rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Tariff Cutting Formula Menu for the NAMA Negotiations

||**Developed**|**Developing**|**LDCs**|**SVEs**|**Para 6**|
|---|---|---|---|---|---|
|Formula|Swiss 8|20<br>(i):<br>No<br>cuts<br>on<br>6.5%/7.5%<br>of<br>|Nolibn|Nolibn|NoLibn|
|Flexibility|None|lines/imports; 20 (ii): 1⁄2 cuts on 14/16% of<br>lines/ imports; 22 (i): No cuts on 5/5% of<br>lines/imports; 22 (ii) 1⁄2 cuts on 10/10% of<br>lines/imports;25: Noflexibilities||||
|Unbound|MFN 2001 + 25|%||||

Notes: Members self-selected developing or developed country status. Members likely selecting developed-country status include members of the European Union, plus Australia, Canada, Iceland, New Zealand, Norway, Switzerland and the United States of America. The Republic of Korea is a developed country for NAMA.. **LDCs** are identified in the UN list of Least Developed Countries. Economies treated as Small and Vulnerable ( **SVEs** ) for NAMA were: Antigua & Barbuda, Barbados, Belize, Bolivia, Botswana, Brunei Darussalam, Cameroon, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Fiji, Gabon, Georgia, Ghana, Grenada, Guatemala, Guyana, Honduras, Jamaica, Jordan, Kenya, Macau, Mauritius, Mongolia, Namibia, Nicaragua, Panama, Papua New Guinea, Paraguay, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Sri Lanka, Trinidad and Tobago, Uruguay and Zimbabwe. Paragraph 6 ( **Para 6** ) economies (those with less than 35% of tariffs bound) were identified as Cameroon; Congo, Cuba, Ghana, Kenya, Macau, China; Mauritius; Nigeria; Sri Lanka; Suriname; Zimbabwe. The following very recently acceded members are not required to make any tariff cuts beyond their accession commitments: Albania, Armenia, Cape Verde, Former Yugoslav Republic of Macedonia, Kyrgyz Republic, Moldova, Mongolia, Saudi Arabia, Tonga, Viet Nam <u>and Ukraine.</u> Source: Laborde and Martin (2011b).

The tariff scenarios reported are:

- **Baseline** : Tariffs that would apply in the absence of a DDA agreement in 2025. These are based on tariffs in 2004, with adjustments for internationally-binding commitments to reform.

- **B** : Tariffs following implementation of the DDA formula without flexibilities.

- **C:** Tariffs following implementation of the formula with country exceptions, such as those for LDCs, SVEs and RAMs..

- **D:** Tariffs after the tariff-cutting formulas with flexibilities for countries and products.

The tariff-cutting formulas are applied to bound tariffs, rather than to applied tariffs, and hence it is useful to first examine the implications of the formula and exceptions for the level of bound tariffs. We use the standard assumption in this literature that the applied rate at the finest available level is cut only when, and to the extent that, the new bound tariff falls below the initial applied tariff rate.

12
Table 7: IV estimates of infrastructure provision (second-stage)

|||_Dependent var_|_iable:_|
|---|---|---|---|
||GVA per<br>capita (log)|GVA (log)|Population (log)|
||(1)|(2)|(3)|
|Electricity Access|_−_2.341<sup>_∗∗∗_</sup>|_−_1.366<sup>_∗∗∗_</sup>|0.975<sup>_∗∗∗_</sup>|
||(0.386)|(0.462)|(0.214)|
|Highway Access|_−_0.554<br>(0.496)|_−_0.406<br>(0.576)|0.148<br>(0.276)|
|Electricity Access X|3.372<sup>_∗∗∗_</sup>|2.898<sup>_∗∗∗_</sup>|_−_0.475|
|Highway Access|(0.951)|(1.110)|(0.532)|
|Wald Test _χ_<sup>2</sup>|3844.2<sup>_∗∗∗_</sup>|2577.3<sup>_∗∗∗_</sup>|2501.3<sup>_∗∗∗_</sup>|
|Observations|1,536|1,536|1,536|

_Note: Robust standard errors clustered at the state level in parentheses. ∗p<0.1,∗∗p<0.05,∗∗∗p<0.01._ However, these negative effects associated with the provision of electricity alone are fully compensated when both types of infrastructure are present, confirming our main findings regarding infrastructure complementarity in section 4.1. The overall effect of joint infrastructure provision (given by the sum of the three coefficients on electricity access, highway access, and their interaction) is a twofold increase in GVA and an increase of GVA per capita by 61 percent.

