rafmacalaba/datause-encoder-data huggingface dataset:
14 The data employed for the analysis comes from three main sources. First each household in the 1996 Population Census of South Africa was matched to its local police station jurisdiction, to generate information on household composition, race, education, primary occupation, housing characteristics and access to services for each police station jurisdiction. Second, crime data for 1996 were obtained from the South African Police Service (SAPS). The crime information is a comprehensive database of crimes reported for the entire country by police station jurisdiction. Third, mean per capita expenditure and per capita expenditure inequality were estimated for each police station jurisdiction by applying a recently developed small area estimation technique to the South Africa 1996 Population Census, along with the 1995 October Household Survey and Income and Expenditure Survey. The estimation method is described in Appendix A.

# _Dependent variables_

We analyze six categories of property crime: residential burglary, vehicle theft, armed robbery with aggravated circumstances, rape, serious assault, and murder. Summary statistics for these crimes are presented in Table 1. Because theory has different implications for property crime and violent crime, we select crimes that fall, as much as possible, exclusively into one category or the other. Burglary and vehicle theft are property crimes that are generally non-violent, while serious assaults and rapes are violent crimes with no apparent direct pecuniary benefit to perpetrators.<sup>8</sup> We also examine two additional crimes that do not fall cleanly into one category but are often studied in the literature: murder and armed robbery. Although both are crimes that are

> 8 Vehicle thefts exclude carjackings.
Policy Research Working Paper 9397

# **Abstract**

Management practices matter for firm performance. As energy is one input in firm production, management practices may interact with energy use. Using a comprehensive firm-level database covering 31 countries, this study documents the link between structured management practices, energy use, and firm performance. The paper reports several findings. _First,_ although management is negatively correlated with energy expenditure, it bears a positive (or null) relationship with physical energy use, suggesting that management effort is directed toward saving costs but not reducing environmental impact. These results are primarily driven by the manufacturing sector. _Second,_ among the structured management practices examined, those relating to target-setting are associated with reduced energy expenditure intensity. _Third,_ generic management practices are correlated with greater discipline around energy management. _Finally,_ while generic practices are correlated with stronger firm performance in manufacturing and services, energy-centric practices show a positive association only in services. Vast heterogeneity in adoption and outcomes suggests that targeted approaches to encourage energy management practices in firms may be more effective than uniform ones.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at agrover1@worldbank.org.

_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 cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
intensity index of non-CO2 emissions in the SUST-RUS data (Xp,s,r) for those SUST-RUS industries (s) and regions (r) that correspond with the WTO model industries (g) and regions (reg). For example, the initial value of regional emission intensity index of SO2 (Sulphur dioxide) emissions for the WTO-model industry ELE (“Electricity”) in region MSC (“Moscow”) (emitintSO2,ELE,MSC0 ) is equal to the value of SUST-RUS emissions intensity index of SO2 (Sulphur dioxide) for SUSTRUS industry E_ely (Production and distribution of electricity) in the region **_reg1_** (“Central Federal District”) (XSO2,E_ely,reg1) .

# **Equation 4**

- 1.16. In the second stage (Equation 5), authors calculate the adjustment coefficient (kp) to rescale reference values of total emissions in 2001 (emitstatp<sup>2001</sup> ). For each pollutant (p), adjustment coefficient (kp) equals ratio of total volume of emissions of the pollutant ( _p_ ) in 2001 (emitstatp<sup>2001</sup> ) and estimated volume of emissions with initial values of regional emission intensity index of non-CO2 emissions in the WTO model. Estimated volume of emissions equals to product of initial values of regional emission intensity index of non-CO2 emissions in the WTO model �emitintp,g,reg0 � and 2001 production levels (Yg,reg2001).

# **Equation 5**

1.17. In the final, third stage of estimation of the adjusted regional emission intensity index of non-CO2 (emitintp,g,reg), the adjustment coefficient is applied to initial values of the intensity index (emitint0p,g,reg) (Equation 6).

