rafmacalaba/datause-encoder-data huggingface dataset:
little relation to cost (especially of energy), and prices seen by users were suppressed and distorted by heavy state subsidies or internal cross-subsidies. In water, for example, cost recovery was very low, with water utility revenues estimated to cover only 61 percent of operational costs in Russia. This low revenue base translated into a cycle of underinvestment, poor maintenance, rising costs, and deterioration of infrastructure.

The economic decline in the early 1990s was accompanied by a sharp reduction in the production and consumption of some infrastructure services. The maintenance and upgrading of what had come to appear to be an oversized infrastructure stock became an early investment casualty of that economic decline. The consequence was a steady deterioration in the infrastructure stock. For example, in the energy sector, the deterioration in the asset base and the associated loss of both capacity and efficiency proved that, by the end of 2007, a number of countries experienced periodic energy shortages, with a serious energy crunch appearing imminent (World Bank 2010).

Climate shocks further deteriorated the stock of existing assets. Severe floods in Russia, Tajikistan and Georgia led to landslides and slope failures, causing deaths from drowning and collapsing road and railway lines. The 2000-01 drought was estimated to have cost Georgia and Tajikistan 6 and 5 percent of their respective GDPs. Warmer temperatures and resulting ground settlement in permafrost areas of Russia began to destabilize a number of infrastructure structures, including a power station, an airport runway and residential buildings. The effect of poor road design standards, coupled with low maintenance, showed when roads began to deteriorate due to the more extreme heat conditions of Central Asian summers (World Bank, 2009b). For example, the Kyrgyz Republic reports losses of about 200 km of roads every year due to vast distances, difficult terrain, extreme temperatures, excessive loads, and lack of road maintenance budgets.

The quality and reliability of infrastructure services became an overarching concern throughout the region. Although Eurasia had nominally high access rates to improved water and sanitation, the quality and reliability of water systems was often poor. Even in capital cities, possibly less than 65 percent of connected households enjoyed 24-hour supply, and performance was typically worse in smaller towns. For example, in Armenia, less than 20 percent of urban settlements had water 24 hours a day in 2000 (Kessides and Khan 2009). According to the 2005 Business Environment and Enterprise Performance survey, transport services worsened, probably reflecting congestion and the declining road quality. With the exception of Kazakhstan (which shows a slight improvement), all other countries in the 6
high (60%). In contrast, farmers in the Northeast region use irrigation on only 25% of their cropland.

Farmers in different regions also choose different cropping patterns. For example, in the Northeast region, farmers currently allocate more of their cropland to maize and vegetables, and less to sugar and cotton. While in the Southeast region, farmers choose to have more of their cropland growing vegetables and rice, and less growing sugar and cotton. Each of the regions has a different cropping pattern because they have different regional conditions (Table 7). For example, the Northeast region is much colder than the other regions; its annual average temperature is 6.5<sup>o</sup> C, while it is warmer in both the Southeast and Southwest regions, their average annual temperature reaches 16<sup>o</sup> C. In addition, the Southeast region is wetter than the other regions with an average precipitation per month of 9.9cm, while it is very dry in the Northwest region with an average precipitation per month of only 2.2cm.

Each region responds in a different way to the climate scenarios (Table 8). For example, with the 2100 climate of the PCM model, farmers in Northwest and Southwest regions are more likely to irrigate their land, while farmers in the other three eastern regions (Northeast, Southeast and Middle regions) are less likely to irrigate. The national numbers hide these much larger changes in each region by averaging across all regions. Farmers will also adjust crop choice differently in each region. For example, with the 2100 PCM climate, farmers in the Southwest region will be less likely to grow wheat by -1%, while in the other four regions the likelihood of farmers growing wheat will increase by +4% to +14% depending on the climate scenario. Under the HADCM3 climate scenario, the likelihood of farmers in the Southeast, Middle and Northwest regions growing rice will increase by up to +2%, while the fraction of cropland devoted to rice in the Southeast and Southwest regions is reduced by -1% to -2%, depending on the future climate scenario. Similar variations in crop choice across regions apply to the other crops as well and for each climate change scenario.