# **5 Conclusions**

This paper is among the few to investigate the effects of different types of infrastructure provision on local economic development and the first to investigate the issue over a long period (3 decades). Focusing on several decades is necessary not only because investments are staggered in time, but also because effects may take time to materialize. In our case, the study period allows us to capture the bulk of infrastructure investment in Brazil, which started in the 1970s, and to assess how these large investment programs affected targeted municipalities.

In contrast with previous results from the literature, we do not find strong 23
Policy Research Working Paper 10453

# **Abstract**

As climate change intensifies, dry rainfall shocks and - droughts are a growing concern. At the same time, scien tific evidence suggests that the world has surpassed the safe planetary boundary for green water, which is water stored in biomass and soil that is crucial for maintaining climate resilience. Yet, evidence at the global scale of these combined forces on economic growth is poorly understood. This paper attempts to fill this gap by using data on annual subnational gross domestic product for 82 countries from 1990–2014. Using rainfall shocks as plausibly exogenous variations in a spatially specific panel at the grid level, the analysis finds that the global effects of droughts on economic activity are substantial. Moderate to extreme droughts reduce gross domestic product per capita growth between 0.39 and 0.85 percentage point, on average, depending on the level of development and baseline climatic conditions, with low- and middle-income countries in arid areas sustaining the highest relative losses. In high-income countries, moderate droughts have no impact, and only extreme droughts have adverse effects, reducing growth by about 0.3 percentage point, a little less than half the impact felt in the low- and middle-income country sample for the same intensity of drought. Crucially, the impact of a dry shock of a given magnitude also depends on antecedent green water availability. The results show that increases in soil moisture in previous years can neutralize the harmful impacts from a dry shock, with suggestive evidence that local and upstream forest cover are key channels through which these impacts manifest. These findings have important implications for measuring the economic impact of droughts and can inform adaptation investments.

This paper is a product of the Water Global Practice. 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 ezaveri@worldbank.org; rdamania@worldbank.org; and nengle@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
Table (G16) Jordanian Neighbor Primary Outcomes Heterogeneity by: Gender

||(1)<br>Social Attitudes<br>and Perceptions|(2)<br>Economic Attitudes<br>and Perceptions|(3)<br>PolicyPreferences|(4)<br>Altruism to Syrians|
|---|---|---|---|---|
|Treat|-0.179|-0.105|0.240|0.144|
||(0.253)|(0.272)|(0.248)|(0.289)|
|Treat*Neighbor Female|-0.236|0.194|-0.117|0.110|
||(0.360)|(0.371)|(0.296)|(0.397)|
|Neighbor Female|0.145|-0.077|0.083|0.027|
||(0.101)|(0.071)|(0.062)|(0.093)|
|p-value: T + T*Het|0.04|0.69|0.50|0.31|
|Control Mean|0.60|0.60|0.60|0.60|
|Control SD|0.49|0.49|0.49|0.49|
|N|1102.00|1102.00|1102.00|1102.00|

This table shows heterogeneity analysis of the primary outcomes for Jordanian neighbors, by whether the Jordanian neighbor respondent is female. The outcomes are as defined in table G1. The regressions have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%).

Table (G17) Jordanian Neighbor Primary Outcomes Heterogeneity by: Age Group 18-25

||(1)<br>Social Attitudes<br>and Perceptions|(2)<br>Economic Attitudes<br>and Perceptions|(3)<br>PolicyPreferences|(4)<br>Altruism to Syrians|
|---|---|---|---|---|
|Treat|-0.268*|0.119|0.183|0.134|
||(0.138)|(0.168)|(0.165)|(0.183)|
|Treat*Neighbor 18-25|-0.716|-1.280*|-0.208|0.927|
||(0.624)|(0.713)|(0.525)|(1.241)|
|Neighbor 18-25|0.094|0.153|0.181|0.169|
||(0.130)|(0.150)|(0.116)|(0.213)|
|p-value: T + T*Het|0.11|0.09|0.96|0.38|
|Control Mean|0.09|0.09|0.09|0.09|
|Control SD|0.29|0.29|0.29|0.29|
|N|1102.00|1102.00|1102.00|1102.00|

This table shows heterogeneity analysis of the primary outcomes for Jordanian neighbors, by whether the Jordanian neighbor respondent is in the age group 18-25. The outcomes are as defined in table G1. The regressions have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%).