# **Equation 6**

1.18. Values of regional emission intensity index of non-CO2 emissions in the WTO model region and sector list (emitintp,g,reg) are presented in Table A21-Table A30.

- 1.19. Average regional emissions intensity index of non-CO2 emissions, WTO model's sectoral classification (thousands tons of emission per billion of 2001 rubles of output) is presented in Table A31.

88
Table 2: DIDM estimates of infrastructure provision (GVA per Capita)

||_Dependent varia_|_ble: Gross V_|_alue Added _|_per Capita (log)_|
|---|---|---|---|---|
|||N2E|N2H|H2B|
|1970-2000 (E)|coef.|0.036<br>|0.021<br>|0.252<sup>_∗∗_</sup><br>|
||s.e.|(0.046)|(0.088)|(0.101)|
||Obs./Switchers|390 / 102|338 / 55|274 / 36|
|1970-1980|coef.|-0.030|0.273|0.081|
||s.e.|(0.057)|(0.174)|(0.175)|
||Obs./Switchers|168 / 35|134 / 21|89 / 4|
|1980-1996|coef.|0.139<sup>_∗∗_</sup>|-0.035|0.276<sup>_∗∗_</sup>|
||s.e.|(0.069)|(0.121)|(0.132)|
||Obs./Switchers|133 / 44|113 / 22|104 / 23|
|1996-2000|coef.|-0.063|-0.319<sup>_∗∗∗_</sup>|0.269<sup>_∗∗_</sup>|
||s.e.|(0.105)|(0.081)|(0.129)|
||Obs./Switchers|89 / 23|91 / 12|81 / 9|
|1970-2000 (D)|coef.|0.074|0.170<sup>_∗_</sup>|0.268|
||s.e.|(0.061)|(0.100)|(0.170)|
||Obs./Switchers|234 / 79|213 / 43|165 / 27|
|Placebo|coef.|-0.110|0.135|0.089|
||s.e.|(0.067)|(0.080)|(0.087)|
||Obs.|222|204|166|

_Notes: Bootstrapped standard errors (200 replications) in parentheses. ∗p<0.1, ∗∗p<0.05, ∗∗∗p<0.01. (E): estimated effect of the treatment at the time period of the infrastructure provision. (D): estimated effect one period after the infrastructure provision._ The results by period (rows 2-4) show a more contrasted picture. There were positive, albeit marginally significant, impacts of highways on output per capita in the 1970-1980. The provision of electricity alone positively affected output per capita in 1980-1996. By contrast, the period 1996-2000 shows negative impacts on gross value added per capita of the separate provision of highways. This could be due to the sequencing of investments targeting lagging regions in the later period, as discussed in Section 2.1. We see suggestive complementarity effects for the last period, as the point estimate of the electrification effect for municipalities already endowed with a highway (column H2B) is greater than that for electrification only (column N2E).

13
-94 -

in Transortation **EngiDnrn:** Proceedings of the Second International Conference, Minneapolis, Minnesota, August 18-21, 1991, American Society of Civil Engineers, New York, New York, pp. 358-362.

Churchill, Anthony (1972), in collaboration with Klaus Huber, Bike Meldau and Alan A. Walters, Road User Charges in Central America, World Bank Staff Occasional Papers Number 15, Johns Hopkins University Press, Baltimore, Maryland.

- City of Stockholm (1989a), "Proposal for an Electronic Fee System for Car Traffic in the Stockholm Inner City Area," The Streets and Traffic Administration, Stockholm, draft, pp. 1-10.

- ciy of Stockholm (1989b), -Zone Fee Proposal Using Car Cards," Street Department, Stockholm, pp. 1-9.

- Davies, Peter (1987), "Vehicle Detection and Classification," in Infmation Technology AppiEam in Transpot, ed., Peter Bonsall and Michael Bell, Chaptea 2, pp. 11-40, VNU Science Press BV, Utrecht, The Netherlands.

