0.050|0.048|7,889,571|6.4|
|2007|3,120|11.0|100|0.053|0.048|16,530,222|12.3|
|2008|3,215|10.2|100|0.055|0.048|20,661,247|14.6|

_Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ kWh = kilowatt-hour; MW = megawatts.

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tend to differ in terms of the relevance of these factors to their activities/services.

BOX C Sequencing ID Through a Series of Projects Three Agricultural Prolects (PaRua New Guinea) The First Agricultural Credit Project initiated a lending program to smallholders for development of pork, beef and poultry enterprises through the Agricultural Bank (ABPNG). The appraisal zeport for the project focused on the need for the project executing agency to strengthen its technical and financial management with technical assistance. The Second Agricultural Project addressed this issue more fully. A program was launched to reduce administrative costs, to streamline aranch operations and to make them field oriented. A reorganization of the branch network, as well as a staff and retrenchment program followed. Improved benefits and remuneration competitive with the private sector were then introduced. As a result the quality of ABPNG management improved substantially, and staff turnover declined from 27 percent in 1981 to 12 percent in 1984. In addition, a localization program was initiated and proceeded at a satisfactory pace without sacrificing the efficiency of the bank and its lending and investment operations. From 54 expatriates officers in 1974, the number declined to 35 in 1980 and 21 in 1985.

In order to alleviate the problems encountered during the implementation of the second project and to enhance institutional development, under the Third Agricultural Project, ABPNG undertook to implement an action program and a new financial strategy. A condition of effectiveness was the appointment of a suitably qualified individual as deputy managing director. In addition, a program was launched to monitor and evaluate the impact of subloans on farmers' income, expenditure patterns, and attitudes about which previous projects lacked information.

# Emerging Issues

OED reviews which use data pertaining to projects completed prior to the mid-1980s also shed useful light on the trends over time in the achievement of Bank ID objectives.25 The data is organized by evaluation year 25 Data do not permit a disaggregation o. ID objectives and of the extent of achievement of each.
We turn next to regression analysis. In Table 10, we present logit regressions of determinants of early payment discounts for the subsample of contracts that offer early payment discounts. The dependent variable takes value 1 if the contract includes a discount (two-part contract), and 0 otherwise. We focus on the regressions for buyers who have at least one discount (columns 2, 3, and 5), though for completeness, we also present regression results for the complete sample of buyers (in columns 1 and 4).

Let us focus first on the regression estimates with supplier fixed effects in column 5. Large buyers get significantly fewer contracts with discounts, and investment grade buyers are also less likely to get discounts for early payment. Discounts also tend to be industry-specific, with discounts being more common for buyers in the groceries sector, where goods tend to be perishable. These findings are consistent with the theory that trade credit is a way to guaranty product quality. Turning next to the estimates with buyer fixed effects in column 3, we find that large and medium-sized suppliers give significantly fewer discounts than small suppliers.

That early payment discounts are more common from small suppliers is consistent with the market power hypothesis, except that these discounts go more to small and non-investment grade buyers **.** The finding is more consistent with the risk management view that stipulates that smaller suppliers are more likely to offer discounts to risky buyers as a way to encourage early payment and prevent default because it is more difficult for these firms to absorb and diversify default risk.<sup>15</sup>

# **5. Concluding Remarks**

The bilateral, multi-contract nature of our dataset is truly unique and is a major improvement on (generally survey based) datasets that have previously been used to study the determinants of credit terms used in trade credit. This multi-contract structure of our dataset allows us to abstract from unobserved buyer and supplier firm characteristics, something previous empirical work has not been able to do.

> 15 We could also examine buyer-seller pairs as in Table 9, but the smaller number of observations on discounts renders much of the analysis statistically inconclusive.

20
provide a facility for multiple random number streams—for example, so that a number of independent simulations may run in parallel—the same effect can be achieved by advancing the entire random number generator far enough down the sequence that multiple simulations will not re-use the same numbers (L’Ecuyer 2001, Law and McComas 1994). While the syntax “ `set seed 1` ” does not do this, Stata does provide the ability to set what it calls the “ `c(seed)` ,” representing the entire state of all four recursions used in the KISS random number generator. The manual discusses how to capture and restore this state, but not how to quickly advance the state by a large number of draws, except by actually making those draws (StataCorp 2009). This could be carried out for streams that are as far apart as desired, up to the period of the KISS random number generator, though the computational time required to find the relevant `c(seed)` by discarding the intervening draws increases linearly, and thus impractically, for very large stream separations.<sup>4</sup>

# **4 Testing the methods**

The pattern that emerges from frequent re-initialization during use of Stata’s `uniform()` command is easily detected via the statistical tests included in the TestU01 suite. Stata is not alone in having a seed re-initialization facility that does not hold up to tests of randomness. To illustrate, Table 1 shows several RNGs’ performance on collection of very weak tests of randomness from the TestU01 suite. The tests are weak because they only use very small samples from the random stream, but I choose these tests because the failure of an RNG on these tests illustrates the severity of the problem.

