rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2 – The Stock of U.S. Antidumping Measures Imposed and In Place, 1990-2009**

Source: Compiled by the authors from Bown (2010a). The stock is computed on a yearly basis as the number of 8-digit Harmonized System (HS) product-exporter combinations subject to U.S. preliminary and/or final AD measures. Since the data relies on the HS system, the stock does not reflect any imposed or removed AD measures that were imposed before 1988 under the TSUSA product classification system.

**Impact and Incidence:** To date, the best evidence we have suggests that were the U.S. to stop zeroing then perhaps as much as half of all U.S. AD measures would be removed and the duties in the other cases would fall significantly.  Our analysis also suggests that dumping margins calculated, and hence duties imposed on developing countries, are as likely to be affected by zeroing as those imposed on developed countries. As we will explain, zeroing particularly punishes suppliers with export price variation. We collect import pricing data for a number of the biggest AD disputes over the past decade (many of which were the basis for WTO zeroing complaints) and review the price

– 10 –
## 5.1.3. VAT exemptions

A closer look at the foregone VAT due to exempt and zero-rated items shows that it is more concentrated amongst the poor than the standard-rated VAT is (Graph a.). Removing exemptions would make the VAT regressive as the incidence of VAT payments, as a proportion of pre-fiscal income, is decreasing with income (Graph b.). Removing VAT exemptions will therefore increase levels of inequality and poverty.

_Figure 10: Concentration and Incidence of VAT exemptions_ <!-- Start of picture text --> a. Concentration shares for foregone VAT due to b. Incidence of foregone VAT due to exemptions (as a<br>exemptions share of market income plus pensions)<br>0.40 0.06<br>0.35<br>0.05<br>0.30<br>0.04<br>0.25<br>0.20 0.03<br>0.15<br>0.02<br>0.10<br>0.01<br>0.05<br>0.00 0.00<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>VAT Foregone VAT Simulated VAT (no ex) VAT Foregone VAT<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17._ It is clear that future marginal increases in internal revenues from higher VAT rates or more excises will negatively impact the vulnerable. Direct taxes like PAYE could provide more revenues without threatening to impoverish already-vulnerable households. For example, the threshold (for reporting) could be lowered while the marginal rates at each bracket could be raised.

# 5.2. Expenditures

## 5.2.1. Direct Transfers

Except for the SCG program, direct transfers (NUSAF, UWEP and YLP) are _simulated_ to be received by the poorest segments of the population, as shown in Figure 10a. This is in turn reflected in very large Kakwani indices of 1.15, 1.18, and 1.23 for UWEP, YLP, and NUSAF respectively, (Figure 10c). As a result, these programs have positive marginal impacts on inequality reduction, but the magnitudes are small: all direct transfers together have a joint marginal impact of 0.1 Gini point. Similarly, the joint marginal impact on poverty is 0.1 percentage points. The low coverage (less than 1 percent of the total population of Uganda) 30
<mark>more $500 million bonds, providing further evidence that firms have responded to a tradeoff when deciding to issue large bonds in amounts that exceed their funding needs.</mark> <mark>We present suggestive evidence that the channel driving these results is the investor demand for emerging market debt that is skewed towards index-eligible bonds. We show that an increase in the demand for emerging market debt, as proxied by investor flows to emerging market debt funds, is highly positively correlated with the percentage of bond issuances with face value of $500 million. Additionally, we show that funds that are less familiar with emerging market corporate debt tend to have the highest demand for indexeligible bonds. For instance, funds that specialize in developed market securities increased their share of investments in emerging markets after the GFC. Also, using portfolio-level data, we show that these funds tend to invest significantly more of their portfolio in bonds with face value equal to or greater than $500 million, relative to funds that specialize in emerging market securities.</mark> <mark>Although the literature has emphasized the role of the monetary policy environment in shifting the demand for emerging market securities, it is conceivable that factors in emerging markets could also be contributing to aggregate changes in issuance behavior. For example, changes in the willingness of emerging market firms to issue bonds could reflect higher commodity prices that increase the profitability of investment opportunities. The fact that we observe emerging market firms clustering their issuances at exactly $500 million after 2008, however, strongly suggests the importance of bond investor demand-side influences on the change in issuance behavior. It is highly unlikely that new investment opportunities leading to greater needs for funds are clustered exactly at issuance amounts of $500 million. Moreover, th</mark> e fact that yield reductions are discontinuous at the $500 million threshold is highly suggestive of bond investor demandside influences. Exogenous increases in firms’ desires for more funds in each capital raising activity should lead to higher yields, not the lower ones we observe.