- Davies, Peter (1989), "Testing and Appraisal of Automatic Vehicle Identification Systems", Selected Proceedings of the Fifth World Conference on Tnsport Research: Transport PoQio. Manag= & Technology Towards 2001, Yokohama, Japan, July 10-14, 1989, Vol. 11, pp. B355-B363.

- De Lozier, Edward J., (1990), "How and Why AVI?" paper presented at the Intenaional Bridge, Tunnel and Turnpike Authority International Symposium on AVI Technology for Toll Collection, New York, New York, June 17-20, 1990, pp. 1-8.

- Departent of the Environment (1976), "A Study of Some Methods of Traffic Restraint: Summary Report," Research Report No. 15, Crown, United Kingdom, pp. 1-20.

- Department of Transport (1988), "Autoguide, Pilot Stage Proposals: A Consultation Document," March, United Kingdom, 38 pages.

- Downs, Anthony (1962), *The Law of Peak-Hour Expressway Congestion," I5affia1y, Vol. 16, July, pp. 393-409.

- Esand, Gunnar S. (1992), "Attacking Air Pollution in Mexico City," Finance and elooment, Vol. 29, No. 4, December, pp. 28-30.

- Foote, Robert S. (1981), "Prospects for Non-Stop Toll Collection Using Automatic Vehicle Identification,' TransQz1azkon Qualy, Vol. 35, NTo. 3, July, pp. 445-460.

- Greater London Council (1974), Suilem*ntary Licensing, Greater London Council, London. Hackman, Lonnie, and Norene D. Martin (1969), The Parking Industy: Private Enterprse for the Public Good, National Parldng Association, Inc., Washington, D.C.

- Hanks, Jr., James W., and Timothy J. Lomax (1990), "Roadway Congestion in Major Urbanized Areas: 1982 to 1988," Texas Transportation Institute, under the sponsorship of the Texas Department of Transportation, Research Report 1131-3, July, pp. 1-66, with appendices.

- Hanks, Jr., James W., and Timothy J. Lomax (1992), "1989 Roadway Congestion Estimates and Trends", Texas Tansportation Institute, under the sponsorship of the Texas Department of Transportation, Research Report 11314, July, pp. 1-57.

- Harrion, Bil (1986), "Electronic Road Pricing in Hong Kong: 3. Estimating and Evaluating the Effects", Iraffic Engn nand Control, Vol. 27, January, pp. 13-18.
incentives also reverberate into the structure of the corporate sector. Allen and Gale (2001) maintain that bank-based financial systems may be more efficient at financing long-term investment projects that require long-term financing.<sup>4</sup> This is due to banks’ ability to renegotiate contracts following the realization of unforeseen contingencies. On the other hand, Dewatripont and Maskin (1995) contend that decentralized financial systems characterized by strong capital markets may be less inclined to roll over financing to inefficient businesses.

Banks’ tendency to evergreen loans is perceived to be particularly acute for some types of banks, such as domestic banks in developing countries. Several studies underscore that foreign banks may partially offset this bias of domestic banks. Similar to dispersed capital market investors, foreign banks establish looser personal relationships with domestic entrepreneurs, so they have less incentives to roll over loans (Mian, 2006). In fact, they typically have a more hierarchical structure than domestic banks, with the top management sitting in a foreign country. By contrast, domestic banks, especially government-controlled ones, have poor cash-flows incentives and are more susceptible to the pressure of domestic governments to keep businesses afloat. Moreover, foreign banks can increase the quality of banking services and promote competition (Levine, 2004; Dobson, 2008) and can facilitate trade by helping firms overcome information asymmetries (Classens et al., 2017).

# **3 Data and Measurement**

In this section, we provide details on the empirical methodology, data sources and measurement of the variables used in the empirical analysis.

