rafmacalaba/datause-encoder-data huggingface dataset:
10

**Table 3 Average Prices and Deliveries of Natural Gas to U.S. Consumers, 1995**

|Consumer category|Average price<br>(dollars per 1,000 cubic feet)|Deliveries<br>(millions of cubic feet)|Deliveries as a<br>percentage of total|
|---|---|---|---|
|Residential|6.06|4,850,318|24.7|
|Commercial|_5.05_|3,031,077|15.4|
|Industrial|2.71|8,579,585|43.6|
|Electric utilities|2.02|3,196,507|16.3|
|Vehicle fuel|-|2,674|0.01|
|Total|3.79|19,660,161|100|

- Not available.

_Source:_ U.S. Department of Energy, Energy Information Administration.

The average price of natural gas increased dramatically between 1970 and 1984 but has been steadily decreasing since then (figure 5). Price increases before 1984 were caused by increasing demand for natural gas, price rigidity imposed by regulation, and the impact of the oil crisis. Deregulation and increasing competition in the wholesale gas market have pushed wholesale and some retail prices down since 1985.

Large end users have benefited from the introduction of open access and the unbundling of interstate pipeline transportation. These reforms have enabled them to participate in the cost savings achieved through competition in the wholesale gas market. The nominal average prices of natural gas at the wellhead and at large end users' consumption sites decreased dramatically between 1984 and 1995. The average wellhead price fell from $2.66 per thousand cubic feet in 1984 to $1.55 in 1995. The average retail price paid by industrial consumers decreased from $4.22 per thousand cubic feet in 1984 to $2.71 in 1995, and that paid by electric utilities from $3.70 to $2.02 (see table 3).

Small end users have also benefited from deregulation of the gas industry, but their gains have been much smaller, mainly because of their limited choice in supply. Most small end users remain captive to the local distribution company. State regulation of retail prices has allowed only limited transfer of cost savings from the wholesale market to small users.

The average retail price paid by small end users declined between 1984 and 1988 but then increased again. Commercial and residential users paid on average $5.55 and $6.12 per one thousand cubic feet in 1984. In 1988 average prices bottomed at $4.63 and $5.47 per one thousand cubic feet. Then they rose again, peaking above the 1984 levels in 1994. In 1995 commercial and residential users paid on average $5.05 and $6.06 per one thousand cubic feet.

Retail prices of natural gas vary widely across the United States. Consumers in the Northeast and Southeast tend to face the highest prices, while those in the Midwest,
**Table 4 Municipal governments borrowing**

|Name of Municipality|Total external borrowings<br>(R millions)|
|---|---|
|CapeTown|2653.3|
|Johannesburg|3 842.8|
|Tshwane (Pretoria)|1539.9|
|eThekwini(Durban)|3 085.4|
|Ekurhuleni(EastRand)|1068.4|
|Nelson Mandela (PortElizabeth)|296.9|
|TOTAL|12 486.7|

Source: South Africa (2004b) Note: These figures exclude the recent bond issues by Johannesburg totaling R2 billion.

The key lenders have been the Development Bank of Southern Africa with loans totaling to a little over R5 billion, and a private sector lender, the Infrastructure Finance Corporation (INCA), with loans of just under R3 billion. Other private banks account for most of the remaining amounts.

Loans to metropolitan governments represent approximately two-thirds of all loans to municipalities by the DBSA, and just under three quarters of loans by INCA.

# 6.2. <u>The role of borrowing in the financing of the capital budgets of metropolitan councils</u>

Tables 5 & 6 show the revenue sources of the Cape Town metropolitan municipality together with the funding sources for the capital budget and the funding sources of Johannesburg.

Municipalities in South Africa are in the process of introducing more modern and nationally standardized accrual accounting systems<sup>9</sup> . The Cape Town figures are presented in terms of the new definitions.

Municipalities are required to establish an External Financing Fund. This fund borrows from lenders such as DBSA, INCA and the private banks and lends to the various departments of the municipality.

