rafmacalaba/datause-encoder-data huggingface dataset:
# **Box 1. Special tariffs for large electricity users: The case of Zambia**

The average effective power tariff in Zambia, at 3 cents/kWh, is one of the lowest in Africa. This current level does not even allow for the recovery of operating costs, yet alone total costs—even though Zambia has one of the lowest average costs in the region (due to a combination of hydropower technologies and excess generation capacity).

Such inefficient pricing policies are compounded by the exceptionally favorable prices that the power utility ZESCO gives to mining companies, in particular the Copperbelt Energy Corporation (CEC). A long-term agreement set mining tariffs at 2 cents/kWh, not only below cost recovery but also one-third lower than the effective tariff for an average residential customer (100 kWh/month).

As the mining sector is the recipient of 50 percent of total ZESCO sales, this translates into a conservative estimate of $30 million in annual subsidies with a projected cumulative deficit of $926 million over the next 10 years.

Zambia’s case is not unique in the region. Until 2003 Ghana’s power distribution company, VRA, was engaged in a long-term agreement with Volta Aluminum Company, which was VRA’s most important customer, consuming onethird of its power generation and benefiting from a preferential electricity price estimated to be half of the costrecovery level.

_Source:_ Zambia Electricity Regulator Board 2008; World Bank 2008; Chivakul and York 2006 Table 7 lists the effective nonresidential tariffs for select African countries. Commercial effective tariffs are higher overall than industrial ones at similar levels of consumption. Two-thirds of the sample countries have commercial tariffs that are, on average, 20–30 percent higher than the industrial ones (table 7). These countries include Burkina Faso, Cape Verde, Chad, Madagascar, and Ethiopia. Another handful of countries have even higher price differentials—Benin, Côte d’Ivoire, and South Africa have commercial effective tariffs between 40 and 77 percent higher than industrial tariffs. This pattern is not unusual, as the genuine production costs of the high-voltage power consumed by industrial users are lower and exposed to fewer transmission and distribution losses (figure 8). The Democratic Republic of Congo and Mozambique are notable exceptions to this pattern: commercial customers pay on average 30–40 percent more than industrial customers.

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Table D14: Ethnicity randomization checks, 2011 and after, 3

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Masai|Judge plur. Meru|Judge plur. Mijikenda|
|Pla. plur. Masai|-0.000695<br>(0.00261)|||
|Def. plur. Masai|0.000584<br>(0.000898)|||
|Pla. plur. Meru||0.00183<br>(0.00265)||
|Def. plur. Meru||0.00195<br>(0.00287)||
|Pla. plur. Mijikenda|||0.00181<br>(0.00255)|
|Def. maj. Mijikenda|||0.00498<br>(0.00480)|
|DV mean|0.00718|0.0171|0.00946|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|11838|11838|11838|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 2011-2020. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

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developed and developing countries and found no significant long-term effects in developed countries; he came to the conclusion that in developing countries aggregate effects fade away after two years, but that some negative effects on income distribution and equality persist. Overall, Albala-Bertrand considered disasters "a problem _of_ development, but essentially not a problem _for_ development." (Albala-Bertrand 1993). According to his analysis, while the number of deaths and people affected and the extent of monetary losses are determined by the current state of a country's development, disasters do not normally hinder long-term development, with the sole exception being widespread droughts.<sup>3</sup> Further, Caselli and Malhotra (2004) based their analysis on neoclassical growth theory and analyzed the losses in relation to country growth rates after disaster events using a dataset of 172 countries for events between 1975 and 1996. They concluded that their hypothesis that losses of labor and capital stock have no effect on short-term economic growth could not be rejected. Finally, Skidmore and Toya (2002) discovered a robust positive correlation between the frequency of natural disasters and long-run economic growth after conditioning for other determinants, which they explain by some type of Schumpeterian creative destruction.

Overall, while the balance of evidence and studies seems to imply that there are adverse economic disaster effects in terms of the “negative” trajectory stylized above, there are important “outliers” that merit more investigation. Another observation is that the studies generally have a short-term focus, and in their analyses often do not go beyond the year following an event. Finally, analyses generally compare key indicators of interest after the fact to their pre-disaster states, rather than comparing the counterfactual, i.e. the system without a shock, to the observed. The latter point seems important, as important opportunity costs, e.g. in terms of economic growth foregone, are consequently often not accounted for in analyses on the macro effects of disasters.