## **3.1 Data sources and empirical strategy**

Our data on exporters’ size, distribution and dynamics aggregated at the industry level are drawn from the World Bank Exporter Dynamics Database (EDD). The data cover 62 countries across all geographic regions for the 1997-2014 period. While countries of all income levels are included, the bulk of the sample consists of middle- and low-income countries.<sup>5</sup> The database contains a large number of measures spanning from the number of exporting firms, to the entry and exit rates of firms into export, to the concentration of the export sector (e.g., the share of exports of the top 5% exporters). The measures in the

> 4Banks can rely on relationship lending and collect qualitative information via personal interactions between loan officers and firm managers and access to detailed documents of the firms (Berger and Udell, 2006).

> 5The data have a good coverage for middle- and low-income countries while information for advanced economies is more porous. For a more detailed description of the data, please refer to Fernandes et al. (2015) and http://microdata.worldbank.org/index.php/catalog/2545.

6
possible path and drivers of debt in the next few years, a decomposition is also applied for the period 2023-26, using forecasts from the International Monetary Fund’s (IMF’s) April 2024 _World Economic Outlook_ .

# **3.2 Evolution of debt**

Government debt in small states averaged 57 percent of GDP between 2011 and 2023, 10 percentage points higher than in other EMDEs (figure 4). In small states, as in other EMDEs, average government debt has risen markedly since 2011. It was rising even before the pandemic, but there was a sharp acceleration when the pandemic hit (Sirimaneetham 2022). Although the debt-to-GDP ratio has moderated in the last few years, it remains higher than in the pre-pandemic period. The debt buildup reflects persistent fiscal deficits as well as the growth collapse caused by the pandemic.

# **3.3 Widespread rise in government debt**

Between 2011 and 2023, average government debt in small states increased by about 11 percentage points of GDP. Although this increase was smaller than in other EMDEs, the average debt-to-GDP ratio in small states in 2023, at 61 percent, remained higher than in other EMDEs. The debt buildup was also widespread, occurring in about 60 percent of small states. Where it fell, this was often the result of surging revenues from sovereign rents, grants, or other non-tax revenues; in other cases, it reflected debt restructuring linked to IMF-supported policy programs. Reflecting the widespread debt buildup, the proportion of small states saddled with high debt rose from around one-third in 2011 to one-half in 2023; throughout the period it was higher than for other EMDEs.

About two-thirds of the increase in debt between 2011 and 2023 occurred before the pandemic. In 2011-19, government debt rose relative to GDP because of lingering adverse effects of the Global Financial Crisis and a variety of country-specific factors such as natural disasters, increased borrowing to fund large projects, and sharp falls in commodity prices for some commodity exporters. Debt rose markedly in Samoa and Vanuatu following large, damaging storms in 2012 and 2015 respectively. In Eswatini, a drought in 2015 added to fiscal challenges. Debt also rose significantly among metal and oil exporters, including Suriname, around 2015, when falling gold and oil prices curtailed revenues.

At the onset of the pandemic-induced recession, in 2020, government debt in the average small state jumped by 15 percentage points of GDP, much more than in other EMDEs, before moderating by around 12 percentage points by 2023. In 2020, non-grant-reliant small states experienced a particularly large increase in debt. Although the pandemic was the main factor driving debt higher, natural disasters also contributed—including a volcanic eruption in St. Vincent and the Grenadines in 2021.

# **3.4 Deficit-driven government debt buildup**

The sharp increase in the average government debt-to-GDP ratio in small states between 2011 and 8
Estimates from the latest PISA round suggest that on average roughly 30 PISA points are in this sense equivalent to an additional year of school.<sup>13</sup> This would imply that the observed 300 point ��� range in test scores in the data correspond to �10 years of school. A drawback of this �� approach is that it does not control for selection. For example, if high‐ability students are more likely to be in 10<sup>th</sup> grade than 9<sup>th</sup> grade at age 15, then this calculation overstates the return in terms of PISA points to an additional year of school. A conceptually‐related approach is taken by Kaarsens (2014), who exploits the fact that the 1995 TIMSS round was administered to some 3<sup>rd</sup> and 7<sup>th</sup> grade students as well as the usual 4<sup>th</sup> and 8<sup>th</sup> grade students for this program. He finds that a year of school in the US is worth approximately three times as much as in low‐ income countries.

