rafmacalaba/datause-encoder-data huggingface dataset:
the incumbent party receive private rents, to the public good versus the private transfer would depend on the relative expected vote returns from the two types of instruments. The incumbent would attempt to find the cheapest possible strategy to win the minimum swing votes needed. The paper examines decentralization as a strategic political instrument in this context.

The first critical assumption we make to build the argument is that the vote return from shifting resources at the margin to targeted transfers to some swing voters is greater than the vote return from increasing the public good, at the preferred public good level of the incumbent. That is, If the incumbent party chooses to seek re-election, it would provide private transfers to swing voters just up to the point where it meets the reelection constraint. The constrained optimal amount of private transfers to swing voters for a government seeking _s_ * * _G s_ * re-election is therefore given by _f_ where _F_ ( _H_ ( _g_ )  _f_ ))   . The government would attempt to finance the provision of these transfers, by diverting tax revenues * _G_ collected through its preferred rate  from rents for its constituents. Assuming * _G_ sufficient revenues, the central politician would continue to keep the tax rate at  , provide _g_<sup>*</sup> _G_ , but would reduce the targeted rents to its constituents to _r j_ ** _G_  _r_ * _G_  _f s_ * .

As mentioned at the outset in the paper, Lizzeri and Persico (2004) use completely opposite assumptions, of the increasing value of the public good to pivotal voters in post-industrial revolution England, to explain how suffrage reforms led to substantial increases in spending by municipal corporations on public health and sanitation infrastructure. The micro evidence on voting behavior in poor countries, however, suggests that small private transfers provided within a _quid pro quo_ arrangement may bring surer vote returns to parties than broad provision of public goods.

Finan and Schechter (2010) explore the role of social preferences such as reciprocity, the psychological pressure to return a favor received from someone, in shaping electoral strategies of vote buying. Combining experimental games that measure the strength of reciprocal feelings among individuals with survey data on vote-buying 13
4 Brennan and Buchanan model the government as a monolithic entity that systematically seeks to maximize its total revenue. According to them, the capacity of government to maximize its revenue is only limited by constitutional constraints, among which is decentralization. Citizen mobility and competition between subnational governments will limit their tax pricing power and encourage more efficient allocation of public services. Consequently, other things being equal, the state should be smaller the more it is decentralized.

The theory of the Leviathan State has limited empirical support. Oates (1985) conducted a study on this relationship using subnational governments’ share of revenue and expenditure as proxies for decentralization. He finds no empirical support for the Leviathan hypothesis. Ehdaie (1994) pointes out a weakness in the Oates (1985) study, arguing that taxing and spending decisions should not be taken separately in the decentralization process. Computing measures of fiscal decentralization and fiscal collusion, he finds that decentralization of taxing power has a negative correlation with the public sector size, while the amount of transfers has no significant correlation. More recently, Jin and Zou (2002), adding the time series dimension to cross-section analysis, find that expenditure decentralization leads to a larger state, while revenue decentralization leads to a smaller state, and finally that vertical imbalances increase the size of the public sector.

_Measurement problem_ . Most of previous studies use GFS data to compute indicators of fiscal decentralization. Most decentralization measures computed with GFS data are defined on the basis of a single aspect of decentralization that is subnational share of aggregate government revenue or expenditure. This gives a limited representation of fiscal decentralization. It does not take into account the subnational governments’ control over tax bases or rates.

Ebel and Yilmaz (2002) use new information from OECD (2001) to estimate models with different degrees of subnational governments’ revenue autonomy. From the most decentralized to the most centralized, elements of fiscal revenue used were tax autonomy, non-tax autonomy, fiscal dependency and tax sharing (see Table A-1 for details). They
service volatility. Separating the management of debt from the management of fiscal and monetary policies can help avoid such conflicts, real or perceived, and can improve policy credibility.

Credible policies are known to produce superior overall outcomes compared to lesscredible policies. For example, a credible monetary policy will be successful in taming inflationary expectations and reduce future uncertainty, which will in turn reduce the risk premium on longer dated domestic currency debt. Lower long-term interest rates are also likely to stimulate economic growth and improve the fiscal position, and help to reduce the fiscal deficit and debt burden.<sup>5</sup> This will further reinforce the credibility of monetary policy. The experiences of New Zealand, Sweden, Denmark, and Ireland since the 1990s show that the implementation of a credible, coherent macroeconomic policy mix has helped generate such virtuous cycle, improving welfare of the society as a whole.

