rafmacalaba/datause-encoder-data huggingface dataset:
from which a substantial share of the import growth and new competition for Dominican Republic producers had arisen. Given how safeguards have been applied historically, the attempt by the Dominican Republic to apply an overall import restriction of 38 percent can be interpreted as actually applying a more favorable rate toward imports from China (since it was starting at a 14 percent tariff) compared to its CAFTA-DR partners (since they were starting at a 0 percent tariff rate). This is somewhat surprising, especially in light of the data described in Table 1, column (3) on TTB use across countries which otherwise disproportionately tends to target imports from China with more protection.

# **F. Conclusion**

Two of the most important trade policy phenomena of the last three decades include the proliferation of preferential trade agreements and of national use of temporary trade barriers such as antidumping, safeguards, and countervailing duties. This paper analyzes some of the interaction and relationships between PTAs and TTBs by reviewing recent data on the increased import coverage of TTBs and the research literature on how countries conduct their trade policy after the implementation of PTAs.

Our main analytical contribution is to use four case studies of how PTA members have applied the global safeguard policy in order to provide a clearer systemic understanding of the intersection between these potentially discriminatory trade policies. The case studies reveal that sometimes governments seek to reinforce a pro-PTA bias in their applied TTB policies, while in others they may seek to reverse the incentives created by PTA implementation and restore more MFN-like treatment by imposing policies with an anti-PTA bias. Sometimes more protection gets imposed on PTA non-partners, even when the import surge leading to the injury to the domestic industry came primarily from PTA partners ( _Argentina – Safeguard on Footwear_ ). In other instances, more protection gets imposed on PTA partners, even when the import surge leading to the injury to the domestic industry also derived from PTA non-partners ( _Dominican Republic - Safeguard on Polypropylene Bags and Tubular Fabric_ ). Discerning the nature of the discrimination requires careful examination of both the form of the applied measures – e.g., specific duties or price undertakings, in lieu of ad valorem duties – as well as the potential differences in the additional level of protection imposed across PTA partners versus non-partners.

25
Table 3: Effect of Extended Provider Training and Patient Information on Malaria Testing at the Clinic: Patient Survey

||(1)<br>Any<br>Malaria<br>Test|(2)<br>RDT Only|(3)<br>Microscopy<br>Only|(4)<br>Multiple<br>Tests|
|---|---|---|---|---|
|Extended Training|-0.0257|0.116<sup>_∗∗_</sup>|-0.0919|-0.0475<sup>_∗∗_</sup>|
||(0.0547)|(0.0486)|(0.0729)|(0.0198)|
|Patient Information|-0.0613<sup>_∗∗∗_</sup>|-0.0432<sup>_∗∗_</sup>|-0.0171|0.00632|
||(0.0177)|(0.0189)|(0.0245)|(0.00837)|
|Mean (Basic Training, No Pat. Info.)|0.58|0.21|0.25|0.07|
|N|1973|1973|1973|1973|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. All regressions include strata and survey date fixed effects. Additional covariates are selected using double lasso, from the set of all covariates listed in Table 1 and all individual-level characteristics in Table A4 (excluding predicted malaria risk and characteristics only measured at the home survey) as well as the square of patient age and illness duration. We also include pairwise interactions of the aforementioned characteristics. Missing values for all characteristics are dummied out and recoded to the mean prior to forming interactions. Missing dummies are also included in the potential covariate set and set of pairwise interactions. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

29
**Figure 4.2: Evolution of electricity tariffs following reform**

<!-- Start of picture text --> 0.18<br>0.16<br>0.14<br>0.12<br>0.1 EEGSA<br>0.08 DEOCSA<br>DEORSA<br>0.06<br>Tarifa social<br>0.04<br>0.02<br>0<br>Source: CNE<br>US$/kWh<br>Mar-98 Jul-98 Nov-98 Mar-99 Jul-99 Nov-99 Mar-00 Jul-00 Nov-00 Mar-01<br><!-- End of picture text --> A new law passed in January 2001 made a number of changes to the social tariff, designed to reduce the associated fiscal burden and provide a more objective basis for determining and revising the level of the tariff. The new law reduced the threshold of eligibility from 500 kWh per month to 300 kWh per month, leading to an estimated cost saving of US$7.1 million annually. It also obliged distributors to tender out the purchase of power for the express purpose of meeting this ‘social demand’. The idea is to allocate supply from the lowest marginal cost power plants (typically hydroelectric) to this category of domestic customers, while leaving more expensive power from mid-merit thermal plants to cover demand from largest domestic, as well as commercial and industrial, customers. Effectively, this approach has done away with the need for direct government finance of the subsidy, by creating a cross-subsidy between customer categories.