The Capital Grants include housing grants, which are received via the province and implemented by the municipality.

> 9 Accrual accounting has been used by municipalities in South Africa for more than two decades; however, new national standards are now being introduced bringing the system as close as is feasible to current internationally recognized GAAP. Part of the motivation for this change has been to the desire to improve transparency for prospective private sector lenders. However, there are many other advantages.

11
methods. Studies have, however, cast doubt on laypersons’ abilities to accurately estimate small probabilities (Viscusi and O’Connor, 1984). If this is the case, the values from revealed preference studies correspond to risk changes of an unknown magnitude.

This has led to the use of stated preference studies. In a pioneering study to value morality risks in a transport context, Jones-Lee, Hammerton and Philips (1985) asked respondents what they would pay to travel on a safer bus, i.e., what they would pay to reduce their risk of death on a bus trip from (e.g.) 8 in 100,000 to 4 in 100,000 and from 8 in 100,000 to 1 in 100,000. Other studies have asked respondents about their WTP for living in a city with lower risk of mortality from road accidents (Guria et al., 2005, Viscusi, 1995), or what they would pay to install an optional safety device in their car (Dubourg, Jones-Lee and Loomes, 1997; Corso, Hammitt and Graham, 2001; Persson et al., 2001).<sup>2</sup> The vast majority of WTP studies in the context of road safety have been conducted in high income countries; few have been conducted in developing countries. Exceptions include stated preference studies in Chile, Thailand and Malaysia (Ortuzar et. al., 2000; Vassanadumrongdee and Matsuoka, 2005; and Melhuish et. al., 2005). In spite of the very different pattern of road traffic deaths in developing countries, these studies have relied on the same scenarios as studies in high income countries.<sup>3</sup> We have attempted to construct scenarios that reflect the profile of road accidents in developing countries, where pedestrians and motorcyclists bear the brunt of road fatalities.

> 2 See also Schwab Christie and  Soguel (1995).

> 3 Vassanadumrongdee and Matsuoka (2005) elicit willingness to pay for an airbag, while Melhuish et al. (2005) use a scenario similar to Jones-Lee, Hammerton and Philips (1985).  Ortuzar et al. (2000) ask

- 5 -
bureaucratic processes of obtaining duty exemption. Finally, an export tax on unprocessed cashmere, intended to encourage production and export of processed cashmere products, has resulted instead in an increase of smuggled cashmere into China, and not to the intended boost in processed cashmere output.<sup>59</sup>

**Figure 11. Tax rates and tax administration**

<!-- Start of picture text --> 60.0<br>Mongolia<br>50.0 Bangladesh<br>40.0<br>Uganda<br>Kyrgyz.<br>30.0 RussiaAzerbaijan China Zambia<br>Indonesia<br>20.0 Cambodia EAP avg.ECA avg.<br>10.0 Kazakhstan Malaysia<br>0.0<br>0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0<br>% of firms citing tax rates as a major or severe obstacle<br>severe obstacle<br>% of firms citing tax<br>administration as a major or<br><!-- End of picture text --> Source: World Bank (2006b).

**Table 17. Average no. of days spent a year meeting with tax inspectors**

|Mongolia|9.0|Europe & Central Asia|2.8|
|---|---|---|---|
|East Asia & Pacific|5.4|Azerbaijan|1.3|
|Cambodia|7.2|Kazakhstan|3.2|
|China|12.0|Kyrgyz Republic|3.2|
|Malaysia|3.8|Russian Federation|2.5|

Source: World Bank (2006b).