51
<!-- Start of picture text --> 20.0<br>20<br>14.9 15<br>©<br>D<br>£<br>10 c8<br>©<br>a<br>6.3<br>4.3 5<br>2.9 26<br>Electricity Internet Paved road 0<br>HH Districts |) Population<br><!-- End of picture text -->

<!-- Start of picture text --> 32.5<br>30<br>g<br>Si<br>8<br>=<br>8<br>€<br>20 eS2<br>3<br>16.3 =8<br>£<br>2<br>=<br>10.0 10 3<br>°<br>0<br>Paved road Electricity Internet<br><!-- End of picture text --> <!-- Start of picture text --> 60.3<br>60<br>5<br>o<br>2<br>Oo<br>£<br>£<br>40 @fe}<br>8<br>29.8 =8<br>=<br>5<br>3Da >2<br>205<br>s<br>3<br>£®<br>2<br>=&<br>0<br>Paved road Electricity Internet<br><!-- End of picture text -->
|**Exhi**|**bit 3b:**<br>|**Evide**<br>|**nce from cross-regio**|**nal studies**<br>**Implications for market**|**(II/V)**<br>|
|---|---|---|---|---|---|
|**Study**|**Brief**<br>**description**|**Time**<br>**period**|**Key finding**|<br>**vs. intervention**<br>**question**|**Implications for centre**<br>**vs. region question**|
|Canova and<br>Marcet<br>(1995)|• Convergenc<br>e study of<br>144<br>European<br>regions<br>(NUTS 2 in<br>most cases)|1980-<br>1992|• Fast convergence to respective steady states<br>• However, differences between steady states<br>persist, poor regions remain poor<br>• Authors claim EU cohesion policy without much<br>impact and structural changes in economic<br>environment needed for catch-up|• Failure of cohesion<br>policy may support<br>market hypothesis<br>• However, unclear if<br>structural change<br>accomplished best<br>through state or market|• Centralized policies not<br>seen to work|
|Canove<br>(2004)|• Investigates<br>club<br>convergence<br>of 144 NUTS<br>2 EU regions|1980-<br>1992|• Natural clustering of units in four groups of regional<br>income per capita<br>• Persistence of initial income characteristics and<br>substantial immobility in ranking (clustering rather<br>than convergence, even within groups)<br>• “Bleak” future for the possibility of reducing<br>inequality between regions|• Same to Canova and<br>Marcet (1995)|• Similar to Canova and<br>Marcet (1995)|
|Cappelen et.<br>al. (2003)|• Sample of<br>190 regions|1980-<br>1997|• More convergence at national than at the regional<br>level<br>• EU support with significant and positive impact on<br>growth performance of EU regions, with economic<br>effects stronger in more developed regions|• Mixed|• Mixed, central support<br>with positive impact,<br>however, convergence<br>more at national level<br>with strong autonomous,<br>accountable<br>governments|
|Carrington<br>(2006)<br>**Evidence for**|• Sample of<br>65 NUTS 2<br>regions<br>**marketEvid**|1984-<br>1993<br>**ence for in**|• Finds evidence of convergence that is neither fast<br>nor continuous, regions are persistent in belonging<br>to certain income groups, while sub periods of<br>convergence and divergence are discernible<br>**terventionist policyEvidence for decentralized a**|• NA<br>**pproach**<br>**Evidence for**|• NA<br>**centralized approach**|

26
However, Figure 10 shows that the number of people relying on social transfers to close their poverty gap increased steadily after 1998, approaching 20 percent in 2002. This means that headcount poverty would have been 20 percentage points higher if social assistance had not been available as a safety net.