> 3 Albala-Bertrand (1993) started fruitful discussions about some assumptions and estimating issues in the literature, and his findings were discussed and replicated by various other authors including Mechler (2004) and Hochrainer (2006). For example, Hochrainer (2006) extended Albala-Bertrand’s sample to 85 disaster events in 45 countries and found GDP growth (on average) negatively affected in the disaster year and no significant increases in growth for the subsequent post-disaster years, which implies that, due to a lack of recovery, a net loss of GDP.

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After concluding, we provide an extensive Appendix with additional details on the data and the model.

# **2 Data and Summary Statistics**

Our empirical work is based on merging three large datasets at the firm level. First, we construct a novel dataset on Spanish public procurement contracts published by the official bulletin of the Spanish Central Government ( _Bolet ́ın Oficial del Estado_ , BOE) over the 2000-2013 period. We have information on the type of good or service provided, the institution awarding the contract, the initial bidding and final price of the contract, the type of procedure used to allocate the contract, and the firm(s) that won the contract. Second, we use more standard firm-level data on balance sheets and income statements of the quasi-universe of Spanish companies between 2000 and 2016, a dataset that is maintained by the Banco de Espa ̃na and taken from the Spanish Commercial Registry. And third, we use credit register data for Spain, which contain detailed information on all outstanding loans over 6,000 euros to non-financial firms granted by all banks operating in Spain (including whether or not a non-personal collateral was posted on a particular loan). Additionally, the credit register data set contains rich information on loan applications. Appendix A provides details about the different data sources and samples that we use.

**Main sample.** We merge the three data sets to build a panel of firms at the annual frequency (which is the frequency of observation of our firm-level data) with detailed information on credit and procurement activity. For our sample we only keep those firms that get at least one procurement project over the sample period.<sup>9</sup> This leaves 12,000 unique firms observed for an average of 12 years. Alternatively, in the Appendix, we also build a sample at a quarterly frequency (that delivers a tighter identification of the effects of procurement on credit growth but cannot be used to examine the effects of procurement on firm sales) and a smaller sample for those projects that have data on the full ranking of bidders.

**Procurement vs. non-procurement firms.** We find that firms participating in procurement are significantly larger and older on average, but there is considerable overlap in the support of the size and age distribution for procurement and non-procurement firms (see Table A.3 in the Appendix). For example, the average number of employees of a procurement firm is around 6 times larger than for the rest of the firms (73.56 vs. 12.75), total sales are 7 times larger (8.9 millions of euro vs. around 1.2 million), and procurement firms are 9 years older (20 vs 11 years). Yet, around 25% of procurement firms have less than 16 employees, have revenues that are lower than 1.1 million euro, and are 12 or fewer years old. We also find that conditional on having at least one procurement project, there is a lot of variation on the importance of these projects as a fraction

> 9Note that because all our empirical work uses firm-level fixed effects, the relevant variation is within-firm variation for those firms that get at least one procurement project in the sample period.

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- 12 -

**Figure 5: Relation between school life expectancy and grade 1 intake rate and the per capita** GNP, **circa 1993**

<!-- Start of picture text --> (A) Overall coverage (B) Entry rate to grade I<br>16 ~ o 0 >°<br>~~ 0 00 ~~~5 0 o%o~~~ 0 0 10o000OD8&gC04<br>C ~v0 0. o~~~~~~~~~~0<br>6 - o 0 o 00 60 0 0<br>0 0~~~~~~~<br>50 4- o 20 0 000 o 0 0- 0<br>2 8 20<br>VI 4 0 0 0~~~~~~~~~~~~~~~III<br>200 400 800 1500 3000 100 20000 200 400 800 1500 3000 10000 20000<br>Per capita GNP (log scale) Per capita G;NP<br><!-- End of picture text --> _Student learning._ The cognitive skills that school leavers bring to the labor force is the other important dimension of educational outcome. 1<sup>3</sup> For our purpose we amalgamate test scores from several rounds of international mathematics and science tests administered to 9-14 year old children in the 1990s.14 The resulting indicator of achievement is calibrated to range between 0 and 100 percent, and is interpreted here as a summary measure of the achievement of children around the end of primary education and the beginning of lower secondary education.

