rafmacalaba/datause-encoder-data huggingface dataset:
Second, the **net interest margin** equals the accounting value of a bank’s net interest revenue as a share of its total earning assets, while **overhead cost** equals the accounting value of a bank’s overhead costs as share of its total assets. Unlike the previous banking system indicators, these two variables are constructed from raw bank-level data from the BankScope database; both measures are constructed as unweighted averages across all banks of a country for a given year.<sup>6</sup> Higher levels of net interest margins and overhead costs indicate lower levels of banking efficiency, as banks incur higher costs and there is a higher wedge between lending and deposit interest rates.<sup>7</sup> Poorer countries have typically higher net interest margins and overhead costs (Figure 8). Net interest margins have shown a decreasing trend over time in the median country, from 4.4% in 1995 to 3.1% in 2007, while overhead costs first increased then rapidly decreased after 2002 (Figure 9). There are different patterns across different income groups; while net interest margins have been low and relatively stable in high-income countries, with somewhat of a slight decrease in the last years, there has been a significant downward trend in net interest margins in upper-middle countries. Net interest margins in the median low and lower-middle income countries, on the other hand, have shown a decreasing trend only in recent years (Figure 10). Overhead costs have shown a decreasing trend across all income groups (graphs not reported).

The final indicator of banking efficiency is the **cost-income ratio** that measures the overhead costs relative to gross revenues, with higher ratios thus indicating lower levels of cost efficiency. As in the case of net interest margins and overhead costs, data on cost-income ratios are based on banklevel data. Banks in richer countries have typically lower cost-income ratios (Figure 8). There has not been much change in the cost-income ratio over time (Figure 9).

We also include an indicator of banking market structure; **Concentration** equals the ratio of the three largest banks’ assets to total banking sector assets. This indicator is based on bank-level data from BankScope, which raises measurement concerns. Since the bank coverage is not 100% in Bankscope, variation across countries and time might be driven by differences in coverage rather than differences in market structure. There is no clear correlation between concentration and income levels

> 6 Unfortunately the coverage of Bankscope is less than 100% of most countries’ banking sector. This poses relatively few problems in the case of the efficiency measures, but more so in the case of the measures of market structure, as discussed below.

> 7 We denote with spreads the difference between ex-ante contracted loan and deposit interest rates, while margins are the actually received interest (and non-interest) revenue on loans minus the interest costs on deposits (minus non-interest

8
# **3.2. How Were Cost Recovery and Financial Viability Reflected in the ‘Standard Model’?**

17. Cost recovery was and remains closely linked to broader normative conceptions about power sector reform since the debate emerged in the 1980s and 1990s (the ‘Standard Model’). At the center of the reform agenda was the policy of unbundling state-owned monopoly utility companies to allow for private entry and competition, the benefits of which would accrue not only to the owners of the private capital, but ultimately also to consumers.

**Figure 1: Stylized model of reform thinking under the ‘Standard Model’**

<!-- Start of picture text --> Price formation<br>• 1 st best: Competitive market<br>• 2 nd best: Set by regulatory agency<br>Sector outcomes<br>Cost recovery New investment • Security of supply<br>Cost-recovery prices • Reasonable return Enables ... • Generation Enables ... • Quality of service<br>Guides on new investment • Transmission • Affordability*<br>Cost optimization / efficient operation • Cost-recovery in ongoing operations • Distribution • Environmental sustainability<br>• Fiscal affordability<br>Utility governance & decisionmaking<br>• 1 st best: Privatized utilities<br>• 2 nd best: Corporatized SOE(s) as off-<br>taker of private investment<br><!-- End of picture text --> _Source:_ World Bank staff.

18. With regard to cost recovery, the stylized reform model had three underlying (and often implicit) hypotheses (illustrated in Figure 1):

1. **Price formation:** Cost recovery is most likely in liberalized/competitive markets for electricity, and more likely in countries with regulatory agency than in countries with government-determined prices;

2. **Utility governance & decision-making:** Cost recovery is most likely in countries with privately-owned utilities and more likely in countries with corporatized SOEs than in cases where the utility remained part of the administrative structure of the government; 3. **Outcomes:** Higher cost recovery is associated with higher levels of private investment in the sector, which in turn is associated with better sector outcomes (security of supply and quality of service).