# **VI. CONCLUSION AND POLICY IMPLICATIONS**

The results of the adaptation analysis strongly suggest that Chinese farmers do adapt to climate by shifting to irrigation (where possible) and by switching crops. With warmer 12
Policy Research Working Paper 9045

# **Abstract**

How do trade reforms impact households in different parts of the income distribution? This paper presents a new database, the Household Impacts of Tariffs data set, which contains harmonized household survey and tariff data for 54 low- and lower-middle income countries. The data cover highly disaggregated information on household budget and income shares for 53 agricultural products, wage labor income, nonfarm enterprise sales and transfers, as well as spending on manufacturing and services. Using a stylized model of the first-order impacts of import tariffs on household real income, this paper quantifies the welfare implications of agricultural trade protection. On average, unilateral elimination of agricultural tariffs would increase household incomes by 2.50 percentage points. Import tariffs have highly heterogeneous effects across countries and within countries across households, consumers, and income earners; the average standard deviation of the gains from trade within a country is 1.01 percentage points.

This paper is a product of the Development Research Group, Development Economics. 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 eartuc@worldbank.org, guido.g.porto@gmail.com, and brijkers@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
Autor, David H., Lawrence F. Katz, and Alan B. Krueger. 1998. “Computing Inequality: Have Computers Changed the Labor Market?” _Quarterly Journal of Economics_ 113 (4): 1169–1213.

Autor, David H., and Anna Salomons. 2018. “Is Automation Labor-Displacing? Productivity Growth, Employment, and the Labor Share.” NBER Working Paper No. 24871, National Bureau of Economic Research, Cambridge, MA.

Autor, David H. 2024. “Applying Ai to Rebuild Middle Class Jobs”, NBER Working Paper No. w32140, National Bureau of Economic Research.

Bachas, Pierre, Paul Gertler, Sean Higgins, and Enrique Seira. 2018. “Digital Financial Services Go a Long Way: Transaction Costs and Financial Inclusion.” _American Economic Association Papers and Proceedings_ 108: 444–48.

Bahia, Kalvin, Pau Castells, Genaro Cruz, Takaaki Masaki, Xavier Pedrós, Tobias Pfutze, Carlos Rodríguez-Castelán, and Hernan Winkler. 2020. “The Welfare Effects of Mobile Broadband Internet: Evidence from Nigeria.” Policy Research Working Paper 9230, World Bank, Washington, DC.

Bahia, Kalvin, Pau Castells, Takaaki Masaki, Genaro Cruz, Carlos Rodríguez-Castelán, and Viviane Sanfelice. 2021. “Mobile Broadband Internet, Poverty and Labor Outcomes in Tanzania.” Policy Research Working Paper 9749, World Bank, Washington, DC.

Bailin Rivares, A., P. Gal, V. Millot, and S, Sorbe. 2019. “Like It or Not? The Impact of Online Platforms on the Productivity of Incumbent Service Providers.” OECD Economics Department Working Papers 148, Organisation for Economic Co-operation and Development, Paris.

Baldwin, Richard. 2019. _The Globotics Upheaval: Globalisation, Robotics and the Future of Work_ . New York: Oxford University Press.

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Banerjee, Abhijit, Esther Duflo, Clément Imbert, Santhosh Mathew, and Rohini Pande. 2020. “EGovernance, Accountability, and Leakage in Public Programs: Experimental Evidence from a Financial Management Reform in India.” _American Economic Journal: Applied Economics_ 12 (4): 39–72.

Baye, Michael R., John Morgan, and Patrick Scholten. 2004. “Price Dispersion in the Small and in the Large: Evidence from an Internet Price Comparison Site.” _Journal of Industrial Economics_ 52 (4): 463–96.

Beaudry, Paul, David A. Green, and Benjamin M. Sand. 2016. “The Great Reversal in the Demand for Skill and Cognitive Tasks.” _Journal of Labor Economics_ 34 (1).