4
- _Business owner_ equals 1 if the member had a business at baseline, 0 otherwise.

- _Aversion to risk general_ is measured on a 0-10 scale where 0 indicates the most

- risk averse and 10 the most risk-tolerant/lover.

- _Months as member_ , number of months as member of NRSP group.

- _Holds Office in Group,_ takes value 1 if member has or has had in the past a leadership position in group.

- _Fraction of Members of same Zaat (caste),_ is a percentage of members in the group that share the same cast of the member.

# _<u>Household</u>_

- _Education of spouse_ is years of completed schooling of the respondent’s partner, if any. Top coded at 16.

- _Total HH income and Expenditures_ , transformed to logs for analysis.

- _Number of children under 9_

- _Land_ is the total owned land inside and outside the village.

- _Credit constraints,_ dummy taking a value of 1 if the member faced any type of credit constraint, formal or informal.

- _Ever in business,_ captures business experience within the household. Equals 1 when this is the case, 0 otherwise.

- _Decision Making,_ is the number of household decisions out of a total of eight that the member usually takes on his or her own. The decisions are: children’s schooling, consumption expenditures, major investments in business or land, the respondent’s participation in community or political activities, the respondent’s spouse participation in community or political activities, whether or not the respondent should work for an income, whether or not the spouse should work for an income and how much the household saves. In the analysis, a dummy is used that takes value 1 if the variable is above the median for each gender subsample.

# _<u>Business characteristics</u>_

- _Type of business,_ dummy variables for businesses shown on brochure

- _Business Literacy_ , scores of component 1 of a PCA for a set of questions about

- knowledge about how to run a business, and of competition.

- _Sales in ‘000 rupees,_ sales of business in an average month at the time of baseline.

# _<u>Analysis-related variables:</u>_

- _Field Unit,_ refers to the NRSP branch and is the stratification variable. There are six field units in the three study districts.

26
# **7. Further Robustness**

# _Robustness to Attrition_

The attrition rate in the SLMS was remarkably low for a five-wave panel survey. Nevertheless, the combination of attrition, missing profit data, trimming capital stock, and trimming firms with large changes in profits reduces our sample of non-bamboo firms from 376 to 325 firms. Of the 51 firms which are dropped, 24 were assigned to treatment and 27 to the control group. This corresponds to a broad attrition rate (including both attritors and those dropped for missing or noisy data) of 11.1 percent for the treated, and 16.9 percent for the control group. To examine the robustness of our results to this differential attrition, we use the bounding approach of Lee (2005) to construct upper and lower bounds for the treatment effect.

The key identifying assumption required for implementing the Lee (2005) bounds is a monotonicity assumption which assumes that treatment assignment affects sample selection only in one direction. In our context, it requires assuming that there are some firms who would have attrited if they had not been assigned to treatment, but that firms do not attrit because of being assigned to treatment. This seems plausible in our context, since firms receiving treatment may stay in the sample when they would otherwise have attrited as a result of a business failure being prevented, or to an increased willingness to answer our survey question. It does not appear likely that receiving treatment would have caused some firms to drop out of the survey that would not have dropped out if they had remained in the control group.<sup>9</sup> To construct the Lee (2005) bounds we then trim the distribution of profits for the group assigned to treatment by the difference in attrition rates between the two groups as a proportion of the retention rate of the group assigned to treatment. In our application, this requires trimming the upper or lower 6.5 percent of the adjusted profits distribution for the group assigned to treatment. Doing this then gives a lower bound for the treatment effect of 348 rupees and an upper bound of 399 rupees. The returns regressions

> 9 Of course attrition from our estimation sample also arises from an outlier change in profits. Conceivably the treatment might cause a large increase in profits, making attrition for this reason more likely. However, it would have to increase profits by more than 962 percent in order to do this, and if this were the case, dropping such observations will only lower the estimated treatment effect.