# **_3.3.5 Is corruption a binding constraint to private sector growth?_**

According to more than half of the firms that participated in the most recent Productivity and Investment Climate survey (2004) corruption is the number one obstacle to growth and pervades every sphere of business activity. Firms paying bribes to obtain access to electricity, water, and communication infrastructure are respectively 26 percent, 15 percent and 23 percent of all firms surveyed. A quarter of these firms pay bribes for customs clearances of traded goods as the number of documents and steps required for trading across borders as well as the time spent on clearance procedures is much higher in Mongolia than in other countries in the world (Table 18). Unofficial payments, required for obtaining licenses, and the average bribe for different types of licenses were high – around 40 percent of the official fees according to World Bank (2006b).

> 59 The government has plans to abolish this export tax.

31
Conditions i and ii ensure that the decline in unemployment is substantial. A decline of 3 percentage points over a four year period is crudely equivalent to a standard deviation away from the mean 4 year change in unemployment. Condition ii ensures that the threshold for an unemployment reduction episode is higher for countries with higher levels of unemployment; for example, in a country with 40% initial unemployment, it needs to reduce by at least 10 percentage points for that reduction to qualify as an episode, whereas a country with an initial unemployment of 20% requires a reduction in unemployment of 5 percentage points or more. By construction, countries with lower than 3% initial unemployment cannot experience an episode. Condition iii requires that the decline in unemployment is sustained for at least three years, and does not merely reflect cyclical fluctuations. Condition iv ensures that unemployment reduction episodes commence with a decline in unemployment, and condition v rules out counting the same unemployment reduction spell as two separate events.

One potential concern is that unemployment could decline due to falling labor market participation. We choose to focus on declines in unemployment, as opposed to increases in employment as a share of working age population since participation can fall as countries become richer, as more people of working age attend high school and university, or retire early.<sup>5</sup> Still, to control for this possibility, we also report results including only episodes where labor force participation remains constant or increases and results remain robust.

Our primary data source is the World Bank’s _World Development Indicators_ 1980-2008. The earliest year for which the onset of an unemployment event can be identified is 1980, whereas the last year is 2001. In case information on unemployment is missing for one year, it is imputed using the average of the unemployment rates in the preceding and the subsequent years for the purpose of identifying reduction episodes, but not in the subsequent analysis. Countries for which we do not have at least 8 consecutive annual observations on unemployment after imputing it are excluded. These restrictions reduce the sample to 94 countries. Arguably as a result of lower labor market monitoring capacity in poor countries, there are no low-income countries in our sample, only five countries from Sub-Saharan Africa and two from South Asia. Thus, our results are most relevant for middle income, transition, and OECD countries. See Table 1 for a description of all variables and their source.

> 5 In addition, employment to population ratios, typically measured as the population over the age of 15 (or 25) that is employed, are potentially (more) impacted by changing demographics, such as those due to increases in life expectancy.

5
| POLICY RESEARCH WORKING PAPER 1613

# Summary findings

A recent but rapidly growing empirical literature focuses on the relationship between public and private capital. But for the most part, it ignores the heterogeneity of public investment.

In many countries, especially in the developing world, public investment includes not only basic infrastructure projects but also commercial and industrial projects similar to those undertaken by the private sector. And those two types of public investment are likely to have quite different effects on the accumulation of private capital.

Using data from India, Serven examines this issue empirically by implementing a simple analytical model encompassing two types of public capital. The empirical results show that in the long run capital for public infrastructure projects crowds in private capital - other types of public capital have the opposite effect. But in the short run, both kinds of public investment may crowd out private investment.

- - This paper a product of the Macroeconomics and Growth Division, Policy Research Department is part of a larger effort in the department to understand the determinants of private investment. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Emily Khine, room NI 1-061, telephone 202473-7471, fax 202-522-3518, Internet address kkhine@worldbank.org. May 1996. (36 pages)

_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 used and cited accordingly. The findings, interpretations, and conclusions are the authors'_ **_oun_** _and should not_ **_be_** _attributed to the World Bank, its Executive Board of Directors, or any of its member countries._ Produced by the Policy Research Dissemination Center
7. R. W. Fairlie, The impact of covid-19 on small business owners: The first three months after social-distancing restrictions, _Working Paper 27462_ , National Bureau of Economic Research (2020).