What then are the separate policy objectives and the instruments used in the conduct of debt management, fiscal policy and monetary policy? For debt management, the objective is to ensure that the government’s financing needs and its payment obligations are met at the lowest possible cost over the medium to long run, consistent with a prudent degree of risk.<sup>6</sup> This may be expressed as a numerical target for the stock composition of the debt, referred to as the strategic benchmark or the strategic target. The policy instrument is medium to long-term debt, and the composition is managed through _new_ debt issuance, as well as changing the composition of _existing_ debt through interest rate and exchange rate swaps, debt buybacks and exchange offers. The objective of fiscal policy is to achieve the least distorting budgetary policy that would stabilize output, improve the resource allocation and to manage the distributive effects.<sup>7</sup> Overall target for fiscal policy is typically set for the primary balance. Managing the composition and level of spending and taxes are instruments used to achieve these policy objectives. Finally,

> 5  Provided primary expenditures levels are controlled.

> 6  World Bank and IMF (2001).

> 7  For example, the stated fiscal policy objective in the UK is, over the medium-term, to ensure sound public finances and that spending and taxation impact fairly both within and across generations. The longterm fiscal objective for New Zealand is to operate surplus on average over the economic cycle sufficient to meet the requirements for contributions to the NZ Superannuation Fund and ensure consistency with the debt objective (http://www.treasury.govt.nz/budget2006/fiscalstrategy/04.asp).

6
Figure 8: Percent change in juvenile catch because of the closures policy by whether vessels fished in the potential closure the day before closure announcement

|After annou<br>a|ncement, before closure|b|Closure period<br>1 da<br>c|y after|||
|---|---|---|---|---|---|---|
|1000%|Vessel fished<br>|1000%|1000%||||
|900%|there the<br>day before|900%|900%||||
|800%|No<br>Yes|800%|800%||||
|700%<br>aught||700%|700%||||
|600%<br>niles c||600%|600%||||
|500%<br>n juve||500%|500%||||
|400%<br>hange i||400%|400%||||
|300%<br>cent c||300%|300%||||
|200%<br>Per||200%|200%||||
|100%||100%|100%||||
|0%||0%|0%||||
|−100%||−100%|−100%||||
|Inside<br>10 km|20 km<br>30 km<br>40 km<br>50 km|I|nside<br>10 km<br>20 km<br>30 km<br>40 km<br>50 km<br>Inside<br>10 km<br>20 km|30 km|40 km|50 km|
|d|2 days after|e|3 days after<br>4 day<br>f|s after|||
|1000%|Vessel fished|1000%|1000%||||
||h h||||||
|900%|tere te<br>day before|900%|900%||||
|800%|No<br>Yes|800%|800%||||
|700%<br>ught||700%|700%||||
|600%<br>niles ca||600%|600%||||
|500%<br>n juve||500%|500%||||
|400%<br>hange i||400%|400%||||
|300%<br>rcent c||300%|300%||||
|200%<br>Pe||200%|200%||||
|100%||100%|100%||||
|0%||0%|0%||||
|−100%<br>||−100%<br>|−100%<br>||||
|Inside<br>10 km|20 km<br>30 km<br>40 km<br>50 km|I|nside<br>10 km<br>20 km<br>30 km<br>40 km<br>50 km<br>Inside<br>10 km<br>20 km|30 km|40 km|50 km|

Notes: The six subfigures (a to f) correspond to the six time periods in my treatment window. In each time period, there are six spatial units of interest (x-axis). The points are the treatment effects in percentages and the whiskers are 95% confidence intervals. N = 68,544. Standard errors clustered at level of two-week-of-sample by two-degree grid cell.

42
# **Table 8. Impacts on sectoral CO2 emissions due to the carbon tax (% change from the baseline)**

