rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Economists have long argued for the introduction of a carbon price as a key component of a cost-effective policy response to addressing climate change. However, worldwide adoption of carbon pricing has been incomplete, with only about one-fifth of global carbon dioxide emissions currently covered by a carbon price, and prices mostly well below estimates of the social cost of carbon.<sup>1</sup> Experts worry that the resulting low levels of carbon prices implemented worldwide are insufficient to drive substantial emission reductions in a timeframe consistent with stabilization of atmospheric temperatures within the targets of the Paris Agreement (Green, 2021).

Wider adoption of carbon pricing is constrained by a number of factors. One key factor is concern over loss of international competitiveness and carbon leakage associated with unilateral implementation of carbon pricing. A domestic carbon price is likely to reduce the competitiveness of internationally traded energy-intensive firms relative to their unregulated foreign counterparts (Dechezleprˆetre and Sato, 2017). Resulting changes in domestic and foreign production are predicted to give rise to carbon leakage, where a portion of domestic emission reductions caused by the carbon price is offset by increases in international emissions (Carbone and Rivers, 2020). A second and related constraint to wider adoption of carbon pricing relates to domestic political economy concerns, including public opposition to the increase in price of emission-intensive products and services such as transport or heating (Drews and Van den Bergh, 2016), lobbying by emission-intensive industries toward lower carbon prices (Anger et al., 2014; Baranzini et al., 2017), or concerns over excessive job loss from structural transformation of the economy associated with carbon pricing (Hafstead and Williams III, 2018).

These concerns appear to limit the design space for acceptable carbon prices considerably. More precisely, to satisfy these concerns, an acceptable carbon price would need to achieve large greenhouse gas reductions while remaining low and not significantly affecting the output or price of regulated firms. We think of these conditions as the design trilemma facing carbon pricing, and posit that policy alternatives that successfully achieve each of these conditions

> 1The World Bank Carbon Pricing Dashboard details existing carbon pricing initiatives worldwide: `https: //carbonpricingdashboard.worldbank.org/` (Accessed 20/4/2021).

2
the only developing-country grouping that sustained an increase in intra-regional trade while also increasing its share in world exports. **Jn** the case of CACM, unlike other developing-arrangements, an across-the-board approach to trade liberalization was carried out. This is the approach that was adopted among developed-country arrangements. Avoiding the product-by-product approach typical of other developing country schemes avoided the decline in trade with the outside world and also helped promote internal trade, confirming the practical wisdom of GAIT article XXIV which requires countries forming an FTA or a CU to substantially liberalize all trade flows.<sup>9</sup> Insofar as one of the main goals of the early RI schemes among developing countries was to industrialize by increasing intra-regional trade, it is clear that the goal was not achieved. There are many reasons for this -- including those relating to their relatively rigid trade and production structures -- but one major reason was implementation failure.

**IL. The Welfare Economics of Preferential Trading Arrangements'1**

# **1. Efriciency Implications**

The figures in table 4 show a large increase in trade for the EC and EFTA following integration and little increase in intra-regional trade for developing-country RI schemes. Above, we attributed these differences to implementation. But there are other reasons as well. Consider the standard analysis of an FTA. Figures 1 and 2 show the general equilibrium import-demand and export-supply 9 This point made in Wonnacott and Lutz (1988, p. 77). Also note that ASEAN adopted a product-by-product approach. The reason for the increase in openness of ASEAN countries is that they simultaneously liberalized unilaterally. The sharp increase in trade among ASEAN partners was due mostly to sub-contracting.

'° This section draws on de Melo, Panagariya and Rodrik (1992).

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expectation formation matters by distinguishing between anticipated and unanticipated price shocks.

# **5.3  Anticipated vs. Unanticipated shocks**

In Table 6, we aim to incorporate the price expectation formation after the food price shocks by decomposing price changes into their anticipated and unanticipated components using the firststage regression of Eq. (8). Table 7 reports the two-step bootstrap estimates of equation (10), which allows the anticipated and unanticipated components to have separate coefficients. Similar to Tables 4 and 5, land size, the initial wealth, and household characteristics are also controlled. We use 737 farmers, excluding those households using _Tebasan_ or _Ijon_ system.