Even though social transfers (excluding old age pensions) have a direct poverty reducing effect, the system is extremely costly and inefficient. Calculations based on Household Budget Surveys estimate the total amount of social transfers in 2002 at 6.3 percent of GDP, which is close to what government statistics report. However, 56 percent of this amount (or 3.6 percent of GDP) is not reaching the poor: 26 percent is in excess of the amount necessary to bring household consumption to the poverty line level (called “overflow”), while 30 percent (an equivalent of 2 percent of GDP) goes directly to non-poor households (see the left-hand panel of Figure 11). Although benefit tightening has been tried on several occasions, the amounts leaking were of a similar order of magnitude throughout the analyzed period.

**Figure 11:  Targeting of Social Transfers (Excluding Old Age Pensions)**

<!-- Start of picture text --> Who receives social transfers ? Social transfers per capita by<br>consumption deciles<br>% of resources going to:<br>d1 0<br>poor non-poor overflow<br>d9<br>d8<br>44% d7<br>26% d6<br>d5<br>d4<br>30% d3<br>d2<br>PLN /<br>d1 month /<br>eq. adult<br>0 50 100 150<br>Source: Authors’ calculations based on HBS<br>consumption deciles<br><!-- End of picture text --> The right-hand panel of Figure 11 illustrates the incidence of social transfers (excluding old age pensions) from an ex post rather than an ex ante perspective and reveals a disturbing pattern: there is very little difference in the actual nominal per capita amount of social transfers across the expenditure distribution. Whatever quintile is evaluated, whether rich or poor, each (equivalent) individual receives on average 120-140 PLN per month. And all deciles except the 9<sup>th</sup> and 10<sup>th</sup> receive higher per capita transfers than the poorest decile. If these resources were applied with perfect targeting, rather than being spread more or less uniformly over the distribution, they would be more than double the amount needed to eradicate poverty .

The conclusion, therefore, has to be that Poland has a costly and inefficient system for distributing social transfers; less than half of which serves to bring household consumption up to the poverty line level. A major proportion of these resources is not going to the most needy. However, it might be the case that poverty reduction is not the only objective of this redistribution, i.e. some of the transfers serve an insurance function and are not necessarily designed to improve the well-being of the poorest. Another 14
- 18 -

# Table 2: The Export Supply Equation for Turkey and MoroccoaI

Turkey Turkey Morocco (1063-83) (1968-83) (1968-85)

|Constant|-18.7|-22.5|-19.0|
|---|---|---|---|
||(2.03)|(4.49)|(2.47)|
|Relative Prices VT)|3.40<br>(.23)|3.82<br>(.50)|1.62<br>(.89)|
|Capacity (T(14+))|3<sup>.</sup>4<sup>0b1</sup>|3.82__|3.83<br>(.29)|
|Capacity Utilization<br>(-T(b-l-P))|<br>-5.19<br>(7.20)|-8.92<br>(7.66)|-3.03<br>(3.03)|
|R2|.917|.79|.95|
|DV|1.79|2.03|2.85|
|Sargan Test (X<sup>2</sup>3)|6.89|5.24|2.08|
|Godfrey Test (X<sup>2</sup>1)|5.83|1.94|1.73|
|Chow Test (X<sup>2</sup>1)|1.50|.18|1.66|

at Standard errors in parenthesis. _kL_ Constrained coefficient.
- 17-

Now, let us see whether the data show that contractual savings institutions are more willing to hold risky and long-term assets than other institutional investors and banks. Figure 8 compares the portfolios of US contractual savings institutions with those of the banking sector.

**Figure 8: United States Financial Institutions Portfolios**

<!-- Start of picture text --> US-Portfolios of Pension Funds US-Portfolios of Life Insurance<br>(1996) Companies (1996)<br>ST Loans Other ST Loans Other<br>and Cash ilo 11%and Cash 5%<br>LT Loans ST B-111S2 Loans LT 9% 7/i g 20% Sharesand<br>and Bonds 8 Bills 2u<br>and Bonds<br>57%~~~~~~~~7<br>L Ionvstmn 1 opais196<br>LT Bonds~~~~~~~~~~~~~~T od<br>US-Portfolios of Open-end US-Portfolios of Banks (1996)<br>Investment Companies (1996)<br>ST Loans Other<br>and Cash 80/ Ohr6% Securities<br>LT Loans ST Loans<br>and Bonds<br>Shares<br>W12%<br>LT Loans<br>59%<br>LT Bon s ~~~~~~~~ST Loans<br>and Cash<br><!-- End of picture text -->