The thresholds that have been set for social tariffs are very high in relation to typical residential consumption (Figure 4.3). The average household consumes 102 kWh per month, with poor households consuming 48 kWh per month on average and non-poor households consuming 128 kWh per month. As a result, 99% of residential customers qualified for the social tariff under the original scheme. Following the recent reforms, this percentage fell only slightly to 94%, reflecting the fact that relatively few households consume in the bracket 300-500 kWh per month.

In terms of affordability, the effect of the current social tariff is to reduce the proportion of the household budget devoted to electricity from 3.7% to 2.7% for poor households, and from 4.1% to 2.6% for non-poor households<sup>23</sup> .

> 23 In practice, this is an over-estimate since it assumes zero price elasticity. If households, who currently benefit from the social tariff were faced with the true cost of electricity, they would presumably adjust by reducing their level of demand and hence the proportion of budget allocated to electricity would be somewhat lower than indicated.

30
the residual labor market. Conditional on these idiosyncratic draws and wages, workers choose the best available job and pay both the iid task cost associated with the new job as well as the moving cost, if applicable, immediately after choosing the job for the next period. Then, the period _t_ ends.

At the preparation and mobility stage of period _t_ , a worker can sample _N_<sup>�</sup> _t_<sup>_k_joboptionsfromlabor</sup> market _k_ . We assume that workers have full information about the idiosyncratic cost shock and the moving cost for the current period when they choose a job. They do not know the exact future values of the idiosyncratic component but form rational expectations. Therefore, each worker compares the expected utility from<sup>�</sup> _k N_<sup>�</sup> _t_<sup>_k_alternativejoboptionswhichincludetheoptiontomovetotheresidual</sup> labor market with the expected utility from the current job at every period.<sup>23</sup> Switching a job and switching a labor market are both subject to frictions. When a worker moves from a job _j_ to a different job _j_<sup>_′_</sup> , she pays the moving cost _δ_ even when switching within the same labor market. If the switch involves a move to a different labor market, i.e., _kj_ = _kj′_ , then she pays an additional moving cost _Ct_ ( _kj, kj′_ ) _≥_ 0, where we assume _Ct_ ( _k, k_ ) = 0 for any _k_ .

Based on the moving cost structure, we derive the present discounted utility of the agent _h_ with job _j_ after the production stage but before the preparation and mobility stage of period _t_ as where Ω<sup>_h_</sup> _t_<sup>isthesetofsampledjoboptionsfromeachformallabormarketbyagent</sup><sup>_h_at</sup><sup>_t_,thecurrent</sup> job _j_ , and the option to move to the residual market. By taking an expectation over the idiosyncratic component, we define the expected present discounted value for all workers in labor market _kj_ before the production and mobility stage of period _t_ as _Vt_<sup>_kj_</sup> _≡ EεUt_<sup>_j,h_</sup> . Finally, the systematic component of the utility for workers in the residual market is assumed to be equal to a fraction _η >_ 0 of the average real wage of all formal labor markets. We assume that the fraction is exogenous and time-invariant.

# **3.2 Equilibrium Labor Mobility and Option Values**

We can express the equilibrium probability of moving across labor markets from our model using an expression that closely resembles the standard McFadden (1973) equation.<sup>24</sup> First, we define which is simply the number of sampled job options divided by a function of job switching costs and the shape parameter _ν_ of the distribution of task cost shocks. Intuitively, it is the number of sampled job

> 23We effectively assume that _N_ � _t_<sup>_I_= 1inevery</sup><sup>_t_.</sup>

> 24 Although our model can derive a standard McFadden equation, it is not isomorphic to standard discrete choice models. See section 6 and the auxiliary model in Appendix A.7 for details.