In the first stage (Table 6), negative estimates of the lagged price growth ∆Pt−1 and the precrisis trend TRt−1 imply that farmers anticipated a structural change in the future food price dynamics after 2007. As expected, the sign of the persistence measure ARt−1 is positive (significant at 10%) and the sign of the volatility measure SDt−1 is negative (significant at 1%).

In Table 7, we found that the two types of price shocks have different impacts on farm investments. Column (1) of the table shows that only the anticipated component of the price shock has a significantly positive effect on productive investments, while the effect of unanticipated component is insignificant. Farmers decide to invest in productive assets in response to the predicted component of the price change.

In column (2), we aim to check possible non-linearity in the effects of the two types of price shocks on farm investments. We find that that (a) the effect of the anticipated price shock on investments is concave as confirmed in the previous section, while the unanticipated shock had 22
1

# 1 Introduction

Foreign capital inflows to developing countries constitute part of the world's saving. Over the past two decades, world saving as a proportion of world income has fallen. As world saving has shrunk, so the real interest rate measured in terms of U.S. dollar interest and inflation rates has risen from 0.8 per cent during the period 1971-1978 to 4.8 per cent in the period 1981-1991 _(International Financial Statistics,_ CD-ROM, September 1994).' It is against this background that foreign direct investment **(FDI)** has appeared increasingly attractive to developing countries facing declining domestic investment and higher costs of foreign borrowing. Foreign direct investment seems an attractive form of capital inflow because it involves a risk-sharing relationship with the suppliers of this type of foreign capital. This kind of risk sharing does not exist in the formal contractual arrangements for foreign loans. Furthermore, as the World Bank (1993, 3) claims, there may be dynamic benefits: "Foreign direct investment is a large and growing source of finance that may help developing countries close the technology gap with high-income countries, upgrade managerial skills, and develop their export markets." Globally, FDI has increased dramatically over.the past decade. However, most of this increase has occurred in the industrial countries. In the developing countries, **FDI** has been heavily concentrated among a small number of countries; over 90 per cent of **FDI** inflows to developing countries in 1990 was received by only 18 countries. Half of this total flowed to eight Pacific Basin developing market economies (Hong Kong, Indonesia, Korea, Malaysia, Philippines, Singapore, Taiwan, and Thailand). Given that neither Korea nor Taiwan has shown strong interest in attracting **FDI,** it may seem surprising that these economies feature in this group of developing countries. Their appearance in this list may support the view that explicit incentive packages are not the key determinants of **FDI** flows.

In undertaking any analysis of FDI, one must recognize that **FDI** data record financial flows which may or may not correspond to changes in capital formation. Whether or not they do depends on (a) the extent of substitutability of this type of financial flow for other types of financial flows and (b) the extent to which **FDI** crowds out domestically financed 'The **real interest rate is proxied here by the London interbank offered rate on U.S. dollar deposits adjusted for the percentage change in the** U.S. **GDP deflator. During the period 1992-1995, this real interest rate averaged only** 1.5 **percent,** but **has** been **rising rapidly since** 1993 **_(World_ Economic** **_Outlook,_ May** 1994, **177):**
# **Can Malaysia Escape the Middle-Income Trap? A Strategy for Penang**<sup>*****</sup>

Shahid Yusuf and Kaoru Nabeshima World Bank

> * We thank Lopamudra Chakraborti for her excellent research support and Vatcharin Sirimaneetham for providing us with data on the investment climate in Malaysia.  An earlier version of this paper was presented at the workshop, “Positioning Penang for Sustainable Growth: A Dialogue on Penang’s Future Growth Strategy”, held on December 17-18, 2008 in Penang.  Comments and suggestions by participants at the workshop were useful in revising this paper.  We are also extremely grateful for comments and suggestions from Mathew Verghis, Philip Schellekens, Hamdan Abdul Majeed, and Homi Kharas.
and estimates of the proportion of the population without access to safe water. For countries where access data are not available, the model uses the population-weighted average level of access for the income groups to which the countries belong. Figure 1 displays the results, which suggest that the greatest number of preventable deaths are in Sub-Saharan Africa, South Asia and East Asia. Countries with relatively low incidence of this problem include Russia and several East European states.