8. X. Cirera, _et al._ , Policies to support businesses through the covid-19 shock: A firm level perspective, _The World Bank Research Observer_ **36** , 41 (2021).

9. J. Van Reenen, A. Valero, How is covid-19 affecting firms’ adoption of new technologies?, _Economics Observatory_ (2021).

10. L. Bellmann, _et al._ , Digitalisierungsschub in firmen w ̈ahrend der corona-pandemie, _Working Paper, Luxembourg Institute for Socio-Economic Research (LISER)_ (2021).

11. R. M. Baron, D. A. Kenny, The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations., _Journal of personality and social psychology_ **51** , 1173 (1986).

12. K. Imai, L. Keele, T. Yamamoto, Identification, inference and sensitivity analysis for causal mediation effects, _Statistical science_ **25** , 51 (2010).

13. J. J. Heckman, R. Pinto, Econometric mediation analyses: Identifying the sources of treatment effects from experimentally estimated production technologies with unmeasured and mismeasured inputs, _Econometric reviews_ **34** , 6 (2015).

14. V. Celli, Causal mediation analysis in economics: Objectives, assumptions, models, _Journal of Economic Surveys_ (2021).

15. J. J. Bai, E. Brynjolfsson, W. Jin, S. Steffen, C. Wan, Digital resilience: How work-fromhome feasibility affects firm performance, _Working Paper 28588_ , _Tech. rep._ , National Bureau of Economic Research (2021).

16. P.-H. Hsu, H.-H. Lee, S.-C. Peng, L. Yi, Natural disasters, technology diversity, and operating performance, _Review of Economics and Statistics_ **100** , 619 (2018).

21
_In_ Table 2. Analysb of **the** Types and Relative Importance of OECD Nontariff **Measures** on lmports from Developing Counties.

|||||Trade Cove|rage Rados_<br>'<br>|||
|---|---|---|---|---|---|---|---|
|Product Group (SITC)|1992 Imports from<br>Developing<br>Countries (Sbill.)|All<br>Nontariff<br>Measures2|Variable<br>Levies and<br>Surcharges|Quantitative<br>Restrictions|Volunta<br> <br> <br>Export<br>RestraimLs|Price<br>Control<br> <br>Measures|Other Entry<br>Control<br>Measures|
|ALL NON-FIJEL ITEMS (0 to 9 - 3)|530,740|18.4|1.9|2.2|12.4|1.5|2.3|
|All Foods (0+1+22+4)<br>|77,083<br>|18.3<br>|9.6|5.4|2.1|3.1|2.4|
|PFtl and Live Animals (0)<br>|67,732<br>|19.2<br>|10.6<br>|5.2<br>|2.4<br>|3.3|2.6|
|Oilseeds and Nuts (22)<br> <br>|2,502<br>|9.8<br>|0.5<br>|9.3|0.0|0.0|0.0|
|AnImal-Vegetable<br>Oils (4)|2,829|7.8|5.6|1.4|0.0|0.0|0.8|
|agricultural Materials (2-22-27-28)|20,076|10.9|0.1|1.A|0.1|0.0|10.0|
|Ores and Metals (27+28+67+68)<br>|41,627<br>|10.0<br>|0.1|0.5|5.5|_6.5_|0.0|
|Perrous Metals (67)<br>|11,063<br>|35.6<br>|0.0|1.0|20.2|23.6|0.0|
|Non-ferrous Metals (68)|15,116|0.0|0.0|0.0|0.0|0.0|0.0|
|Mineral Fuels (3)|164,420|16.5|2.4|14.1|0.0|0.0|0.0|
|Chemicals (5)|21,806|3.4|1.1|2.2|0.1|0.0|0.0|
|Other Manufactures (6 to 8-67-68)<br>|354,370<br>|21.6<br>|0.4|1.7|18.3|0.5|2.1|
|Leather (61)<br>|3,741<br>|_56.5_<br>|0.8|0.3|0.2|0.0|55.2|
|Textile Yam & Fabric (65)<br>|18,375<br>|52.1<br>|1.7<br>|6.2<br>|49.0<br>|0.0|0.0|
|Clothing (84)<br>|75,293<br>|64.3<br>|0.8|3.4|59.3|0.0|3.3|
|Footwear (85)|15,844|34.0|1.2|1.0|29.9|0.0|3.3|
|ALL ITEMS (O to 9)|695,160|18.0|2.0|5.0|9.5|1.2|1.8|

