rafmacalaba/datause-encoder-data huggingface dataset:
for investments in physical capital, but were targeted at small firms with revenue below £22.8 million.<sup>20</sup> To ensure that our results are only capturing the effects of the AIA, and not any residual impacts of the FYA, as a robustness test we exclude firms in our sample with revenue below the threshold necessary to qualify for the FYA.

A second policy of interest was an alteration to the definition of SMEs by the EU in 2008 which in turn affected qualification for the R&D Tax Relief Scheme for UK firms. This definition change shifted the qualifying threshold of assets from €43 million to €86 million, the employment threshold from 249 to 499, and the sales threshold from €50 million to €100 million (Dechezleprˆetre et al., 2016). Again, we explore the robustness of our findings to the exclusion of firms that become eligible for the R&D incentive.<sup>21</sup>

# **3 Data and statistics summary**

The research relies on four types of data: panel data on firm use of cloud, big data and AI technologies; matched employer-employee data; details regarding the introduction and changes to the AIA; and firm capital investment data.

All firm level data are taken from the Office for National Statistics (ONS), which is the UK’s Census Bureau equivalent. Information on cloud, big data analytics and AI adoption is available through the E-commerce Survey. The survey contains questions on firm use of different types of cloud computing, including its use for hardware services such as data, storage, processing, and software services, such as finance software, office software, customer relationship management software and email. We code the firm as an adopter of cloud technologies if it uses any of these different cloud services and zero otherwise.

The survey also includes questions on the use of big data analytics and AI. We construct a measure of firm use of big data as a binary variable which is equal to 1 if an enterprise

> 20See Maffini et al. (2019) for further discussions on the FYA.

> 21 During our sample period the UK did not have any policies that were specifically targeted at digital technologies beyond the AIA. A capital incentive scheme targeting IT investment by small businesses was in place between 1st April 2000 to 31st March 2004 which was empirically investigated by Gaggl and Wright (2017).

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Table 7: Urban Infrastructure Investment Ratios

|Year|Ratio to Total Fixed|Ratio to GDP|
|---|---|---|
||Asset Investment||
|1980|1.2%|0.2%|
|1985|2.5%|0.7%|
|1990|2.7%|0.7%|
|1995|4.0%|1.4%|
|2000|5.8%|2.1%|

Source: Construction Industry Publication of China as reported in Fu Tao (2004) Government experts have estimated that approximately 20 to 30 percent spent on urban infrastructure construction during the 10<sup>th</sup> Five Year Plan period, ending in 2005, will be expended by local, provincial, and central governments directly from budget (fiscal) resources. The remainder will be financed by a combination of domestic loans, foreign and multilateral loans, revenue from asset earnings and asset sales, and various forms of market-based financing.

# THE FISCAL FRAMEWORK AND INFRASTRUCTURE FINANCING

# OPTIONS

Perhaps the best way to examine the linkage between the national fiscal framework and national fiscal policy, on the one hand, and urban infrastructure 35
WPS3795 systems. That is, weaker legal systems may have more bank-based rather than capital-markets-based financing (Rajan and Zingales 2003, Ergungor 2004). By extension, this argument may apply to relationship lending versus the transactions technologies.

# **C.  The social environment**

The social environment may also affect SME credit availability. Evidence suggests that the level of social capital and trust may be important in facilitating the writing and enforcement of financial contracts. Social capital, as proxied by electoral participation, has been found to be significant in explaining regional differences in some measures of entrepreneurial activity, such as number of firms and new firm entry (Guiso, Sapienza, and Zingales 2004). Common language may also help develop mutual trust and facilitate relationship building. It has also been found that cultural differences across countries are associated with differences in the level of investor protection (Stulz and Williamson 2003).

The greatest impact of the social environment is likely on relationship lending because social norms, religion, and culture may have the most effect on the production of soft information and the ability of banks to use this information to forge relationships. The social environment could also affect the production and use of hard information used in the transactions technologies, but likely to a lesser degree.