Figure 6 shows the relation between the indicator and per capita GNP in 1993. Predictably, test scores are generally higher among children in richer than poorer countries. The relation is relatively weak, however, with a regression R<sup>2</sup> statistic of only 0.17, implying substantial variation among countries. It therefore appears that differences in student learning is not just a function of country wealth, but is probably also the result of differences in policy choices that ultimately affect the effectiveness of education processes within the classroom.

> 3 For examples of studies on the relation between worers' cognitive sklfls and work productivity, see Murnane, Richard, J. John B. Willet, andFrank Levy (1994). 'Ihe growng impn tance of cognitive skills in wage determination," Harvard Graduate School of Education (rnimeo); and Bishop, John (1991) "Achievemen **t** , est scores, and relative wages," in Marvin H. Kosters (ed.) Workers and Their Wages, Washington D.C.: The AEI Press.

> 4 The raw data pertain to some 53 countres, and come from the various international science and mathematics tests (e.g. the Third International Mathematics and Science Study) and conveniently reported in Jong-Wha Lee and Robert J. Banro, 1997 "School quality in a cross-section of countries" National Bureau of Economic Research, Working paper no. 6198, Cambridge, Mass. Not all countries patcipated in all the tests We used regression estimates relating one test score to another to fill the gaps, and to produce a single test score incoporating all the available information. The procedure produced data on 44 observations.
indicator (being calculated in nominal terms), is sensitive to short-run fluctuations in world commodity prices, whereas the average effective tariff rate does not capture non-tariff barriers--information on which is highly unreliable. Similarly, the ratio of FDI to GDP may show large fluctuations in specific years, reflecting specific operations, such as the privatization of a large public enterprise. To mitigate or "smooth out" the impact of year-on-year fluctuations in terms-of-trade changes on extemal openness and year-on-year fluctuations in FDI flows, I use in the regressions below averages over two and three years, in addition to the oneyear lagged value. I suspect that the "smoothing" effect associated with a threeyear average may be excessive and impart some bias in assessing the relationship between globalization indicators and poverty; nonetheless, I will report them for comparative purposes.

In addition to the problem of finding adequate indicators of trade and financial openness, there is a major data constraint relative to poverty rates and some of the other control variables defined earlier. I first started by compiling all the data on developing countries available on the poverty headcount index contained in the World Bank Live Database (LDB), which cover the period from the late 1980s to the late 1990s. This gives a sample of 57 countries, and a total number of observations equal to, at most, 91.17 However, due to the lack of available data on some of the control variables or globalization indicators (most notably government transfers as a share of GDP, average tariffs, and FDI), the "actual" sample size is at most 52 observations (for a total of 31 countries) in the initial set of regressions. After excluding the countries for which the number of observations is equal to one (Algeria, Brazil, Ecuador, Egypt, Ghana, Haiti, Kenya, Madagascar, Malawi, Malaysia, Mauritius, Nicaragua, Nigeria, Pakistan, Paraguay, Rwanda, Venezuela, Zimbabwe); I end up with a sample of 11 countries and at most 30 observations--6 countries for which the number of

> 17As is well known, there are serious conceptual and practical issues associated with the measurement of poverty, and the use of an 'international poverty line" for cross-country comparisons (see, for instance, Deaton (2001)). I will return to these measurement Issues in the concluding section.

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10 Party age itself could be evidence of competence and credibility. Incompetent parties are less likely to perform well, making them more vulnerable to electoral defeat and, ultimately, to extinction. Politicians in older parties therefore benefit less from spending around elections than those from younger parties, since voters are less certain about the competence of candidates from younger parties.<sup>5</sup> This argument is entirely consistent with the idea that party organization is likely to differ between older and younger parties, however, since candidate competence is not an exogenous attribute of political competitors. It requires a conscious decision by party leaders to promote competence within the organization and to select candidates on the basis of competence.<sup>6</sup> It is possible that parties have a history of good performance for reasons unrelated to politician competence. However, this is unlikely to create a spurious negative association between party age and political budget cycles. For example, parties could have experienced positive shocks during their time in office. In that case, older parties are simply luckier, but otherwise no different than younger parties. To the extent that this is true, it creates a bias against finding that party age has a negative effect on political budget cycles. On the contrary, the opposite is more plausible: older, luckier parties, benefiting from positive economic shocks, are able to spend more around elections relative to younger parties. The results below, however, are exactly contrary to this.