The first twelve columns show RNGs provided by Stata, while the last six columns show RNGs from MATLAB. For Stata, three underlying generators are tested: the `uniform()` RNG, in Columns (1) – (4); the original `rnormal()` RNG, in Columns (5) – (8); and the

> 4In Stata 11.2, typing `set seed 0` yields a c(seed) of `X1b5b8174c43f462544a474abacbdd93d00023a41` ; advancing 10<sup>12</sup> draws, which advances the c(seed) to `Xd2c55174405eeffae35b1912f1a5cbf6000206cb` , took roughly 7 hours on a computer using a 2.3 GHz Intel Core i5-2410M processor. Other versions of Stata have the same underlying RNG with the same underlying state, but use different checksums in the last bits of the c(seed). Thus, in Stata 10.1, setting the seed to zero yields the c(seed) of <u>`X1b5b8174c43f462544a474abacbdd93d4b02`</u> , while in Stata 12.1, it yields the c(seed) of <u>`X1b5b8174c43f462544a474abacbdd93d00043a43`</u> . In all three versions, the 32 characters which represent the RNG state (after the `X` , underlined) are identical.

7
POLICY RESEARCH WORKING PAPER 3011

# Abstract

Guasch, Laffont, and Straub construct a regulation model **in** which renegotiation occurs due to the imperfect enforcement of concession contracts. This enables the authors to provide theoretical predictions for the impact on the probability of renegotiation of a concession, regulatory institutions, institutional features, economic shocks, and the characteristics of the concession contracts. Then they use a data set of nearly 1,000 concessions awarded in Latin America and the Caribbean countries from 1989 to 2000 covering the sectors of telecommunications, energy, transport, and water to test these predictions. Finally, the authors derive some policy implications of their theoretical and empirical work.

This paper-a product of the Finance, Private Sector, and Infrastructure Unit, Latin America and the Caribbean Regionis part of a larger effort in the region to foster private sector participation **in** infrastructure. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Joy Troncoso, room 15-108, telephone 202-473-7826, fax 202-522-2106, email address jtroncoso@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. Luis Guasch may be contacted at jguasch@worldbank.org. April 2003. (41 pages)

**_The Policy Researcb Workmg Paper Seroes dusseminates_** _the_ **_Reigs_** ea _of ork_ **_in progress to encourage the exchafge_** _of_ **_ideas about development issties. An objective of the series ts to get the findings ouit qutitkly, even if the presentationis are less than fully polished The papers carry the names_** _of_ **_the auithors antd shotild be cited accorditngly. The finditngs, interpretations, and concluisons expressed in this paper are entirely those_** _of_ **_the authors They do not necessarily represeyzt the view_** _of_ **_the World Bank, its Executive Directors, or the couintries they represent._**

Produced by the Research Advisory Staff
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Given the notorious lack of the precise elasticity<sup>estimates needed to calculate optimal incentivcs, the</sup> illustrative calculations here do not support<sup>a departure from the rule of thumb advocated by Balassa.</sup>

# **Table 4. Optimal Expor. Subsidies for a**<sup>**Given Ten-Percent**</sup> **Import Tariff on All Tradeables**

(cost-disadvantage ratio **of 10** percent)

||**_CTRlS_**|**_Cmttwabk_**|**_Cmn_**<br>**_(do_**<br>**_c_**|**_rpflt)6_**|
|---|---|---|---|---|
|Equivalent**variation**<br>|°|66(5 g)b<br>128|55<br>102|(-6)b|
|**ScaleeMckttcy(total)**<br>||<br>54|32||
|Producer goods<br>||<br>4|52||
|Consumer goods<br>||9<br>|||
|Heavy industry||24|18||
|Firm**entry**<br>|||4||
|Producer goods<br>|||-<br>||
|Consumer goods<br>|||-12<br>11||
|Heavy industry|||-||
|**Export**subsidy<br>|||62||
|Primary products<br>|10<br>|-6<br>I|36||
|Food processing<br>|10<br>|-<br>|33||
|Producer goods<br>|10<br>|24<br>2|25||
|Consumer goods<br>|10<br>|7<br>|41||
|Heavy industry<br>Traded services|10<br>10|25<br>-1|41||

Note: The subsidy is in percentage points.<sup>Other figures are in basis points.</sup> a. Pricing according to equations 11, with firm entry<sup>determined by profits on domestic sales.</sup> so that.7, = 0.

b.Corresponding equivalent variation figure under<sup>neutrality, that is.fromcombining a 10-</sup> percent import tariffwith a 10-percent export<sup>subsidy in sectors with increasing returns to</sup> scale.