# **3.2 Health Damage From Outdoor Air Pollution**

We attribute health damage from outdoor air pollution to atmospheric contamination by fine particulates. Over time, health research has narrowed its focus from total suspended particulate matter (SPM) to small particles less than 10 microns in diameter (PMIo) and, most recently, to particles whose diameters are less than 2.5 microns (PM2.5). Small particles are likely to be more dangerous because they can be inhaled deeply into the lungs, and because their constituent elements tend to be more chemically active (WHO, 2000; WRI, 1999; Holgate, 1999). At present, atmospheric monitoring in developing countries is limited to SPM and PM10 .

Our health damage estimates come from a recent collaborative project with WHO, which is described in Pandey, et al. (2003). Using an econometrically-estimated model of particulate pollution, we project ambient PM1 O concentrations for 3,226 cities. We use recently-estimated "dose-response" relations to compute the associated mortality and morbidity probabilities by age-sex group for each city; multiply these probabilities by numbers of people in each group; convert the results to daly's, and sum across cities, groups and damage categories in each country to obtain our estimates of total health damage. Figure 2 displays our estimates of total daly losses by country. Health damage 8
except they had not been handed over as CFs. Therefore, the intent is that sample CF and NCF forests should be fully comparable and using propensity score matching and regression we evaluate the effect of CF status on carbon stocks. The quality of the matches at the forest level is formally addressed in Sections 4.2 and 6.

Selected NCFs are also proximate to comparator CFs (e.g. in the same district), but NCF sites are not adjacent to CFs. This was avoided in case forest users simultaneously use both forest types. If many NCF sites were believed to be good matches (as was the case in the _Terai_ ), NCF sites were chosen randomly. In the middle hills NCFs are relatively rare and identifying NCF matches with randomly selected CFs was sometimes difficult. Table 1 presents numbers of sites by CF/NCF and Hill/ _Terai_ status. The map of Nepal in Figure 1 shows the spatial distribution of CFs and NCFs. CFs tend to be concentrated in the hills and NCFs in the _Terai_ , which reflects the population of CFs, which is highly concentrated in the hills.

Table 1 <u>Sites by CF Status and Physiographic Region</u>

||CF|NCF|Total|
|---|---|---|---|
|Hill|50|15|65|
|_Terai_/Inner_Terai_|15|50|65|

**10 |** P a g e
WDI-PPP recasts figures from the 1992 edition of the Bank's _World Development Indicators (WDI),_ to FSU= 100. The technique used is described in Ahmad (1992).22 The WDI did not report a figure for the FSU; for the present purpose IECSE used a preliminary PPP from the 1990 Austria-FSU work taking place under ICP auspices (somewhat under $7000, moving from schilling to dollars at prevailing exchange rates). The figures for other historically planned economies are as reported in WDI and have not been adjusted to reflect proposed revision by the SACF method.

The United Nations Statistical Division (UNSTAT) is publishing its estimates of world GDP, with country-level figures converted with its Price Adjusted Rates of Exchange (PARE). PARE is a variant on Atlas-type conversion; the main difference being that PARE relies on a longer-term averaging of apparent changes in real exchange rates. Country-level figures computed in this way are used in decisions of the UN Committee on Contributions. For the FSU, PARE seems to accept the official rate, which this study considers egregiously overvalued.

Two studies separately by Russian economists, Bolotind and llliarnov,4 also show relative GNP per capita levels for FSU republics although little is known about their sources and methods. Bolotin describes his work as a PPP study and his FSU-US relative in GNP per capita level parallels that inferred from WDI-PPP, as noted above. llliarnov's is likely to be a PPP study as well and differences between Bolotin and Illiarnov, for individual PSU economies, p:obably reflect differing ruble-based per capita GNP estimates rather than conversion issues. It is almost certain that each uses a single PPP estimate for the FSU as a whole, since there is no evidence of that the detailed price comparisons required for PPPs have been made at the republic-level. The main difference, relative to SACF figures recommended here, is that Bolotin ranks Arnenia and Georgia much higher (and Lithuania and Turkmenistan somewhat higher) than we do.

A recent survey of Eastern Europe, by Deutsche Bank, seems to have used commercial exchange rates. This would explain not only why the FSU slides so far down the relativity scale but also why Bulgaria in particular looks so high in 1990, before the sharp depreciation of 1991.