The next section of the paper discusses the data used in the analysis, particularly the party measures and the public spending data. We then present extensive evidence that political budget cycles are more pronounced in the presence of younger political parties. These results are robust to controlling for the effects of democratic age or income on cycles. It is not the case that a spurious

> 5 Political budget cycles themselves could be regarded as indicators of poor performance, creating a tautology:  older parties are those that have not engaged in political budget cycles.  However, the literature on political budget cycles, especially Rogoff and Sibert (1988) and Rogoff (1990), makes precisely the opposite assumption:  spending around elections is an indicator of competence – of good performance – not of incompetence.

> 6 Keefer and Vlaicu (2008), for example, explicitly model the tradeoffs that politicians make when they invest in party credibility.
spread to Central Asia and Europe in the thirteenth century and, following transplantation by the Spanish conquistadors and Pilgrims, into North and South America in the seventeenth century (Herer 1998). This points to a fifth driver of drug cultivation – the use of these plants in early bartering and financial systems, the Spanish for example transformed coca leaves into one of the most highly commercialized products in the Andes by using coca as means of payment.

Relaxation, recreation and experimentation were the final factor accounting for the popularity of drug use. However, in both ancient and modern societies this was the preserve of the elite. The synthetic drug revolution in the second half of the nineteenth century did see an increase in recreational drug experimentation, but this remained confined to bohemian groups, literary and artistic figures and secret societies, who transformed non-medical drug use into a ‘social signifier’ of rejection of mainstream society values (Keire 1998). The invention of the injecting syringe in 1843 did create new recreational as well as medical markets for cocaine and opiates, the 1890s Sears Roebuck catalogue for example offering a syringe and vial of cocaine for $1.50.

A significant exception to the model of elite recreational use was the Chinese – and broader South East Asian market for opium. Opium consumption in China was common among all social classes and owing to the intensity of demand – and addiction - domestic cultivation had to be reinforced by opium imports from India, Persia and Turkey. Recreational opium smoking was also common among Chinese immigrants scattered across port cities such as London and San Francisco.

# _The Trade in Drugs_

Drug cultivation and use has persisted across time, but there was a dramatic change in patterns of cultivation, production and use during the eighteenth century when opium, and to a lesser extent coca, became commercialized. This was catalyzed by Western efforts to expand their commercial and colonial presence in Asia. A brief assessment of the early opium trade puts into perspective the significance of the U.S. effort to regulate and ultimately eliminate what was one of the most important globally traded commodities in the international market.

Early Portuguese traders were responsible for initiating the ‘mass’ market for opium. They first discovered opium poppy cultivation and opium production in India after their arrival in the country in 1501. As part of early efforts to enter the Chinese 5
for each country over the same period.<sup>15</sup> Each country group is highlighted using different colors. Although there has been a steep increase in the quantity and complexity of regulations, the figure suggests that there was not even a positive, let alone a significant, relationship between growth in bank assets and growth in number of supervisors who oversee these banks for the whole sample.<sup>16</sup> The same was also true for highincome OECD countries and within individual regions.

# **Figure 12: Supervisory capacity**

## **a. Change in number of professional bank supervisors**

**b. Supervisors with post-graduate degrees such as MBAs, CPAs, or CFAs (% of total supervisors)**

## **c. Mean number of hours of on-the-job training**

**d. Bank supervisors with more than 10 years experience in bank supervision (% of total supervisors)**

Source: Waves 4 and 5, BRSS.

Note: CFA = chartered financial analyst; CPA = certified public accountant; MBA = master’s in business administration. DEV are developing countries.

15 Note that in 2014 the ECB assumed its role as single supervisory entity in the Euro Area (18 countries at that time and 19 countries by the end of year 2016). This entailed a transfer of personnel from national central banks (i.e., the bank supervisors) to the ECB. Of the 19 Euro area countries, 17 are high income OECD countries. Therefore, figures for high income OECD countries may underestimate the real labor power of bank supervisory agencies in high income OECD countries. 16 Although not reported here, there is also no relationship between the growth in the number of banks and growth in supervisory personnel.

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