17
from no education to completed EP1 (grade 5), while holding the other education variables at their mean (see Table 6).<sup>20</sup>

**Table 5. Test Statistics for Estimated Coefficients**

|_H0_|_Prob. H_|_0 is true(C_|_hi2)_|
|---|---|---|---|
|_SpouseEdu = 0_|0.071|0.627|0.005|
|_Oth1Edu =_<br>_Oth1Edu + (Oth1Edu x Female) =_<br>_Oth2Edu =_<br>_Oth2Edu + (Oth2Edu x Female) =_<br>_Oth3Edu =_<br>_Oth3Edu + (Oth3Edu x Female) = 0_|0.146|0.001|0.007|
|_Oth2Edu =_||||
|_Oth2Edu + (Oth2Edu x Female) =_<br>_Oth3Edu =_<br>_Oth3Edu + (Oth3Edu x Female) = 0_|0.934|0.351|0.578|
|_Oth1Edu x Female =_||||
|_Oth2Edu x Female =_|0.902|0.258|0.315|
|_Oth3Edu x Female = 0_||||
|_Oth2Edu x Female =_<br>_Oth3Edu x Female = 0_|0.753|0.424|0.526|

_Statistics from Wald tests of single and joint hypotheses._ As expected, the level of education of the woman has a big impact on the probability of using maternal and child health services. At the means of the other variables, there is a difference of approximately 0.2 in the predicted probabilities as the woman’s education increases from no education to completed primary. The high level of significance of own education is reflected in fairly narrow confidence intervals. Considering the case of women with an average level of education, spousal education has a fairly small impact on predicted probabilities, particularly in the case of institutional delivery. In contrast, the marginal effect of increasing education of other household members is notable. This is particularly the case for maternity care, where the probability of service use increases with 0.16 and 0.27, respectively, for pregnancy control and institutional delivery as the level of education of _Oth1_ goes from 0 to 5.

> 20 The impact of the respective education variables on predicted probabilities is described more fully in the appendix (Figure 2, Figure 3, and Figure 4). These graphs also illustrate that the potential importance of the indicator variables for spouse and other household members. The indicator variables permit the effect of the spouse or other household member not being present to differ from the effect of that person having no education. In some cases this distinction appears to be important.

15
when generating standard errors for key parameters, which allows us to check whether the results derived from the available data are sensitive to the random exclusion of markets within our sample.

# **4 Data**

As mentioned, prices used in this study were taken from the 1995, 1997 and 2001 Bulgaria Integrated Household Surveys. The 1995 survey included approximately 2,500 households. The second round, in 1997, was conducted using the 1995 sample and approximately 2,000 households were interviewed for both surveys. In 2001, another survey took place that included an identical section on food prices. Because of widespread internal migration, the sample used for the 1997 survey was no longer considered representative. The National Statistical Institute drew a new sample of 2,500 households from the pre-census listing of the 2001 Population Census. Prices on 34 food items, observed in each survey, are used in the analysis.<sup>23</sup> Consequently, the sample constitutes a panel of communities and regions, rather than a panel of households.

Information on all feasible trading partners is included for all spatial markets ( _i_ , _j_ , _c_ ) . In particular, for each of Bulgaria’s nine administrative regions, representative prices are derived by finding the modal price for each commodity among the surveyed households of each region. For each commodity, representative prices are then matched to create region-to-region price pairs. Because the prices relate to observed purchases, we assume that associated inventory levels are not so low as to prohibit trade. Under the assumption that transaction costs are non-negative, trade is considered feasible when price distances, measured in logs, are positive. The nine regions yield 72 location dyads for each year and 27 commodities. Not all commodities potentially trade between all dyads. Consequently, the process results in 1,988 observations overall that include 654 observations for 1995, 705 observations for 1997 and 629 observations for 2001.

Table 2 reports average price distances by region and by year. Very generally, the average calculated price distances fall from 1995 to 2001, although not in uniform fashion.<sup>24</sup>

# **5 Empirical results**

As discussed in section 3, the variation in the price distances can be decomposed into between and within components using standard regression or analysis of variation techniques, under the assumption that the within-market-time effects are normally distributed. The composition of the sums-of-squares is reported in table 3. Taken together, the between effects account for more than

> 23 The commodities are: beans, beer, bread, bulgar, cabbage, carrots, chicken, cucumbers, fresh milk, ground meat, honey, lamb, lard, lentils, margarine, oranges, pasta, pears, pork, rice, sausages, soft drinks, sweet peas, tomatoes, veal and beef, vegetable oil, and white cheese.