# **D. The tax and regulatory environments**

The tax and regulatory environments may have significant effects on SME credit availability. For example, stamp taxes on factored invoices and certain types of value-added taxes can have negative impacts on factoring. Changes in capital regulations and tougher bank supervision in the U.S. are also often cited as contributing to the reduction in supply of business credit or “credit crunch” in the U.S. in the early 1990s (e.g., Berger and Udell 1994, Peek and Rosengren 1995). The implementation of the Basel II capital requirements may also have an impact in the future by imposing a different implicit tax on SME lending and favoring some lending technologies over others for some financial institutions. For example, large banking organizations may have more favorable capital requirements if they manage SME loans as part of a pool, which may favor some of the transactions technologies over relationship lending (Altman and Sabato 2005, Berger 2006).<sup>16</sup>

> 16 In the U.S., the largest banks are generally expected to have lower capital requirements on many SME loans, while in other nations, banks of all sizes may have lower requirements on many SME loans.  To receive the favorable retail loan categorization, the exposure to the SME must be under €1 million, and the credit must be managed as a retail exposure as

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income countries. To minimize the role of measurement error, we exclude households in the top and bottom 0.5% of the status quo expenditure distribution in all our analyses. All statistics derived from household surveys presented in the remainder of the paper are weighted using survey weights. For the relatively few surveys for which survey weights are not available, we simply assume each household has the same weight.

Expenditure shares are reported in Table 2. We show averages for six major expenditure aggregates, namely Staple Agriculture, Non-Staple Agriculture, Manufactured Goods, Non-Traded Goods, Other Goods, and Home Consumption. Expenditure on food is the dominant expenditure category, accounting on average for 45 percent of all household spending across countries, which is not surprising since the bulk of countries in our sample are low income countries with an average poverty rate of 35 percent (using national poverty lines) and an average GDP per capita of US$ 1879. This focus on poor countries also helps explain why home consumption is important, accounting for an average budget share of 17 percent across countries and for more than a third of all expenditure in Ethiopia, Madagascar, Mali and Uzbekistan. Spending on manufacturing goods on average accounts for 17 percent of overall household expenditure, and spending on non-tradables accounts for 15 percent.

Average income shares for staple Agriculture income, Non-Staple Agricultural income, Wages, Family Enterprise Income, Other income, and Own Home Production are reported in Table 3. Wage income is the single most important source of income, accounting on average for 29 percent of household income across countries, and for 39 percent if we weigh countries by their GDP, suggesting that the importance of this source of income increases with development. The value of autoconsumption accounts for 23 percent of household income. Profits from running farms and other family businesses account for 17 and 13 percent of household income, respectively. These averages hide important heterogeneity across countries, which reflects differences in structural features of their economies and heterogeneity in survey design (including coverage of different sources of incomes and expenditures).

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**Table 26: Impact of Energy Subsidy Reform (Scenario 1) on Quantities Consumed**

||Gasoline (liter)|Diesel<br>(liter)|Electricity (kWh)|LPG Bottle<br>(15 kg)|Kerosene<br>(liter)|
|---|---|---|---|---|---|
|Quintile_1|-48.1|-1.2|-424.4|-0.9|-1.0|
|Quintile_2|-58.3|-1.3|-482.4|-0.9|-1.7|
|Quintile_3|-60.2|-1.0|-485.3|-0.9|-1.9|
|Quintile_4|-60.3|-1.0|-469.4|-0.9|-2.3|
|Quintile_5|-56.0|-1.2|-508.1|-0.8|-2.3|
|Total scenario 1|-282.8|-5.6|-2,369.5|-4.4|-9.3|
|Total scenario 2|-454.2|-9.0|-3,843.2|-6.6|-14.4|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

# **6. The political economy of reforms**

There have been attempts at subsidy reform during the decade that preceded the revolution but they did not last. In the early 2000s, following the removal of international sanctions, Libya embarked on a reform path to modernize and open up its economy (Vandewalle 2011), and cutting subsidies seems to have been an important part of that program (Wahby 2005). Despite widespread opposition among the population, the government proceeded with the reform, raising fuel, diesel, and electricity prices in 2005 and completely liberalizing the price of some food products. By 2006, only four food products were still subsidized: flour, rice, semolina and pasta. In 2007, the government also eliminated the subsidy on pasta, leaving only 3 food products subsidized, and, in compensation, tried to put in place a transfer system of 4 dinars per capita per month. However, the government was unable to dispense this cash transfer. Still, subsidies remained restricted to the three items just listed, until early 2011 when Gadhafi, in an attempt to quell the revolutionaries’ demands, extended food subsidies back again to 13 items.

The political economy of the Gadhafi period was entirely driven by the Colonel’s decisions and these decisions served budget interests or short-term political objectives. The post Gadhafi period has been characterized by internal conflicts among various factions that participated to the revolution and by a very volatile political environment. This made subsidy reforms very difficult to implement and the possibility of a public debate on subsidy reforms almost impossible. High oil and gas prices that characterized the period between the revolution in 2011 and the first half of 2014 helped to boost government revenues but the internal conflict over natural resources limited the possibility to exploit oil reserves to their full potential. The most recent slump in the price of crude oil and the continued internal instability are contributing to increase the pressure on government finances while keeping subsidy reforms very difficult to implement from a political perspective. Hence, Libya remains the most extreme of the cases in the MENA region in terms of the size and variety of subsidies, in terms of weight of subsidies on the government budget and in terms of lack of reforms and it will be very unlikely to see a reform of the subsidies system anytime soon.