||Scen|ario 1|||Scen<br>|ario 2<br>|||||Scen<br>|ario 3<br>||||Scen<br>|ario 4<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|or|||Cas|e a|Cas|e c|Cas|e e|Cas|e a|Ca|se c|Cas|e e|Ca|se c|Ca|se e|
|ect||l||l||l||l||l||l||l||l||l|
|S|Formal|Informa|Formal|Informa|Formal|Informa|Formal|Informa|Formal|Informa|Formal|Informa|Formal|Informa|Formal|Informa|Formal|Informa|
|S1|-5.42|-3.73|-3.52|-3.80|-2.62|-3.86|-0.23|-4.39|-4.14|-3.38|-3.60|-3.25|-2.29|-3.72|-3.99|-3.12|-5.36|-4.26|
|S2|-1.68|-1.93|-3.57|-4.27|-4.64|-5.54|-1.79|-3.66|-2.40|-2.74|-2.68|-3.04|-2.03|-3.05|-0.28|-0.45|-1.66|-1.93|
|S3|-6.40|-4.69|-2.74|-4.10|-0.94|-3.84|6.12|-3.81|-3.89|-4.11|-2.80|-3.88|-0.11|-3.80|-5.20|-3.23|-6.87|-4.46|
|S4|-15.9|-14.2|-8.38|-7.72|-4.22|-4.19|-15.6|-14.8|-13.0|-11.6|-11.9|-10.6|-15.7|-14.5|-15.2|-14.0|-16.3|-15.1|
|S5|-2.07|-1.97|-2.54|-2.55|-2.83|-2.89|-1.52|-2.07|-2.05|-1.97|-2.02|-1.95|-1.40|-1.58|-0.59|-0.39|-1.37|-1.08|
|S6|-2.94|-2.40|-1.71|-1.76|-1.31|-1.65|1.54|0.75|-1.67|-1.41|-1.22|-1.07|0.29|0.27|1.59|2.26|0.88|1.67|
|S7|-5.18|-3.99|-3.21|-3.27|-2.27|-2.94|-0.25|-4.12|-3.92|-3.29|-3.40|-3.05|-2.58|-4.07|-3.20|-2.73|-4.42|-3.69|
|S8|-14.7|0.00|-12.8|0.00|-11.9|0.00|-14.8|0.00|-13.1|0.00|-12.4|0.00|-14.7|0.00|-13.4|0.00|-15.2|0.00|
|S9|-2.22|-2.22|-2.20|-2.56|-2.36|-2.87|0.32|-0.92|-1.84|-2.03|-1.71|-1.97|-0.50|-1.17|1.10|0.73|0.30|-0.11|
|S10|-2.94|-1.89|-2.97|-2.91|-3.02|-3.41|-2.42|-2.38|-2.99|-2.42|-3.00|-2.59|-2.74|-2.17|-1.25|-1.08|-2.12|-1.99|
|S11|-3.82|0.00|-3.35|0.00|-3.17|0.00|-3.04|0.00|-3.35|0.00|-3.17|0.00|-3.42|0.00|-1.96|0.00|-2.99|0.00|
|S12|-4.58|0.00|-2.85|0.00|-2.02|0.00|-3.61|0.00|-3.58|0.00|-3.26|0.00|-4.42|0.00|-2.13|0.00|-2.86|0.00|
|S13|-12.5|0.00|-4.16|0.00|0.50|0.00|-12.3|0.00|-9.51|0.00|-8.37|0.00|-12.3|0.00|-12.9|0.00|-13.0|0.00|
|S15|-4.48|-4.51|-3.76|-2.41|-3.41|-1.37|-4.16|-4.85|-3.98|-2.91|-3.78|-2.35|-4.51|-4.80|-4.04|-4.06|-5.25|-4.83|
|S16|-4.65|-5.89|-3.21|-5.59|-2.54|-5.47|-0.31|-6.75|-3.74|-5.40|-3.35|-5.23|-2.20|-6.26|-3.26|-3.55|-4.48|-5.01|
|S17|-4.76|-4.54|-3.72|-3.87|-3.22|-3.56|-3.91|-4.59|-4.00|-3.77|-3.72|-3.48|-4.22|-4.21|-3.35|-3.16|-4.47|-4.27|
|S18|-3.21|-2.02|-3.23|-2.52|-3.25|-2.76|-3.02|-2.93|-3.31|-1.20|-3.33|-0.75|-3.25|-1.23|-2.36|-1.73|-3.30|-2.46|

32
# Evolution of Credit Terms: An Empirical Study of Commercial Bank Lending to Developing Countries

# by Sule Ozler*

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|Empirical Issues|4|
||A. Experience and Spread: A Cursory Look at the Data|4|
||B. Methodology<br>1. Credit Terms in the Eurocurrency Market<br>2. Credit Terms and Experience with the Borrowers|5<br>5<br>8|
||C. Data<br>1. Sources and Discussion of Variables<br>2. The Experience Variable|9<br>9<br>11|
|III.|Results|12|
|IV.|Conclusions|16|
|Footn|otes|18|
|Appen|dix|23|
|Refer|ences|26|
|Figur|es|28|

* I thank Bhagwan Chowdhry, Daniel Cohen, Albert Fishlow, Jeff ry Frieden, Robert Kahn, Richard Kohl, and especially Edward E. Leamer for helpful comments and stimulating conversations. I have also benefitted from comments of the seminar participants at Stanford University, UC Berkeley, UCLA, UC San Diego, USC, Federal Reserve Bank of New York, the World Bank's International Debt Division, and participants at the All UC Economic History Conference. Funding from ISOP at UCLA and from the World Bank's International Debt Division are gratefully acknowledged.
the United States and New Zealand officially publish shipping / transport cost data based on the declarations of the importers for the purpose of taxation<sup>7</sup> .