In the case of Cournot competition. however,<sup>the optimal pattern of export subsidies dcparts further</sup> from neutrality. Under thisscenario, an optimal<sup>policywould encourage firm exit to reapscale economies.</sup> As the figures in the last column of table4 indicate,<sup>firm exit would be achieved by providinghigherexport</sup> subsidies to sectors with constant returns to<sup>scale.10Now departure from a simple rule of thumb yields</sup> larger welfare benefits. However, the discussion<sup>of table 2 suggested that the results under Cournot</sup> competition areverysensitiveto thedeterminantsof<sup>firmentry, so these resultsshould be interpretedwith</sup> care.

# Conclusion

**This** paper set out to test the robustness<sup>of Balassa's recommendation of neutral incentives to</sup> domestic and export sales in a setting<sup>where some sectors have domestic market power. Wc have shown</sup> analytically that the welfare effects of trade<sup>policy are more complex than they are in a setting of across-</sup> the-board constant returns to scale.<sup>In particular, we have shown, analytically and numerically, that the</sup> standard distortionary costs of protection<sup>emphasized under conditions of constant returns to scale must</sup> be amended to accommodate, among other<sup>things, the welfare effects of changes in scale efficicncy.</sup>
In this context, it is critical for entrepreneurs as they need to show endurance when confronted with high risk and resource‐constrained conditions (Hmieleski and Carr 2008; Markman, Baron, and Balkin 2005). A longitudinal study conducted with small entrepreneurs in Spain showed resilience to be an important predictor of entrepreneurial success, with resourcefulness being one of the driving factors (Ayala and Manzano, 2014).

Resilient individuals tend to be more adaptable to changes in the workplace, and more emotionally stable in dealing with adversity. In addition, they are able to thrive and learn in the face of adversity (Frederickson 2004; Masten 2001). However, resilience is not a fixed attribute; rather, it can be both a trait and a process (Cicchetti 2010; Jacelon 1997;). Rutter (2012) posits that exposure to stress or adversity can alter resilience toward future stressors in either direction. Specifically, it can lower one’s vulnerability through a steeling or strengthening effect — or increase it through a sensitization effect.

**Figure 2: Framework for Psychosocial Curricula Design: Psychological Capital or “HERO”**

<!-- Start of picture text --> Hope<br>Psychological (self)<br>Optimism Capital<br>" " Efficacy<br>HERO<br>Resilience<br><!-- End of picture text --> _Source:_ Authors’ schematic based on self‐efficacy (Bandura 1997), optimism (Carver and Sheier 2003), hope (Snyder, Cheavens, and Sympson 1997), and resiliency (Masten 2001).

To conclude the discussion of psychological capital and its constituents, research from positive psychology and organizational behavior shows that when the four “HERO" psychological resources — hope, self‐efficacy, resilience, optimism — are combined, they assume a higher order function. Specifically, they are a “stronger predictor of attitudes and performance” than any one of the four components by itself (Luthans and others 2007). In addition, building this 21
has also been hit hard by a rapid increase in the amount of credit losses and an extended uncertainty on the credit environment and duration of the crisis.

# **4. Measuring the market response to financial sector distress policy initiatives**

What is the effectiveness of policy measures taken during the height of the crisis? To answer this question, in this section we evaluate the market response to different policy initiatives addressing the financial sector distress.

# **4.1. Policy interventions**

We use data on the dates and types of major policy initiatives to support the financial sector and address the impact of the COVID-19 emergency. The data were compiled and made publicly available by the World Bank (World Bank, 2020). It covers policy measures in low, middle, and high-income countries. Information on each policy measure was collected from national authorities and international organizations. For each policy initiative, the dataset reports details on the announcement, and the day (and not the hour or minute) of the announcement which restricts our analysis to daily frequency. We classify approved measures that target the banking sector into the following categories: (i) liquidity support, (ii) prudential measures, (iii) borrower assistance, and (iv) monetary policy (Table 3).

_Liquidity support_ deals with potential shortages in funding, either because precautionary demand for liquidity has increased or because of lack of access to funding (e.g., wholesale funding markets have dried up). Measures included are provisions of domestic currency liquidity through broadened access to central bank refinancing, extended collateral framework, such as those in the short-term and long-term repo market, and available foreign currency liquidity through swap agreements between central banks.