25
Using the Envelope Theorem, we observe that If _α_ 11 = 1 _/_ 2, then _G_ 1 = _G_ 2, _p_ 1 = _p_ 2, _p_<sup>_′_</sup> 1<sup>_<_0,and</sup><sup>_p′_</sup> 1<sup>=</sup><sup>_−p′_</sup> 2<sup>sothat</sup> _∂α∂v_ 111<sup>_∗_reduces</sup> to<sup>_<u>βG</u>_</sup> _p_<sup>1</sup> 1<sup>_<u>p</u>_</sup> <u>1</u><sup>_′_</sup> (1 _−_ 2 ̃ _α_ 11 ̃) _<_ 0. Thus, delegating aid to ML must increase donor 1’s utility given that _α_ 11 _>_ 1 _/_ 2. Notice, however, that _α_ 11 = 1 _/_ 2 does not maximize donor 1’s payoff. Ideally, this donor would delegate aid to an agency with _α <_ 0 _._ 5. However, this agency in contrast would then ideally delegate aid to another agency with _α >_ 0 _._ 5. Only a donor with _α_ = 0 _._ 5 cannot benefit by delegating aid to another agency that has distinct preferences. We interpret this property of the model as a rationale for ML to represent the average donor.

The following proposition summarizes the discussion:

**Proposition 3.** _Donors i_ = 1 _,_ 2 _are strictly better off delegating aid to ML for any α_ 11 _∈_ (1 _/_ 2 _,_ 1] _. Full delegation, i.e. m_ 1 = _b_ 1 _and m_ 2 = _b_ 2 _, can be supported as an equilibrium outcome in this game._

Notice, however, that Proposition 2 applies. That is, generically the game will have multiple equilibria as delegating less than _bi_ can also be an equilibrium provided that ML’s budget remains sufficiently large. We can conclude that in all cases, whether there is sufficient symmetry in which case we have no marginal donor (Panel a) in Figure 2), or whether there is asymmetry so that there is a marginal donor (Panel b) in Figure 2), donors are better off delegating all aid to ML.

# **4 Empirical Evidence**

Our empirical evidence, both qualitative and quantitative, focuses on the International Development Administration (IDA), the World Bank’s fund for providing grants and concessionary loans to low-income countries. In Section 2 we identified IDA as the multilateral agency with the largest sectoral overlap and as one of the largest donors. According to our model, both of these conditions are necessary for making aid delegation beneficial for bilateral donors. The model has shown it is optimal for bilateral donors to delegate aid if ML represents the middle ground among disagreeing donors. Thus, we should observe that ML’s aid allocations coincide with the average bilateral donor. We have seen that IDA exhibits the largest sectoral overlap among all donors (see Table 3), which is consistent with our hypothesis that IDA represents the ‘average donor.’ 23
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Form 20-F registration statement that contains detailed financial information about the issuer, and Form F-1 to register the equity securities underlying the ADRs and annual reports.

Foreign companies can also access the U.S. and other capital markets through Rule 144A and/or Regulation S Depositary Receipt facilities without SEC registration. Rule 144A programs provide for raising capital through the private placement of Depositary Receipts with large institutional investors (Qualified Institutional Buyers or QIBs) in the United States. Regulation S programs provide for raising capital through the placement of Depositary Receipts offshore to non-U.S. investors in reliance on Regulation S.

Since I am interested in studying the effect of US laws on ownership changes for crosslisting firms, I use only Level II and Level III ADRs for this study.

# _2.2. Why do Firms Cross-List? The Bonding Hypothesis_

There exist several reasons why a company might want to list abroad: to expand investor base, to increase stock liquidity, to improve the terms on which they can raise capital, to increase visibility of the company, and to achieve non-financial benefits such as increasing customer base by broadening product recognition amongst investors of the host country. These and other motivations are reviewed in detail in Karolyi (1998) and Pagano, Roell and Zechner (2002).