Estimating trade costs for empirical analysis is challenging, therefore. An empirical compromise has been the “matching method” which uses ratio of the CIF import value divided by FOB export value between the same trading partners, whereas the former is reported from the importing country and the latter, from the exporting country. Limao and Venables (2001) estimate transport costs, or more precisely the “transport cost factors” by applying the method to the Direction of Trade Statistics (DOT), published by the International Monetary Fund (IMF). The authors use estimated transport cost factors as the dependent variable of the regressions to examine various determinants of transport costs, which include an index of infrastructure level. While they appear to obtain a persuasive result, the matching method should require a careful treatment in use. For instance, Hummels and Lugovskyy (2006) analyze the accuracy of the method, comparing the estimates with the officially published import charges statistics of the United States and New Zealand, concluding that the matching method may generate “noisy” information.

# _Determinants of Transport Costs_

Limao and Venables (2001) estimate determinants of transport costs, in particular those related to infrastructure. Their transport cost factor regression has distance, per capita incomes, geographical factors, such as common barriers and island dummies, and the indexes of the levels of infrastructure of various parties, as explanatory variables. Their infrastructure index consists of four items: (i) length of road, (ii) length of paved road, (iii) length of rail, and (iv) telephone main lines per person. These four items are normalized and averaged to construct the infrastructure index of a country. Due to its main interest in transport costs for the geographically landlocked counties, the study tends to be implicit on the port infrastructure. But the regressions of trade costs and bilateral trade amount both include the dummy variable for inlands, partially controlling the effect of sea transport. According to their findings, sea transport is much cheaper than land transport. In contrast, explicit measures of port infrastructure should be necessary in

> 7 A few countries appear to have transport data in cross-section (Hummels and Lugovskyy (2006)).

11
III INTRODUCnTON Table 9 Population and Percentage Distribution by Geographic Region and by Income Group, 1985-2100

|||Po|pulati|on<br>(millio|ns)|||Percent|age of|world p|opulati|on|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Region<br>or|||||||||||||
|jncome<br>group|1985|1990|2000|2025|2050|2100|<br>1985|1990|2000|2025|2050|2100|
|World|4,844|5,285|6,204|8,415|10,03|5<br>11,330|100.0|100.0|100.0|100.|0 100.|0 100.0|
|Less developed<br>(a)|3.666|4.074|4,939|7,078|8,71|6 10.020|<br>75.7|77.1|79.6|84.1|86.9|88.4|
|Moredeveloped(a)|1,179|1,211|1,26|5 1,33|6 1,31|9 1,310|<br>24.3|22.9|20.4|15.9|13.1|11.6|
|Africa|559|<br>651|<br>870|1,58|7 2,27|5 2,962|<br>11.5|12.3|14.0|18.9|22.7|26.1|
|East Africa|231|<br>271|<br>367|<br>700|1,03|4 1,383|<br>4.8|<br>5.1|<br>5.9|<br>8.3|10.3|12.2|
|West Africa|204|<br>238|<br>320|<br>602|<br>873|1,152|<br>4.2|<br>4.5|<br>5.2|<br>7.2|<br>8.7|10.2|
|North Africa|124|<br>142|<br>182|<br>286|<br>368|<br>427|2.6|2.7|2.9-|<br>3.4|3.7|3.8|
|America|667|<br>721|<br>826|1,04|0 1,14|6 1,192|<br>13.8|13.6|13.3|12.4|11.4|10.5|
|Latin<br>America<br>and Caribbe|an 399|<br>441|<br>525|<br>703|<br>814|<br>864|<br>8.2|<br>8.3|<br>8.5|<br>8.4|<br>8.1|<br>7.6|
|Northern<br>America|268|<br>280|<br>302|<br>336|<br>332|<br>328|<br>5.5|<br>5.3|<br>4.9|<br>4.0|<br>3.3|<br>2.9|
|Asia|2.823|3,100|3,664|4,901|5,72|8 6,288|<br>58.3|58.6|59.1|58.2|57.1|55.5|
|East and Southeast<br>Asia|1,65|0 1,781|2,038|2,503|2,71|9 2,822|<br>34.1|33.7|32.8|29.7|27.1|24.9|
|South<br>Asia|1,058|1,186|1,45|4 2,108|2,61|4 2,977|<br>21.8|22.4|23.4|25.1|26.0|26.3|
|Southwest<br>Asia|115|<br>132|<br>172|<br>290|<br>396|<br>490|<br>2.4|<br>2.5|<br>2.8|<br>3.4|<br>3.9|<br>4.3|
|Europe and U.S.S.R.|771|<br>787|<br>813|<br>848|<br>843|<br>842|15.9|14.9|13.1|10.1|8.4|7.4|
|OCeania|25|<br>27|31|<br>39|<br>42|<br>44|0.5|0.5|0.5|0.5|0.4|0.4|
|Income group (b)|||||||||||||
|Low|2,711|3,004|3,627|5,165|6,34|3 7,315|<br>56.0|56.8|58.5|61.4|63.2|64.6|
|Lower-middle|580|<br>648|<br>792|1,14|5 1,41|6 1,609|<br>12.0|12.3|12.8|13.6|14.1|14.2|
|Upper-middle|422|<br>462|<br>545|<br>748|<br>891|<br>989|8.7|8.7|8.8|8.9|8.9|8.7|
|High|767|<br>789|<br>830|<br>884|<br>869|<br>869|15.8|14.9|13.4|10.5|8.7|7.7|
|Nonreportingnonmember|365|<br>382|<br>410|<br>474|<br>515|<br>548|7.5|7.2|6.6|5.6|5.1|4.8|