# VI. **Directions** for Near-Term Work

All data used in this study are from official FSU sources, although some of it was obtained indirectly, as a by-product of commissioning independent evaluations of national accounts. It is not certain that national officials in all 15 economies emerging from the FSU have, and have digested, all the information obtained for this study. For that reason, a special

## 22Availab1e on request from IECSE.

**NSee** Bolotin, 1992.

**24Se** Mliamnov, 1992.

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# **Table 12: Cross-country covariates associated with the demographic penetration of branches**

The results of OLS regressions of the natural logarithm of number of commercial bank branches per 100,000 adults on different country characteristics.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|GDP it l|0.526***|0.533***|0.516***|0.516***|0.402***|0.344***|0.484***|0.531***|0.523***|0.504***|0.480***|0.493***|0.519***|
|per capa (og)|(0.027)|(0.029)|(0.034)|(0.033)|(0.098)|(0.091)|(0.052)|(0.044)|(0.027)|(0.032)|(0.044)|(0.038)|(0.041)|
|Population density (log)|0.053|0.054|0.046|0.050|0.042|0.011|-0.039|0.053|0.048|0.012|0.047|0.108**|0.061|
||(0.040)|(0.041)<br>|(0.044)|(0.040)|(0.045)|(0.042)|(0.074)|(0.042)|(0.038)|(0.055)|(0.052)|(0.049)|(0.045)|
|Expected real economic growth||0.166<br>(0.220)||||||||||||
|Number of commercial banks|||-0.006|||||||||||
|(log)|||(0.070)|||||||||||
|Inflation||||-0.004<br>(0.006)||||||||||
|Electricity consumption|||||0.087<br>(0.116)|||||||||
|Land line and cell phone users||||||0.436**<br>(0.188)||||||||
|Road density|||||||0.085<br>(0.090)|||||||
|Absence of violence||||||||-0.039<br>(0.086)||||||
|Offshore financial center|||||||||0.130<br>(0.215)|||||
|Concentration||||||||||-0.240<br>(0.459)||||
|Government bank share|||||||||||-0.304<br>(0.377)|||
|Foreign bank share||||||||||||0.095<br>(0.211)||
|Branch approval|||||||||||||-0.033<br>(0.162)|
|Constant|-<br>13.87***|-<br>14.08***|-<br>13.74***|-<br>13.74***|-<br>13.35***|-<br>13.99***|-<br>13.40***|-<br>13.92***|-<br>13.84***|-<br>13.32***|-<br>13.36***|-<br>13.79***|-<br>13.81***|
||(0.316)|(0.426)|(0.318)|(0.379)|(0.421)|(0.344)|(0.496)|(0.396)|(0.306)|(0.629)|(0.536)|(0.482)|(0.474)|
|N|121|120|110|116|96|88|81|111|121|109|75|75|109|
|Adjusted R<sup>2</sup>|0.645|0.645|0.636|0.638|0.518|0.650|0.659|0.608|0.643|0.572|0.606|0.609|0.593|
|Robust standard errors in parenthese<br>***p<0.01,**p<0.05,*p<0.1|s|||||||||||||

38
While unprecedented for containerized shipping, freight rate spikes have periodically occurred in other shipping segments like dry bulk prior to the 2008 financial crisis when soaring commodity demand over a multi-year span quintupled the Baltic Dry Index (Figure 12). This historical pattern suggests that shipping rates have a “low normal” state, interrupted by bursts of high rates when capacity cannot match demand surges.

# **Conceptual Framework of Stress and Rate Formation**

Historical freight rate patterns suggest two market regimes: Under “normal” conditions, shipping capacity accommodates shipper demand. Complex price mechanisms exist, which have, in some regions, replaced systems such as freight rate conferences, where prices were fixed. The industry structure is concentrated with six major carriers providing the bulk of the capacity in global container shipping. The existing global operational alliance system, accepted by regulatory authorities, allows carriers to consolidate and optimize vessel utilization with partners while alliance members remain commercially independent.

The nature of the business and investment size limits the number of entrants impacting the competitive environment in oligopolistic behavior. However, in a normal regime and in the absence of price collusion, lines are price takers, and rates correspond to the marginal price considering the imbalance in demand (full containers) across directions. The conventional wisdom in the industry is that shipping lines cannot recover their investment but for the period of tensions when rate spikes happen. Classical models of price formation such as Bertrand’s apply to this regime.