14
# **_Money growth and inflation_**

The data shows that in the short run, inflation is more volatile than money growth due to shocks to agriculture and cereal production in particular (Figure 2), but in the long run, inflation and money growth follow a similar pattern. Money and non-food inflation followed an upward trend from 2002 until end 2011, followed by a downward trend over the period 2011-2017.

**Figure 2: Money growth and inflation (month-on-month growth rates, percentage)**

<!-- Start of picture text --> 200.0<br>150.0<br>100.0<br>50.0<br>0.0<br>-50.0<br>-100.0<br>CPI inflation food inflation nonfood inflation<br>cereal inflation M1 growth Linear (M1 growth)<br>Jul-98 May-99 Mar-00 Jan-01 Nov-01 Sep-02 Jul-03 May-04 Mar-05 Jan-06 Nov-06 Sep-07 Jul-08 May-09 Mar-10 Jan-11 Nov-11 Sep-12 Jul-13 May-14 Mar-15 Jan-16 Nov-16 Sep-17 Jul-18 May-19 Mar-20<br><!-- End of picture text --> _Source_ : Authors’ construction using data from NBE database.

# **_World prices and Ethiopian prices_**

The data shows close co-movement between Ethiopian inflation and world inflation. Figure 3 displays the co-movement between Ethiopian all-items CPI inflation and world non-energy inflation, world food inflation and Ethiopian food inflation, and world grain inflation and Ethiopian cereal inflation. Both Ethiopian and world inflation exhibit sharp swings in 2008 and 2011 due to global food and energy crises. This study examines the relative price of foreign goods in terms of Ethiopian goods. This relative price is defined as ) or in logarithm form as l ) , where is the world price, _P_ the domestic price, and _E_ the exchange rate of the birr against the U.S. dollar.<sup>6</sup> The ff ff ff series plotted in Figure 4 are centered around the mean. PP/(EE. PP PP−(l EE+ l PP PP The trend of relative prices exhibits three regimes. In the first period, relative food prices and cereal prices are relatively stable, while the all-items and nonfood relative price inflation rates are rising. In the second period, the trend of all the series is punctuated by a major drop in relative prices followed by a quick V-shaped reversal. Third, after this reversal, the series reach a peak and begin to accelerate (appreciate) and later stabilize again.

> 6 The relative price thus defined represents the amount of foreign goods (e.g., number of U.S. goods) per unit of Ethiopian good (e.g., per kg of coffee). Thus, an increase in the ratio means real appreciation of the Ethiopian currency.

8
in these countries are 8%, 16%, and 38%, respectively.<sup>5</sup> Do these numbers provide a good yardstick with which to judge the relevance of race to an understanding of inequality in these countries? Should South African and Brazilian policy-makers worry much more about racial differences in incomes than do their American counterparts? Does the small percentage of income inequality attributable to race in the U.S. mean that racial inequality is not a pertinent economic and social issue?

Conventionally, between-group inequality is calculated as a function of two arguments: differences among groups in mean incomes and the relative size and number of the groups. The figures above are based on four population groups for Brazil, three for South Africa, and five for the U.S., but the population shares of the white groups versus non-white groups differ tremendously.<sup>6</sup> In each country, the mean income of the nonwhite groups is much below that of the white group, but the non-white groups form the majority in South Africa (80%), half of the population in Brazil (50%), and a minority in the U.S. (28%). The difference in between-group inequality observed between these three countries could in fact be due largely to the difference in population shares of the racial groups instead of the differences in relative mean incomes of these groups.<sup>7</sup> The conventional between-group share is calculated by taking the ratio of observed between-group inequality to total inequality. Total inequality, however, can be viewed as the between-group inequality that would be observed if every household in the

> 5 These figures have been calculated by the authors using data from PNAD (2001) for Brazil, IES(2000) for South Africa, and LIS(2000) for the U.S.

> 6 The racial groups used in our analysis are “White”, “Black”, “Pardo”, and “other” in Brazil, “White”, “African”, and “other” (combining Coloreds, Asians/Indians, and others) in South Africa, and “White”, “Black”, “Hispanic”, “Asian”, and “American Indian” in the U.S.