This section examines the investor protection and corporate governance argument applied to cross-listing. According to the Bonding Hypothesis, exchanges with strict regulation and disclosure requirements are the ones attracting foreign firms rather than exchanges with inadequate supervision. Below are some of the aspects of greater regulation that cross-listing on a US stock exchange via a Level II or Level III ADR entails:

- Issuer is subject to SEC enforcement

- Issuer is committed to providing full information and to reconciling financial statements

to GAAP 6
Policy ReseaRch WoRking PaPeR 4441

# **Abstract**

Development economics in its early years created the image of a fierce fight between advocates of contrasting theories or approaches—“balanced growth” vs. “unbalanced growth” or “program loans” vs. “project loans.” This view has the merit to highlight such conflicts in great detail; yet it fails to take into account the reality of development economics as it was practiced in the field. This paper reassesses these old conflicts by complementing the traditional focus on theoretical debates with an emphasis on the practice of development economics.

A particularly interesting example is the debate between Albert Hirschman, one of the fathers of the “unbalanced growth” approach, and Lauchlin Currie, among the advocates of “balanced growth” on how to foster iron production in Colombia in the 1950s. An analysis of the positions held by these two economists shows that they were in fact much less antithetical than is usually held and, indeed, were in some fundamental aspects surprisingly similar.

Debates among development economists during the 1950s thus must be explained—at least partially—as the natural dynamics of an emerging discipline that took shape when different groups tried to achieve supremacy—or at least legitimacy—through the creation of mutually delegitimizing systemic theories.

This paper—a product of the World Bank Archives and the Development Economics Vice Presidency—is part of a larger effort to use the Bank’s Archives to investigate the links between development economics and operational policy. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Michele.Alacevich@unimi.it _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
**Table 1**

||**1973**|**1980**|**1990**|**1998**|
|---|---|---|---|---|
|**Agriculture**|**18.09**|**14.36**|**17.67**|**17.28**|
|**Industry**|**39.22**|**33.80**|**31.74**|**32.96**|
|- Oil|19.38|10.21|11.81|13.52|
|- Manufacturing|14.11|18.16|15.45|15.48|
|- Electricity|0.67|0.76|1.53|1.40|
|- Construction|5.07|4.68|2.94|2.56|
|**Services**|**40.14**|**50.02**|**48.96**|**48.67**|
|- Commerce|12.39|14.66|13.35|13.52|
|- Transport|4.53|6.11|6.17|6.25|
|- Financial|2.22|3.88|2.36|3.55|
|- Other (non-governmental)|14.27|16.08|18.25|18.53|
|- Government|6.72|9.29|8.82|6.81|

_Source:_ Banco del Ecuador

# **3. Methodology**

# **3.1 Data and Methodology**

# **Data Description**

The data used in this study consists of quarterly data for real GDP from 1973 to 1998 for agriculture, manufacturing, oil, transportation and communications, commerce, financial services and public services (104 observations per time series). The data was provided by the Banco del Ecuador. All series were log-transformed.

Table 1 presents the contribution of the different sectors in percentage shares of total GDP at various points in time. When comparing the contribution of the sectors to the total GDP across time, it appears that the weight of the agricultural sector declined from 25.8% in 1965 to 17.3% in 1998, while the industrial sector managed to increase its share in the same time from 22.3% in 1965 to 33% in 1998. Once we disaggregate the industrial sector, we find that the increasing weight of the industrial sector can be largely attributed to the oil industry. The share of the manufacturing sector appears to have 3
# TEff TABLES

|TableI:|Key Indicators: Base Projections..........................|20<br>|
|---|---|---|
|Table 11:|Key Indicators: ExchageRate Policy ........<br>...............<br>|23<br>|
|Tablem:|Key Indicators: Interest RatePolicy........................<br>|<sup>27</sup><br>|
|Table TV:|Monetzaton of the Residual Pt'blic Sector<sup>Borrowing Requirement ....</sup><br>|28<br>|
|Table V:|Key Indicators: Joint Exchange/Inters Rate Policy<sup>.....</sup><br>.........|31<br>|
|**Table**VI:|Exchange, Interest Rate Policy and Monedizatio<sup>................</sup>|35|

||FIGURES IN**TEXT**||
|---|---|---|
|Figure One:|External Gap and Residual Public Sector Borrowing<br>Requirement ..........<br>.........................|25|
|Figure Two:|<br>Exchange Ra Simulations, Real<sup>Loan nterest Rate</sup><br>Simuaion, flation Rate Simulaions ........................|33|