30
of the world, in particular, developing countries. When MRAs are open to ...rms regardless of origins, both intra-regional trade and trade with the rest of the world, especially with developing countries, rise substantially.

We noted in the introduction that multilateral rules on goods trade have taken a permissive approach to regional agreement on standards. It is neither feasible nor desirable to restrict the freedom of countries to harmonize or mutually recognize their standards. However, more could be done to protect the rights of countries that are excluded from such agreements. This is particularly important because few of the agreements on standards include developing countries, and the big di¤erences in social preferences over issues such as safety and the environment suggest that few developing countries are likely to be party to such agreements with industrial countries in the foreseeable future. Multilateral rules should make it more di¢ cult to conclude agreements of the type that impose an unnecessarily high price of exclusion, especially on developing countries. Thus, the imposition of restrictive rules of origin, which deny the bene...ts of mutual recognition agreements to third countries for no legitimate reason, should be outlawed. It is harder to prevent the upward harmonization of standards. But it may be possible to build on the presumption that international trade law already creates in favor of the use of international standards by individual countries (in Articles 2.4 and 2.5 of the WTO’s Agreement on Technical Barriers to Trade) and oblige countries that harmonize their standards to demonstrate why international standards or the less strict of the original standards are not adequate to meet their regulatory goals.

This paper should be seen as the beginning of a research program, and there remains much scope for deepening the analysis. In particular, two types of industry-level data would help: ...rst, on how the level of harmonized standards compares with the standards that countries originally imposed; second, the implications of complying with standards for the cost of ...rms, across industries and countries. Such data would make it feasible to carry out the industry-level analysis that would ideally be the next step in this research program.

29
<!-- Start of picture text --> 5<br>Y<br>4<br>c<br>3<br>1 2 A X Abatement potential at TMtCO2/yr<br>Marginal cost $/tCO2<br><!-- End of picture text -->

**Figure 1:** A measure-explicit marginal abatement cost curve. The general appearance of the curve suggests that it is meant to be used as an abatement supply curve, i.e. that the “abatement demand” X should be met by implementing measures 1 to 4, possibly using the carbon price Y.

take decades to implement. This _technical inertia_<sup>1</sup> means that it can be optimal to implement more expensive measures before cheaper ones, if the former have a large potential but are long to implement (Vogt-Schilb and Hallegatte, 2014). Moreover, focusing on short-term targets (e.g., for 2030) without considering longer-term objectives (e.g., for 2050 and beyond) could lead to carbon-intensive lock-ins that would make it very expensive (or even impossible) to achieve the long-term objectives.

MAC curves do not report information on technical inertia, and should not be misinterpreted as supply curves, as they frequently are (e.g. DECC, 2011, fig 17).<sup>2</sup> To avoid this, we proposed in Vogt-Schilb and Hallegatte (2014) to complement MAC curves with information on implementation speeds, and to use a simple optimization tool to derive optimal strategies taking inertia into account.

In this paper, we use a MAC curve built at the World Bank for studying lowcarbon development in Brazil in the 2010-2030 period to confirm our theoretical results, and to test our proposed improvements. Lack of data beyond 2030 does not allow us to demonstrate that using the 2010-2030 MAC curve to design a mitigation strategy would lead to suboptimal choices in view of longer-term objectives (2050 and beyond). We can however illustrate this problem by assuming that we want to achieve an objective for 2030, and that we use the MAC curve to design a mitigation strategy for the 2010-2020 period only. Because of inertia, our theoretical results suggest that the resulting strategy would be suboptimal, with insufficient investments in some expensive, long-to-implement and large long-term potential options. And indeed, we find that a strategy for 2010-2020 that disregards the longer-term target under-invests in options such as metro and other transportation infrastructure. Conversely, it over-invests in marginal, cheap but low-potential options, such as heat integration and other improvements in existing refineries.

> 1 Using the wording by Grubb et al. (1995), Ha-Duong et al. (1997); Lecocq et al. (1998) and Vogt-Schilb et al. (2012).