> 7 The observed differences in between-group inequality may also depend on the number of groups under consideration, making the specific definition of groups a non-negligible issue.  For example, the share of between-group inequality attributable to caste in India when one groups people simply into “high”, ‘medium”, or “low” caste groupings, can be quite different from that which emerges when the partitions are _finer_ , i.e. when one makes distinctions between castes within each broad category.

5
In this context, spectrum reforms in developing countries could move simultaneously along the following broad directions:

- Carry out an audit of spectrum allocation and use, and identify priority spectrum issues that need to be addressed

- Establish a regime of tradable spectrum rights and deploy it as fast as possible in all spectrum segments for which it is likely to be suitable

- Gradually develop a spectrum commons, on demand

- Refocus government administration on

- spectrum segments where rights and commons are unlikely to work well

- government functions that cannot be delegated to markets or users

- improving administrative performance

- improving dispute prevention and resolution mechanisms to deal with interference and non-compliance

- monitoring

- reassessment of needs and management regimes

<u>Option 3: Improve piecemeal at the margins. A third alternative is piecemeal change in the</u> desired longer-term directions. This involves seeking opportunities to put in place elements of a new spectrum management regime by addressing specific problems following a consistent set of principles. New solutions are likely to be tried first in situations on which there is experience elsewhere and the risks are low, or where risks are higher but payoff in terms of economic or social benefits is large. Priority should be given to steps with potential to trigger or enable broader changes.<sup>139</sup> These piecemeal changes can be designed to gradually add up to a framework for spectrum management that is robust to market and technology changes.

This more gradual transition may be preferred in mature markets where much or all of the spectrum has already been assigned and there is an established tradition of government administration. Where a substantial body of government administration is already in place, emphasis can be given to simplifying and paring it down to the bare essence, improving the performance of remaining functions, and relying increasingly on market- and technology-driven solutions to handle growth and innovation. The Spectrum Framework Review published in the UK in 2005 foresees that by 2010 around 71 percent of the spectrum will be managed through market mechanisms (Ofcom 2005b). A gradual transition reduces disruption and enables users as well as the authorities to learn by doing, but limits the benefits from trading and liberalization, enables incumbents to delay change, and may create opportunities for interference and corruption especially in the context of weak governance.

The following are examples of piecemeal steps in spectrum reform that meet several of the criteria indicated above:

- _Remove restrictions on current spectrum use._ Flexibility in using existing spectrum assets, rather than access to new spectrum, may be what operators urgently need to

> 139In a different context, licensing a major private-sector competitor to Morocco’s state-owned incumbent telecommunications operator accelerated sector development well beyond the scope of the new license and created incentives for privatizing the incumbent (Wellenius and Rossotto 1999)

**55**
**Table 1: Informal Interest Rate and Micro Finance Coverage: OLS, Matching and IPW Results**

|||**O**|**LS**||**Matching**|**IPW**|**MB**|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|**Dummy for High MFI Coverage**|**5.475**|**8.466**|**8.546**|**6.054**|**8.185**|**8.511**|**12.518**|
||**(2.92)*****|**(4.77)*****|**(4.87)*****|**(4.14)*****|**(4.04)*****|**(6.26)*****|**(5.40)*****|
|% of functionally landless||||||||
|household||0.029|-0.041|-0.114||||
|||(0.41)|(0.55)|(1.54)||||
|% of household with irrigated land||-0.288|-0.273|-0.315||||
|||(7.09)***|(6.28)***|(6.51)***||||
|Distance to bank||-0.469|-0.612|-0.385||||
|||(1.48)|(1.81)*|(1.32)||||
|Distance to market and facilities||1.015|1.005|0.674||||
|||(2.27)**|(2.24)**|(1.82)*||||
|No. of survey households in the||||||||
|village||-0.006|-0.008|-0.007||||
|||(1.49)|(1.77)*|(1.62)||||
|% of households with VGD card||0.362<br>(5.14)***|0.383<br>(5.32)***|0.423<br>(6.09)***||||
|Fixed Effects|No|No|District|Upazilla|Upazilla|Upazilla|Upazilla|
|No. of Observations|793|793|793|793|793|793|793|

* significant at 10%; ** significant at 5%; *** significant at 1% Absolute t statistics in parentheses.