We gtezly wknowledge the ugemeand helpful suggestio of John Holem, Pew Miovic and coleague i AF2CO. Katheen<sup>B. Jordan provided valuable editonal</sup> ausitanc whil plyabha Kongsamut wiligly shared<sup>her experence with JAVELIN.</sup>
# 9. Tables

_Table 1: Sampling design and response rate for the 2017-18 survey_

|||Shor|<br>t form||Lon|gform (Hous|<br>ehold expend|<br>iture)|
|---|---|---|---|---|---|---|---|---|
|Governorate|Number of<br>clusters|<br>Households<br>per cluster|Number of<br>households<br>sampled|Number of<br>households<br>interviewed|Number of<br>clusters|<br>Households<br>per cluster|<br>Number of<br>households<br>sampled|<br>Number of<br>households<br>interviewed|
|Dohuk|150|6|900|899|60|3|180|180|
|Sulaymaniyah|150|6|900|888|60|3|180|180|
|Erbil|150|6|900|894|60|3|180|180|
|Nineveh|60|6|360|360|30|2|60|60|
|Kirkuk|60|6|360|360|30|2|60|60|
|Diyala|60|6|360|360|30|2|60|60|
|Anbar|60|6|360|360|30|2|60|60|
|Salah al-Din|60|6|360|358|30|2|60|60|
|Baghdad|150|6|900|898|60|2|120|120|
|Babylon|60|6|360|360|30|2|60|60|
|Karbala|60|6|360|360|30|2|60|60|
|Wasit|60|6|360|358|30|2|60|60|
|Najaf|60|6|360|360|30|2|60|60|
|Qadisiyah|60|6|360|360|30|2|60|60|
|Muthanna|60|6|360|360|30|2|60|60|
|Dhi Qar|60|6|360|360|30|2|60|60|
|Maysan|60|6|360|360|30|2|60|60|
|Basra|60|6|360|360|30|2|60|60|
|Total|1440||8,640|8,615|660||1500|1500|

30
11 trade. When, for instance, an importing country with a tariff is induced to purchase more inputs by the provision of another country’s export subsidy, each additional unit of imports inside the importing country costs less than it would in the absence of that export subsidy. This suggests one should expect a global CGE model’s estimate of the contribution of import market access restrictions to the welfare cost of agricultural policies to be somewhat above 83 percent, the lowest of the above back-of-the-envelope estimates.

# **3. Relative importance of the three pillars in the GTAP Model’s estimate of their impact on global economic welfare, agricultural trade, and net farm incomes**

How do those expectations compare with model-based estimates of the three pillars’ relative contributions to the global welfare cost of current agricultural distortions? The Diao, Somwaru and Roe (2001, p37) study provides estimates that imply an 89 percent share of the total costs for import tariff market access, 10 percent for domestic support, and 1 percent for export subsidies, while the estimates from the World Bank’s recent analysis reported in Anderson and Martin (2005), drawing on results in Hertel and Keeney (2006) using their GTAP-AGR Model, are 93, 5 and 2 percent, respectively.<sup>16</sup> These results have been replicated and added to by Anderson and Valenzuela (2005), who also find that the cost to non-OECD country welfare is even more heavily dependent on market access barriers than is the global cost, because export subsidies are only prominent in OECD countries and contribute to the welfare of non-OECD countries by lowering the price of their food imports. These results are summarized in the first three rows of Table 4.

The middle row of Table 4 shows that import barriers have a far more important impact than do subsidies on global agricultural trade as well, accounting for 85 percent of the trade-reducing impact of the three measures. Domestic support measures explain the rest, apart from a small contribution of the opposite sign by export subsidies. Freeing all

> 16 This 5 percent relative contribution to global welfare corresponds to an upper bound estimate, as the GTAP database does not differentiate non-coupled to production payments, and the GTAP model partially restricts the use of land in alternative agricultural uses (see Dimaranan and McDougall 2005; Keeney and Hertel 2005).