Source: World Bank-UNCTAD SMART Database. Trade statistics from UN COMTRADB records.

INTM groups are defined as follows. Variable levies and surcharges include all variable import levies (including variable components), product specific surcharges. minimum, reference, or basic import price regulations, price surveillance and 'voluntary" export price restraints. Quantitative restrictions include prohibidons, quotas (global or country specific), .te monopolies and non-automatic licensing requirements. 'Voluntary export restraints include measures under the MFA and similar textile quotas as well as other VERs _(_ quantity) that were negotiated outside the textile and clothing sectors. Other entry control measures include a variety of restrictions like local content regulations, Mrhe coverage shares for the individual types of NT.Ms may sum to more than the 'all NTM coverage ratio due to 'stacking' or the multiple applicadon of NTMs on a specific product. Imports are for those OECD countries listed in the notes to Table 1.
# Table 2 Contributions of Shocks to Interest Rate Variation

(Percent of total variance)

|Shocks|70-23|70-98|99-23|70-84|85-98|99-07|08-19|20-23|
|---|---|---|---|---|---|---|---|---|
|Total Global Shocks|25.9|23.3|47.1|23.6|26.8|49.9|46.4|64.3|
|Oil Price|4.4|4.7|8.5|4.5|5.0|8.8|7.6|12.0|
|Global Supply|5.5|5.0|8.8|4.8|6.1|10.2|8.6|11.5|
|Global Demand|9.9|8.9|20.4|9.0|8.2|15.7|20.7|26.8|
|Global Monetary<br>Policy|6.0|4.8|9.4|5.3|7.5|15.3|9.5|13.8|
|Total Domestic Shocks|74.1|76.7|52.9|76.4|73.2|50.1|53.6|35.7|
|Domestic Supply|9.6|11.3|7.1|10.0|10.5|6.8|8.4|5.1|
|Domestic Demand|33.6|34.2|23.9|35.9|32.5|21.9|23.7|16.9|
|Domestic Monetary|||||||||
|Policy|30.9|31.2|22.0|30.5|30.2|21.4|21.5|13.7|

Sources: Authors’ calculations based on G5 economies (Canada, the euro area, Japan, the United Kingdom, and the United States). Notes: Forecast error variance decompositions of policy rates over the 40-month forecasting horizon, based on country-specific FAVAR models that consist of four global variables (inflation, output growth, policy rates, and oil prices) and three domestic variables (inflation, output growth, and policy rates).

45
_Table 1: Summary Statistics_

|Variables(N=245)|Mean|Std. Dev.|Min.|Max.|
|---|---|---|---|---|
|GDP per capita growth|1.63|4.56|-8.27|49.7|
|Total factor productivity (TFP)|0.58|0.34|0.12|2.05|
|GDP per capita|1447.46|2062.42|124.35|14241.83|
|Exchange rate regime coarse class|2.01|1.19|1|6|
|Exchange rate regime fne class|6.24|4.25|1|15|
|Domestic credit to private sector (% of GDP)|21.16|21.36|0.97|150.97|
|Private credit (% GDP)|19.78|20.46|0.003|145.39|
|Education|34.36|22.47|2.64|104.46|
|Trade Openness (% of GDP)|75.53|35.94|13.59|211.57|
|Infation (annual %)|15.8|37.08|-3.42|302.12|
|Government expenditure (% of GDP)|15.07|6.02|2.89|37.32|
|Bank crisis|0.08|0.2|0|1|