7
a) <!-- Start of picture text --> Food Crops (billion tons) Difference between Stochastic and Deterministic Solutions<br>9<br>Range of all sample paths<br>10% quantile<br>8.8 0.5 25% quantile50% quantile<br>8.6<br>8.4 0<br>8.2<br>8 −0.5<br>7.8<br>7.6 −1<br>7.4<br>7.2 −1.5<br>7<br>−2<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2010 2020 2030 2040 2050 2060 2070 2080 2090<br>Year Year<br>b)<br>Biofuels (million tonnes of oil equivalent) Difference between Stochastic and Deterministic Solutions<br>0<br>60<br>−5<br>50 −10<br>−15<br>40<br>−20<br>30 −25<br>−30<br>Range of all sample paths<br>20 10% quantile<br>−35 25% quantile<br>50% quantile<br>10 −40<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2010 2020 2030 2040 2050 2060 2070 2080 2090<br>Year Year<br>billion tons<br>million tonnes of oil equivalent<br><!-- End of picture text --> 25
Because the more nationally-representative Enterprise Survey samples we use include a larger number of small firms, and because we find important differences in some cases between micro and other small firms, we use our four-category size classifications in the remaining tests reported below.

# **3.2. Financial Development, Institutions, and Access to External Finance**

How do financial development and institutions affect firms’ access to external finance? Some preliminary answers to this question are provided in Table 6, in which we include firm-level controls, country-level macro characteristics, and one of the following: Private Credit, Value Traded, and various measures of contracting or property rights institutions. All macro variables and the financialdevelopment and institutional measures are lagged by one period. To conserve space, the coefficients of other macro control variables are reported only for the first set of results, in Panel A of Table 6 where the key independent variables are measures of financial development and contracting institutions. Coefficients of firm-level controls are not reported to conserve space, but they remain similar to those reported in Table 5.

Financial development in general is not associated with better access to external finance at the firm level. Bank credit as a percentage of GDP (i.e., Private Credit), the standard indicator of financial development, does not have a positive and significant association with external finance either for working capital or for fixed asset investment. In fact, the associations are negative and significant. Nor do we find a positive effect of stock market development (i.e., value traded/GDP) on external financing for firms—the associations are again negative, and more pronounced than in the case of Private Credit. There is no reason, therefore, to conclude that financial development (or the sheer size of the formal finance sector) implies better access to external finance for firms overall.

The quality of contracting institutions also has no significant association with access to external finance for firms overall. CPIA Legal Framework, the _de jure_ measure of the legal system, is unrelated to the share of finance from external sources either for working capital or investment in fixed assets. Turning to the _de facto_ measure of the legal system, CPIA Legal System similarly has no significant association with firms’ access to external finance. The measure used by AJ (2005), Contracting Procedures (from Doing Business), again exhibits no significant association. Examining the information environment, WEF Auditing Quality, a specific ingredient of contracting institutions, we once more find no significant associations. Finally, turning to Creditors Rights, a contracting 18
|Table4.<br>|5: Operati<br>|ngExpen<br>|ses,200<br>|1-2006<br>|||
|---|---|---|---|---|---|---|
|basis points|2001|2002|2003|2004|2005|2006|
|External Management Fees||7.3|14.1|15.5|15.9|14.6|
|Commission Costs|1.4|3.6|3.2|2.1|3.3|2.8|
|Total NPRF Costs|1.4|10.9|17.3|17.6|19.2|17.4|
|NTMA Expenses|2.7|3.1|3.9|3.3|3.5|3.4|
|TotalOperating Costs|4.1|14.0|21.2|20.9|22.7|20.8|

Source: NPRF The NTMA employs 17 people to run the operations of the fund. The Commission does not have any staff but incurs all expenses related to IT systems, custodians, consultants, legal fees and the fees and expenses of commissioners.