> 2 Such interpretation would lead to “ _implement the cheapest measure first, preferring measures with a lower total saving potential but more cost-effective than those with a higher GHG saving potential in absolute terms_ ” (W ̈achter, 2013).

2
26 economy, and the fluidity of the political environment. This knowledge should help the World Bank to convince authorities in developing countries that the infornation revolution that is underway in finance today makes it short-sighted and unfair to adopt policies that intentionally or unintenti-naily permanently subsidize weak banks.

# REFERENCES

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Demirgdi-Kunt, Asli, 1992. "Creditor Country Regulations and Commercial Bank Lending to Developing Countries." Washington: The World Bank.
**Table 1: Annual technical potential of solar energy and energy demand (Mtoe)**

|**Region**|**Minimum**|**Maximum**|**Primary**|**Electricity**|
|---|---|---|---|---|
||**technical**|**technical**|**energy**|**demand**|
||**potential**|**potential**|**demand**<br>**(**2008**)**|**(**2008**)**|
|North America|4,322|176,951|2,731|390|
|Latin America & Caribbean|2,675|80,834|575|74|
|Western Europe|597|21,826|1,822|266|
|Central and Eastern Europe|96|3,678|114|14|
|Former Soviet Union|4,752|206,681|1,038|92|
|Middle East & North Africa|9,839|264,113|744|70|
|Sub-Saharan Africa|8,860|227,529|505|27|
|Pacific Asia|979|23,737|702|76|
|South Asia|907|31,975|750|61|
|CentrallyPlanned Asia|2,746|98,744|2,213|255|
|Pacific OECD|1,719|54,040|870|140|
|**Total**|**37,492**|**1,190,108**|**12,267**|**1,446**|

Note: The minimum and maximum reflect different assumptions regarding annual clear sky irradiance, annual average sky clearance, and available land area. Source: Johansson et al. (2004); IEA (2010) Kurokawa et al. (2007) estimate that PV cells installed on 4% of the surface area of the world‟s deserts would produce enough electricity to meet the world‟s current energy consumption. Similarly, EPIA (2007) estimates that just 0.71% of the European land mass, covered with current PV modules, will meet the continent‟s entire electricity consumption. In many regions of the world 1 km<sup>2</sup> of land is enough to generate more than 125 gigawatt hours (GWh) of electricity per year through CSP technology.<sup>3</sup> In China, for example, 1% (26,300 km<sup>2</sup> ) of its “wasteland” located in the northern and western regions, where solar radiation is among the highest in the country, can generate electricity equivalent to 1,300 GW – about double the country‟s total generation capacity projected for year 2020 (Hang et al, 2007). In the United States, an area of 23,418 km<sup>2</sup> in the sunnier southwestern part of the country can match the present generating capacity of 1,067 GW (Mills and Morgan, 2008).

> 3 With an assumption of CSP efficiency of 8m2/MWh/year, which is in the middle of the 4-12 m2/MWh/year range offered by Muller-Steinhagen & Trieb (2004).

6
# REFERENCES

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- Baiocchi, G., “Emergent Public Spheres: Talking Politics in Participatory Governance,” _American Sociological Review_ , 68(1), 52-74, February 2003.

Barros, R. and D. Lam, “Desigualdade de renda, Desigualdade em educação e escolaridade das crianças no Brasil,” _Pesquisa e Planejamento Economico_ , 23(2), 191-218, 1993.

- Behrman, J., A. Gaviria, and M. Székely, “Intergenerational Mobility in Latin America,” _Economía_ , 2(1), 1-44, Fall 2001.

- Bouillon, C., A. Legovini and N. Lustig, “Rising Inequality in Mexico: Returns to Household Characteristics and the ’Chiapas Effect’,” Inter-American Development Bank, mimeo, October 1998.

- Bourguignon, F., F. Ferreira, and P. Leite, “Beyond Oaxaca-Blinder: Accounting for Differences in Household Income Distributions Across Countries,” Departamento de Economia. PUCRio, TD #452, March 2002.

- Bourguignon, F., F. Ferreira, and N. Lustig, “The Microeconomics of Income Distribution Dynamics in East Asia and Latin America,” World Bank-IADB Research Proposal, 1998.

- Bourguignon F., M. Fournier, and M. Gurgand, “Fast Development With a Stable Income Distribution: Taiwan, 1979-94,” _Review of Income and Wealth_ , 47(2), 139-63, June 2001.

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Bravo, D., D. Contreras, T. Rau, and S. Urzua, “Income Distribution in Chile 1990-1998: Learning from Microeconomic Simulations,” working paper, Department of Economics, Universidad de Chile, 2000.

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