31
||Control mean|(1)|(2)|(3)|
|---|---|---|---|---|
|**Child health**(children < 5years old)|||||
|Diarrhea prevalence 48 hours|0.040|-0.006<br>(0.011)|-0.003<br>(0.011)|-0.002<br>(0.011)|
|Diarrhea prevalence 7 days|0.060|-0.001<br>(0.016)|0.001<br>(0.016)|0.002<br>(0.016)|
|ALRI prevalence 48 hours|0.041|-0.008<br>(0.019)|-0.014<br>(0.018)|-0.011<br>(0.019)|
|ALRI prevalence 7 days|0.051|-0.009<br>(0.024)|-0.019<br>(0.022)|-0.017<br>(0.022)|
|Weight-for-age z-score|-0.69|0.006|0.041|0.000|
|||(0.099)|(0.089)|(0.085)|
|Length/Height-for-age z-score|-1.453|-0.013|-0.019|-0.056|
|||(0.099)|(0.086)|(0.080)|
|Weight-for-length/height z-score|0.203|0.074|0.111|0.076|
|||(0.086)|(0.082)|(0.079)|
|Anemia (Hb < 110 g/L)|0.283|-0.049<br>(0.037)|-0.039<br>(0.035)|-0.026<br>(0.033)|
|Controls||NO|YES|YES|
|District dummies||NO|NO|YES|
|Clustered standard errors in parentheses; *** p<0.|01 ** p<0.05 * p<0.1||||

63
<!-- Start of picture text --> .<br>at _<br>| )<br>r: zp<br>| ,<br>}P<br>:- |<br>;<br><!-- End of picture text -->

HB e196) & HB 79,83) We 7479) HE 70,74) WE 66,70 cess I) os,62 I)ID) 49,53)63.59) 1) 45,49) mar.) (87,41) (32,37) (28,32) (24,28) [20,24]
**Figure A1. Relative Price Level, Selected Richer Export Destinations**

<!-- Start of picture text --> UK USA Sweden<br>1997 1999 2001 2003 2005 1997 1999 2001 2003 2005 1997 1999 2001 2003 2005<br>year year year<br>Switzerland Norway Japan<br>1997 1999 2001 2003 2005 1997 1999 2001 2003 2005 1997 1999 2001 2003 2005<br>year year year<br>200 200 200<br>150 150 150<br>100 100 100<br>relative price level (1997=100) 50 relative price level (1997=100) 50 relative price level (1997=100) 50<br>0 0 0<br>200 200 200<br>150 150 150<br>100 100 100<br>relative price level (1997=100) 50 relative price level (1997=100) 50 relative price level (1997=100) 50<br>0 0 0<br><!-- End of picture text --> Notes: Relative price level calculated as (1 _/e_ ) _∗ CP ICP Iportujtgal,t_ where _e_ is the nominal exchange rate; this is the reciprocal of the real exchange rate as conventionally defined. Relative price level normalized to 100 in 1997.
Policy ReseaRch WoRking PaPeR 4724

# **Abstract**

The authors employ the recently completed “poverty map” for Morocco, referring to the year 2004, as a tool for an ex-ante evaluation of the distributional incidence of geographic targeting of public resources. They simulate the impact on poverty of transferring an exogenously given budget to geographically defined sub-groups of the population according to their relative poverty status. In both rural and urban areas, the findings reveal large gains from targeting smaller administrative units, such as communes or districts. However, these gains are still far from the poverty reduction that would be possible had the planners had access to information on household level income or consumption. The results indicate that a useful way forward might be to combine fine geographic targeting using a poverty map with within-community targeting mechanisms.

This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to develop and apply tools for the analysis fo poverty and income distribution. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at planjouw@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
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles component of the defined benefit first pillar. The following figure reports replacement rates for the stock of retirees in Reform II.2.

**Figure 36: Comparison of replacement rates* for civil servant switchers– Reform II.2 with Reform I.2**

<!-- Start of picture text --> 50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>ref I.2 ref II.2<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br><!-- End of picture text -->

**Notes** : * Average pension as percentage of average group wage **Source** : WB calculations based on PROST.

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