# **4.7 Concluding Remarks**

The NPRF has been created with a strong governance structure. It is managed by a small independent Commission of experts rather than representatives of stakeholders. The Commission has absolute discretion to control, manage and invest the assets of the fund, but is required to consult with the Minister and obtain his or her consent in the appointment of the manager. The appointment of the NTMA as first manager for ten years was required by the Act that created the fund.

The Commission is responsible for formulating the investment policy objectives and setting the strategic asset allocation of the fund. Taking into account its long investment horizon, it has set a broad allocation of 80 percent equities and 20 percent bonds. The eurozone market was chosen as the domestic market. Thus, investments in Irish equities account for less than one percent of assets, while the fund is not allowed to invest in Irish government securities. The equity portfolio is equally divided between Eurozone and global equities, while half of the foreign currency exposure is hedged.

Active management is used for half the portfolio. External asset managers are used for specialized mandates. The Commission also appointed a transition manager and a global custodian. It participates in both securities lending and recapture of broker commissions. The asset allocation strategy has been refined over time, increasing allocations to emerging markets (5 percent) as well as alternative assets (20 percent). However, the Commission has not advocated engaging in either principal investing or short-term trading.

54
# **Table 8: Descriptive OLS Regression of Views on the Current Environment and Migration**

|||**OLS**<br>|**: Current Migr**<br>|**ant= 1, No Mi**<br>|**grant= 0**<br>||
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Rising Prices|0.048***<br>(0.010)||||||
|Optimistic Country Direction||0.036***<br>(0.006)|||||
|Cannot Afford Enough Food|||0.052**<br>(0.025)||||
|Worse Fin. Sit. this Year||||0.095***<br>(0.033)|||
|Worried about Losing Job|||||0.075***<br>(0.014)||
|No Restrict Movement||||||0.048***<br>(0.009)|
|Constant|0.030***|0.025***|0.029***|0.029***|0.022***|0.015***|
||(0.004)|(0.004)|(0.005)|(0.005)|(0.005)|(0.005)|
|Number of observations|12,137|12,137|12,137|12,137|12,137|12,137|
|Calendar Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|R2|0.012|0.012|0.006|0.008|0.017|0.014|
|Adjusted R2|0.011|0.011|0.006|0.007|0.016|0.013|

note: .01 - ***; .05 - **; .1 - *; 20
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21
Figure 6. Estimated distribution of insurance payouts <!-- Start of picture text --> .85 .9 .95 1<br>Payout rank<br>1000<br>800<br>600<br>400<br>Payout amount (Rs.)<br>200<br>0<br><!-- End of picture text --> Note: Figure plots payout amount against payout rank, sorting all putative payouts in increasing order of size. Source: Giné et al. (2007).
- Landlocked dummy: This dummy is equal to one if the state does not have a sea coast.

- Percent indigenous: Percentage of the population that is indigenous for all the countries with the exception of (i) Mexico: the percentage of the population speaking an indigenous language and (ii) Peru: the percentage of indigenous or black (not only indigenous) since they are not reported separately.

- Percent black: Percentage of the population that is black (exists only for Brazil, Colombia, Ecuador, Uruguay, and the US)

- Percent indigenous or black: The sum of the pervious two variables

# 9 Appendix B: Pre-Colonial Population Density

This appendix describes in detail how we construct the pre-colonial population density variable. We use data from several sources to estimate pre-colonial population density at the state level. The main sources of information are region-speci�c chapters in Denevan (1992) and references cited in that book. This section presents the main sources for each country and explains the assumptions we used to impute population estimates for the di�erent regions of each country. In each case we adjust the estimated size of the native population in each country to match the numbers presented in Denevan (1992, Table 00.1). Appendix Table 2 lists our pre-colonial population estimates for each region.