 Evans and Yuan (2017) suggest a related approach which instead looks at slope of the relationship between test score obtained by adults and the number of years of education they obtained as children. The main advantage of this approach is looks at the effect of education on learning over a wider range than just the one‐year comparisons in the PISA approach, and also controls for selection issues. They find that a one standard deviation improvement in Skills Towards Employability and Productivity (STEP) scores – a testing program for adults in developing countries – is equivalent to between 5 and 7 years of school. The difficulty in applying these results in this context is that it is unclear how to map standard deviations of STEP scores into standard deviations of PISA scores. Under the somewhat arbitrary assumption that the standard deviations are the same, their results would imply that the 300 PISA point differences (or 3 standard deviations) observed across countries translate into a very large gap of 15 to 21 years of school.<sup>14</sup>

- A third approach is suggested in World Bank (2017) and developed further in Filmer, Rogers, Angrist, and Sabarwal (2018). They propose scaling actual average school years in a country by the ratio of the country’s test score to a benchmark value of top performance, and explain how that this scaling factor can be justified under the assumption that grade‐learning trajectories are linear through the origin.  A natural benchmark value for top performance is a TIMSS score of 625, which corresponds to the TIMSS “advanced” international benchmark.  Since country‐level average test scores range from around 300 in the worst‐performing countries to 600 in the best‐

> 13 See OECD (2016), p. 64.

> 14 A similar exercise is done in Hanushek and Zhang (2009), who use adult test scores for 14 mostly OECD countries and relate them to years of educational attainment of the test takers.

32
# **Concluding Remarks**

New datasets have been created in recent years that hold promise to understand better the phenomenon of corruption. Yet, the nature of the beast makes measurement hard. The obfuscating strategies of the corrupt create, rationally, identification problems. By juxtaposing different data sets, we may be able to generate intriguing and plausible results as we tried to show. However, for policymaking the existing measures of corruption illuminate weakly.

That does not mean we should stop measurement attempts and related analysis. But perhaps we should be clearer about “measurement philosophy” for indicators, for example, the CPI. Measurement is part of the scientific endeavor. It is also a communication tool. Corruption is clearly an important feature of life in many countries. Dealing with it is important. It is a secretive business. Its very success requires obfuscating reality. The analysts in of Transparency International, for example, who created the CPI were aware of all the potential pitfalls of an indicator. But they went ahead and found out that simple rankings communicate. They focus attention; they generate activity from research to policy.

Consumers of the information know that it is hard to observe corruption directly. They are also aware that rankings are imperfect. Keeping the story simple may allow readers to have better judgment than a complex story, for example, about the derivation of confidence intervals that adds a layer of confusion rather than precision.

Communicating is one thing. Making changes another. That requires a view on policy. Existing measures of corruption yield little for policy. In our exploration, the data we fall back on to arrive at a basic policy stance are data about prices, about access, about the rules on the books. Making regulations simpler and emphasizing commercial principles for infrastructure ventures are hardly new ideas. Yet, consideration of the mechanisms that drive corruption strengthens the arguments, if anything.

Moving policy in this direction brings new dilemmas. For example, if one brings more commercial discipline through privatization, it may lead to public dissatisfaction, because it allows both better service and more (absolute) corruption that can be seen more easily when measures such as prices and profits exist (Martimort and Straub, 2007). There may not actually be a corrupt payment out of profit, but people may, sometimes correctly, believe that powerful people obtained shares in a company without paying a fair price.

At the same time, getting systems to change usually needs some willingness of powerful people to change as well. To some degree (selfish) interest in better policy comes from the interest of the elites to benefit more. For that one needs better policy that raises productivity. If one believes corruption is a major issue, one needs to confront such dilemmas. They cannot be wished away.

16