In times of capacity tensions, shippers compete for scarce remaining slots on vessels, becoming rate-takers rather than setters. This is especially true for shippers not engaged in longer-term contracts. Willingnessto-pay dynamics shapes the markets: price formation à la Ramsey-Boiteux-Dessus hinging on freight’s ad valorem markup over product value. Lower value goods suffer more acute increases proportional to cargo value. This first manifests for export loads out of origin regions (e.g., China) before affecting import legs.

Table 2: Shippers’ Willingness to Pay: Mechanism, Rate Formation and Theoretical Reference

|**_Regime_**|**_Unstressed “Normal”_**|**_Stress and rate bursts_**|
|---|---|---|
|Mechanism|Shipping capacity supply clears<br>demand|Shippers are competing for<br>shipping slots|
|Rate formation|Oligopolistic competition: Marginal<br>price|Shippers' willingness to pay|
|Theoretical reference|Bertrand|Ramsey-Boiteux|
|Source: Authors.|||

# **Empirical Connection of Stress and Rate**

Quantitative rate-setting models remain an underexplored topic. Rate impacts happen at varying stress thresholds depending on demand levels. The 2021-22 period saw the conjunction of high demand (pandemic-induced consumer demand for goods) with disruptions. The Red Sea crisis (starting November 2023), while increasing stress, faced a comparatively weaker demand backdrop.

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Table 5: Correlates of payment delays

|Dependent Variables:|Average Payment Delay|% Over 30 Days|% Over 45 Days|% Over 60 Days|
|---|---|---|---|---|
|Model:|(1)|(2)|(3)|(4)|
|_Variables_|||||
|Log(GDP)|-1.74<sup>_∗∗∗_</sup>|-3.86<sup>_∗∗∗_</sup>|-2.83<sup>_∗∗∗_</sup>|-2.02<sup>_∗∗∗_</sup>|
||(0.391)|(0.502)|(0.357)|(0.291)|
|Year Fixed Efects|✓|✓|✓|✓|
|State Fixed Efects|✓|✓|✓|✓|
|_Fit statistics_|||||
|Observations|19,314|19,314|19,314|19,314|
|Dependent variable mean|20.5|19.4|10.5|6.7|
|RMSE|11.1|16.1|11.3|8.4|
|R<sup>2</sup>|0.11|0.15|0.12|0.10|
|Adjusted R<sup>2</sup>|0.11|0.15|0.12|0.10|

**Notes** : This table presents regressions using different measures of payment delay as dependent variable and log(GDP) as main dependent variable. All regressions control for the log of population and include state and year fixed effects. Observations are at the municipality-year level and encompass the period 20142020. Standard errors clustered at the state-level in parentheses. Significance levels: ***: 0.01, **: 0.05, *: 0.1.

35
16 Seiford, L. and R. Thrall (1990). "Recent Developments in Data Envelopment Analysis: The Mathematical Programming Approach to Frontier Analysis", _Journal of Econometrics,_ 46, 7- _38._ World Bank (1990). _A World Bank Policy Paper on Primary Education,_ World Bank, Washington D.C.

World Bank (1994a). _World Development Report,_ World Bank, Washington D.C.

World Bank (1994b). _Bulgaria: An Economic Update,_ World Bank, Washington D.C., May.
PROVISION OF WATER TO THE POOR IN AFRICA weaknesses of the model: the poor transparency of the selection procedures for the standpipe operators, particularly when the municipality is involved in choosing the manager; and the lack of monitoring efforts by the water utility in collecting water revenues, ensuring good quality service and maintaining adequate tariff levels.

Privately managed standpipes tend to be better maintained (in working order) than those managed under alternative schemes (table 2.2) but they are more expensive for consumers. However, because of issues with utility reporting and the sample size, this finding is not conclusive. The estimation problem arises as utilities may over-estimate the number of properly working standpipes/kiosks if these are not regularly monitored.

**Table 2.2    Type of standpipe/kiosk management and percentage in good working condition**

|Standpipe / kiosk<br>operator|% good working condition|
|---|---|
|Private|91|
|Utility|83|
|Community|74|
|Source: AICD WSS da|tabase|

This model is dependent on regular hours of supply and pressure of water to the standpipe by the utility. In the 1990s in Quelimane, Mozambique, private standpipe operators were invoiced according to fixed estimates of water consumption, but water supply was extremely limited and intermittent. Certain standpipe operators found it difficult to generate enough water revenues to pay back the water bill and did not have funds for adequate maintenance of the standpipes (SAWA 1997).

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