## 9.1 Argentina

The only source of information we use corresponds to Pyle (1992), a chapter in Denevan (1992). This paper includes several estimates of the native population for di�erent regions of Argentina. We take the average of the number of natives in each region as our estimate of the denominator. In addition, using maps from the same paper, we allocate di�erent tribes or groups to the di�erent modern states. As some of the Argentinean regions identi�ed in Pyle (1992) correspond to clusters of more than one modern Argentinean states, we estimated population density for the regions presented in Pyle (1992) and we impute the same population density for all the states in the same region. In particular, the regions that include more than one state are: (i) Buenos Aires and Capital Federal, (ii) Chubut, La Pampa, Neuqu�en, R��o Negro, Santa Cruz, and Tierra del Fuego.

27
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 7

**Table 2.** Measures of metropolitan coordination.

|**Representation**|**Fragmentation Index (metro level)**|**Authors**|
|---|---|---|
|I. Governance|Governance Body<br>Age of the metropolitan area<br>Number of municipalities with the same political party|Ahrend et al. (2014)<br>Nelson and Foster (1999)<br>Pradenas (2006)|
|II. Land use plan<br>and mobility|Percent of municipalities covered by integrated transport<br>systems between municipalities and central city|Kim et al. (2014)|
|III. Coordination<br>for special<br>purpose|Percent of special purpose to general purpose<br>governments<br>Number of general purpose units<br>Number of special purpose units|Hendrick and Shi (2015)<br>Goodman (1980)<br>Goodman (1980)|
|IV. Functional<br>|Percent of people working in the central city|Feria and Susino (2005)|
|region|Number of commuting from the municipalities to the<br>central city|De Esteban (2009).|
||Percent of student population that go to the central city<br>Percent resident-job in central city|Pradenas (2006)<br>Pradenas (2006)|
||Number of telephone calls per month from the<br>municipality to the central city must be four times<br>greater.|Pradenas (2006)|

# **3 The Model**

To estimate the relationship between institutional fragmentation and economic performance of cities, measured through city productivity, we follow the two-step empirical approach devised by Ahrend et al. (2014). The authors warn about the importance of accounting for individual sorting of highly skilled individuals into cities when estimating productivity differentials across urban areas (Combes et al., 2011). This is necessary in order to account for the tendency of more talented individuals to colocate in cities that may lead to confounding agglomeration benefits with productivity increases from a more skilled workforce. Thus, in the first step, we use data from the Defense Meteorological Satellite Programs – Operational Linescan System (DSMP-OLS) nighttime lights (NTL) imagery to identify urban areas as well as survey-based micro-data for the period 2000-2014 to estimate productivity differentials across urban areas, net of individual and employment characteristics observables. The estimation on this first stage is then:
**Figure 5: Total job content of exports as a share of total employment 2004-2011**

<!-- Start of picture text --> 1995 2000 2005 2010<br>Year<br>ZAF BRA RUS COL<br>PER THA TUR<br>50<br>40<br>30<br>20<br>Export jobs in total employment (%)<br>10<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

# **4. Labor and Jobs Content of Exports across Sectors**

# **a. Within South Africa**

The aggregate trends mask a wide heterogeneity across sectors. In particular, we show in this section how commodity exports versus manufacturing and services exports contribute differently to jobs and wages, which depends on whether we consider the direct versus indirect content as well as the forward versus backward linkages. A sectoral perspective also shows how the composition of South Africa’s exports are driving the above aggregate results.

Natural resource sectors – which tend to be capital intensive – generate most of South Africa’s labor value added contained in its exports. Figure 6 shows that in 2011 the leading export sectors in terms of total labor content were minerals, metals, machinery and equipment, and ferrous metals. Minerals contributed over $6 billion in wages, by far the largest contributor to wages among export sectors, reflecting the large value of gross exports and a relatively high – and growing - labor intensity of exports vis-à-vis other sectors (more on this below). Together these four sectors contributed 51% of South Africa’s total labor contained in its exports and 52% of South Africa’s gross exports.

Consistent with its enclave-type characteristics, the minerals sector primarily generates domestic labor value added directly within the sector’s exports, as opposed to its backward linkages with input sectors. On the other hand, around three-quarters of the total labor contained in exports of machinery and equipment and of transport equipment is due to labor used by domestic suppliers 13
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