a. "More developed" cooPrises Europe, U.S.S.R., Northern America (United States and Canada), Australia, New Zealand, and Japan. "Less developed" comprises the rest of the world.

b. Criteria for income groups are given in World Bank 1989. See listing in Exhibit 1.
- 21 -

probit least squares model. It can be interpreted as the particular significance of economies of scale in transmission and distribution works.<sup>21</sup> With the technical evaluation process introduced, fewer firms seem to be allowed to enter; the coefficient of _D_<sup>_TE_</sup> is negative but not statistically significant.

Regarding the impact on the equilibrium bid, the second stage regression reveals that more competition leads to lower procurement costs as expected. However, the exogeneity of _n_ may not be able to be rejected at the conventional significance level. The Hausman exogeneity test statistics is estimated at 0.14. Besides _n_ , Table 6 shows that larger projects, especially in terms of generation and substation capacities, would be more costly. The equilibrium bid would also be higher if bidders are supposed to be assessed technically before the price comparison. This follows natural expectations. It is worth recalling that in this instrumental variable estimation, another potential simultaneous equation associated with auctioneer’s technical consideration decision is ignored.

# **_Quality and bids_**

The treatment effect model more clearly highlights the possible effect of involving technical thresholds on the equilibrium bid. _D_<sup>_TE_</sup> is treated as potentially endogenous in the equation of ln _b_ . It is confirmed that auctioneers would prefer to place more emphasis on technical assessment for large electricity generation and substation projects.22 And this quality consideration will bring about additional procurement costs; the coefficient of _D_<sup>_TE_</sup> is estimated at 0.709, which is significantly different from zero. In this estimation model, however, _D_<sup>_TE_</sup> may be exogenous, because the Hausman exogeneity test statistics is rather low at 0.60. Therefore, at the entry decision stage, the technical standard adoption may be endogenous (as shown above), but the price bidding stage, the technical requirements are give to potential bidders, and they would likely reflect extra costs on their price bids. Notably,

> 21 See Estache and Iimi (2008a) for more detailed discussion on potential economies of scale in infrastructure procurements.

> 22 The dummy variables for dam and civil works are excluded for convergence purposes. However, the main results have been found unchanged even if the system is estimated by the two-stage estimation technique.
- National Bureau of Statistics (NBS) (2012). Nigeria General Household Survey (GHS), panel 2010, wave 1. Public Use Dataset. Ref: NGA ~~2~~ 010 ~~G~~ HSP-W1 ~~v~~ 04 ~~M~~ . Downloaded from `https: //microdata.worldbank.org/index.php/catalog/1002` on 6 September 2019.

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- National Statistical Office (NSO) (2017). Malawi Integrated Household Panel Survey 2010-20132016 (long-term panel, 102 eas). Public Use Dataset. Ref: MWI ~~2~~ 010-2016 ~~I~~ HPS ~~v~~ 02 ~~M~~ . Downloaded from `https://microdata.worldbank.org/index.php/catalog/2939` on 6 September 2019.

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54