15
_Africa’s Macroeconomic Story_ The magnitude of growth surges varies considerably across countries (Figure 10). At one end of the spectrum, trend growth increased by more than five percentage points in seven countries, five of which are resource abundant (the DRC, Sierra Leone, Liberia, Angola and Zambia), At the other end, real GDP per capita decreased in eight countries. However, in seven of the eight countries growth was already above three percent in 1994 (Mali being the exception).

**Figure 10: Change in Trend Real GDP Growth per Capita, 1994-2012**

_Percent_ <!-- Start of picture text --> 12<br>6<br>0<br>-6<br>-12<br>Liberia Congo, Dem. Rep. Sierra Leone Angola Ethiopia Zambia Burundi Congo, Rep. Cameroon Ghana Rwanda Niger Tanzania Nigeria Chad Cent. Afr. Rep. Cote d'Ivoire Kenya Gambia, The Zimbabwe Mauritania South Africa Gabon Mozambique Namibia Guinea Togo Madagascar Lesotho Comoros Senegal Malawi Burkina Faso Seychelles Sudan Cape Verde Guinea-Bissau Benin Botswana Uganda Mali Mauritius Swaziland South Sudan Eritrea Equat. Guinea<br><!-- End of picture text --> _Source:_ World Bank _Global Economic Prospects_ January 2013.

# _Output has become less volatile_

The growth acceleration in the mid-1990s coincides with a decline in output volatility. From the cross-regional perspective, the standard deviation of growth rates in Sub-Saharan Africa (SSA) has declined substantially but remains higher than in other developing regions, with the exception of East Asia and Pacific (EAP) (Figure 11, left panel). By contrast, the amount of variation in the output gap (the deviation in output from its trend level) in Sub-Saharan Africa is lower than in most other regions (Figure 11, right panel). From the cross-country perspective, the decline in volatility has been pervasive across most of the region. The standard deviation of growth rates has declined in all but 8 17
inefficiency, including the overuse of polluting technology, and provided few incentives to conserve. Reversal of this policy through privatization with strictly enforced standards for environmental protection should prove beneficial to the environment.

Redistribution of income and wealth has an effect on the environment. By giving ownership to farmers, land reform helps protect the environment because it enables the cost of land use to be fully borne by decisionmakers and eliminates the externality that leads to overexploitation. Because the laws in Pakistan were very lax and easy to circumvent, past land reforms did not bring about the desired change in land tenure and did not have any effect on environmental protection.

|**Table 1. Past**|**and Present Economywide Pol**|**i cies and Their Effects on the En**|**vironment in Pakistan**|
|---|---|---|---|
|Policy area|Policy instruments|Outcome||
|||Economic|Environmental a/|
|Agricultural<br>policies|Support prices<br>Input subsidies<br>Public expenditures on agricultural<br>infrastructure|Increased cultivable area<br>Increased crop yields<br>Increased irrigation water use<br>Increased fertilizer and chemical use|Change in soil quality<br>Change in agricultural<br>rn-off<br>Change in rate of<br>deforestation|
|Industrial<br>policies|Controls on industrial imports and<br>exports of raw materials|hicreased industrial output<br>Increased energy use|Increased industrial<br>emission<br>Increased industrial waste<br>water pollution|
|Trade and<br>exchange rate<br>policies|Removal of quantitative restrictions<br>Lowering of tariffs<br>Exchange rate devaluation|Higher retums to efficient sectors<br>(cotton, for example)<br>Changes in agricultural output pattern<br>Change in energy cost|Change in use pattems of<br>agricultural inputs<br>Change in land use<br>patterns<br>Change in rate of<br>deforestation|
|Fiscal balance|Broadened tax base<br>Reduction in agricultural input<br>subsidies<br>Reduction in energy subsidies<br>Reduction in rural development<br>elimination of some poverty safety<br>nets|Increased rate of economic activity<br>Decreased agricultural input use<br>Increased energy conservation<br>Decreased research and extension<br>Increased poverty levels|More efficient resource<br>use<br>Lower air and water<br>pollution levels<br>Decreased soil<br>degradation and<br>agriculturalrun-off|
|Private sector<br>development|Sale of public enterprises<br>Promotion of private sector<br>promotion|Increased industrial production and<br>efficiency|Change in industrial<br>pollution|
|Redistribution|Land reform|Changes in income and consumption|Change in pattern of land|
|of income and|Labor protection laws|patterns|use|
|wealth|Social and safety net programs|I_I_I||
|a/ Some of thes<br>**_Source:_**Based o|e outcome assume that no corrective me<br>n Noman 1988, Shafik and Bandyopad|asures were taken.<br>hyay 1992, Munasinghe 1993, and World|Bank 1994a.|

6