**Source:** OECD, Institutional Investors Yearbook, 1997, and Federal Reserve, Monthly Bulletins.

Among the remarkable facts in Figure 8 are the high weight of securities in the portfolios of pension funds (84 percent), life insurance companies (79 percent) and openend investment companies (90 percent) relative to banks (23 percent), and the low weight of short-term loans and cash in the portfolios of those institutions (4 percent, 7 percent, and 8 percent respectively) relative to banks (59 percent). Pension funds, life insurance companies and open-end investment companies are also heavily invested in long-term bonds. Clearly, US contractual savings institutions hold larger fractions of their total assets invested in traded securities such as stocks and long-term bonds while the assets of the banking sector are invested more heavily in private financial instruments (loans) of short-term maturity.
For this reason in columns (1) and (2) of table 7 we exclude these regressors in order to confirm that their inclusion is not crucial for our results.

Because of the presence of the interaction we can calculate the impact of a percentage change in tariffs on productivity growth for different types of firms depending on their distance from the productive frontier.<sup>31</sup> In figure 4.2 we graph these marginal effects based on the results from column (3) and (4) of table 7. The bottom line is that for most of the plants, except those in the top decile, e.g. the ones that are more distant from the production frontier, the tariffs reduction spurs their productivity growth.

<!-- Start of picture text --> Marginal Effect of % Tariff Change on % Productivity Growth<br>1.5<br>1<br>0.5<br>0<br>-0.5<br>-1<br>OLS FE<br>-1.5<br>1% 5% 10% 25% 50% Mean 75% 90% 95% 99%<br>Percentile - Distance from Frontier<br><!-- End of picture text --> Figure 5: Marginal effect of Tariffs on Productivity Growth The results from table 7 just discussed confirm the two basic predictions of the model. After having presented our main results, we will now discuss a set of potential concerns that may invalidate our findings and assess if these are truly robust.

First of all, an important issue to be discussed is the possibility that tariffs could be endogenous. Fundamentally we think this is not a major problem for two reasons. The main reason is that while it is reasonable to expect that an entire industry is be likely to be able to influence its tariffs schedule, it is harder for a plant alone to influence the tariffs set for its entire industry. Therefore, if our analysis 31 � Our β of interest is equal to β2 + β3 ∗ LogDistance.

17
8 government credibility and its willingness to respect property rights of individuals (see, e.g., Knack and Keefer 1995 and Acemoglu, et al. 2002, Clague, et al. 1996).

High bureaucratic quality is another government policy with public good characteristics. Lower bureaucratic quality implies that the quality of public services offered generally by government to the average citizen is low, and that the average citizen confronts more obstacles to doing business with government. At the same time, those obstacles can always be eliminated for favored constituents of the government. Hence, bureaucratic quality is a useful indicator of the tradeoff that governments make between the pursuit of general and private interests. It can, again, be measured using the eponymous variable from Political Risk Service’s _International Country Risk Guide_ .

# **_Measuring public or non-targeted good provision:  secondary school enrollment_**

These measures of public or non-targeted good provision are subjective. The best objective measure would be one that quantified government spending on public or nontargeted goods. Unfortunately, even when we can identify categories of government spending that appear to be untargeted or dedicated to public good provision, the actual destination of these funds is often targeted. For example, education spending can be aimed at raising achievement for all children (through curriculum reforms, testing, spending on high quality teachers); or it can be targeted (by building new schools in some areas, but not in others). Cross-country budget data on education do not distinguish which.

To circumvent this difficulty, an outcome indicator, secondary school enrollment, is used below to approximate the extent to which the public component of education spending predominates.<sup>6</sup> If politicians care relatively more about political targeting than they do about

> 6 Primary school enrollment could, in principle, also be used, but most countries send most of their children to primary school  The coefficient of variation in the sample used here is 17.6 percent for primary school enrollment, and 50.8 percent for secondary school enrollment.