# **3 Empirical Analysis**

## **3.1 Data**

The data for the study covers a panel of 48 African countries, spans the period 1974-2015, and has been transformed into non-overlapping five year averages.<sup>3</sup> The data for most of the variables are obtained from the World Development Indicators and International Financial Statistics. Productivity measures are obtained from Penn World Tables Version 9.0 (Feenstra et al., 2015). The data for exchange rate regimes comes from Reinhart and Rogoff (2004, 2009), Levy-Yeyati and Sturzenegger (2016) and Ilzetzki et al. (2017). The classification of exchange rate regimes is based on 2 schemes: coarse classification with 6 categories and fine classification with 15 categories.<sup>4</sup> Table 1 presents some basic summary statistics for the variables.

Figure 1 presents preliminary evidence which suggests that, on average, there seems to be a negative association between exchange rate flexibility and GDP per capita growth, as well as between exchange rate flexibility and productivity. The relationship between exchange rate flexibility and per capita growth, however, varies across different levels of financial development as . depicted in Figure 2 For countries in the upper quartile of the financial development distribution,

> 3Averaging over five year periods has become a standard approach in analyzing long run growth to filter out business cycle fluctuations and identify long term growth trends (Agenor and McDermott, 2000; Aguiar and Gopinath, 2007; Aghion et al., 2009). Unless a significant share of the data is missing, we include all 48 countries in the estimations.

> 4The classification is presented in Table A1 in the appendix. Figure A1 (in the appendix) also provides a representation of the evolution of exchange rate policy across Africa, and shows that in recent years, about half of the countries have operated some form of an exchange rate peg.

9
_<u>Table 9A: Summary of scenarios in billions of PKR</u>_

|**Modality**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|**2031**|**2032**|**2033**|**2034**|**2035**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Business as usual**(without additional<br>interventions)|76.5|82.8|89.8|97.5|106.0|115.4|125.7|137.1|149.9|164.0|179.6|197.1|216.6|
|**Augmented package**(without additional<br>support from the private sector)|130.0|140.9|156|172.9|191.9|213.2|237.3|264.5|295.3|328.4|331.5|371.4|418.1|
|**Alternative delivery modalities**(combine<br>public provision, targeted vouchers to<br>leverage private sector and community-<br>basedmodels)|99.2|108.2|119.3|131.8|145.6|161.0|178.2|197.5|219.2|241.7|253.1|280.0|311.4|
|**“Core” package**(without new major<br>capitalexpenses)|86.3|93.6|102.2|111.8|122.5|134.5|147.9|163.0|180.1|199.3|211.1|234.0|260.9|

_<u>Table 9B: Summary of scenarios in percent of GDP</u>_<sup>_18_</sup>

|**Modality**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|**2031**|**2032**|**2033**|**2034**|**2035**|**Avg**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Business as usual**(without additional<br>interventions)|0.09|0.10|0.10|0.11|0.11|0.12|0.13|0.14|0.15|0.16|0.17|0.18|0.19|0.13|
|**Augmented package**(without<br>additional support from the private<br>sector)|0.15|0.16|0.18|0.19|0.21|0.22|0.24|0.26|0.29|0.31|0.31|0.34|0.37|0.25|
|**Alternative delivery modalities**<br>(combine public provision, targeted<br>vouchers to leverage private sector and<br>community-based models)|0.12|0.13|0.14|0.15|0.16|0.17|0.18|0.20|0.21|0.23|0.24|0.25|0.28|0.19|
|**“Core” package**(without new major<br>capital expenses)|0.10|0.11|0.12|0.12|0.13|0.14|0.15|0.16|0.18|0.19|0.20|0.21|0.23|0.16|

> 18 Percent of GDP computed using GDP at current market prices of 84,068.76 in 2023 from the SBP and an average annual growth rate of 2.5% as per SBP.

22