> 24 A similar pattern can be observed for price distances based on commodity averages. See table S1 in the supplemental annex.

13
# **ENABLING CONDITIONS FOR SECOND PILLARS OF PENSION SYSTEMS**

## **By Heinz Rudolph and Roberto Rocha**

## **INTRODUCTION**

**Over the past decade the Word Bank has promoted pension reforms entailing the introduction of multi-pillar pension systems.  This approach to reform has many positive aspects but has not been exempt from problems** .  Multi-pillar pension reforms typically included the introduction of a second pillar, in which mandatory savings are accumulated in individual accounts.  The new second pillars were expected to contribute to the primary objective of a more diversified and sustainable pension system and also contribute to secondary objectives, such as higher savings ratios, faster capital market development and improved growth performance.  A recent report by the Independent Evaluation Group (IEG) of the World Bank<sup>2</sup> concludes that these secondary benefits have not materialized in some of the countries that have implemented these reforms.  The Board of Directors of the World Bank has called for a better understanding of the preconditions that should be met in reforming countries, to improve the prospects of reforms that include a new second pillar.<sup>3</sup>

**Various World Bank documents have addressed the issue of preconditions for a successful second pillar.**<sup>4</sup> The literature identifies the three basic sets of factors that need to be in place to enhance the prospects of a pension reform: macroeconomic stability, a sound financial infrastructure, and adequate regulatory and supervisory capacity.  The discussion on macroeconomic stability is usually focused on the need to maintain low inflation, so as to enable the development of long-term capital market instruments suitable for pension funds.  Regarding financial infrastructure, the literature concludes that the existence of a core number of sound banks and insurance companies is sufficient for the early stages of the reform.<sup>5</sup> These institutions would ensure the proper management of contributions, payments, and individual accounts, and the provision of custodial services.  They would also ensure the provision of life and disability insurance, in the cases where this type of insurance is shifted to the private sector.  Finally, the literature stresses the need for effective regulation and

> 2 World Bank (2005)

> 3  Since the IRG paper was published, a number of reforming countries, including Argentina, Bolivia, El Salvador and Slovakia began policy discussions that may have consequences for the sustainability of their second pillars.

> 4 See Vittas (1998), Vittas (1999), Impavido, Musalem and Vittas (2002), Glaessner and Valdes (1998), and Holzmann and Hinz (2005).

> 5 Glaessner and Valdes (1998) conclude that this is the most important pre-condition in small countries.

2
effect of direct taxes and transfers, excluding contributory pensions, leads to a mere 0.05 reduction in the Gini coefficient, highlighting the limited impact of these measures in addressing income inequality.

**Figure 4. Inequality Changes, Pre vs. Post Fiscal Incomes, by CEQ income concept**

_Panel a. Change in Inequality due to Fiscal Policy using Gini Coefficient, 2018 vs. 2020_ <!-- Start of picture text --> 0.50<br>0.48 0.454<br>0.46<br>0.44<br>0.452 0.404<br>0.42<br>0.385<br>0.40<br>0.408<br>0.38<br>0.36 0.385<br>0.34 0.322<br>0.32<br>0.30 0.325<br>Market Income Disposable Income Consumable Final Income<br>Pensions Income<br>2018 2020<br>Panel b. Change in Gini coefficient due to Direct Taxes and Transfers*2020, EU countries<br>Bulgaria (EUROMOD)<br>Hungary<br>Bulgaria 2018 (CEQ)<br>Bulgaria 2020 (CEQ)<br>Slovakia<br>Poland<br>Croatia<br>Latvia<br>Cyprus<br>Greece<br>Czechia<br>Romania<br>Malta<br>Spain<br>Estonia<br>Italy<br>Lithuania<br>Portugal<br>Slovenia<br>EU-28<br>Germany<br>Sweden<br>Netherlands<br>Denmark<br>Luxembourg<br>Finland<br>France<br>Austria<br>Belgium<br>Ireland<br>-0.18 -0.16 -0.14 -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 0.00<br>Change in Gini coefficient<br>Gini coefficient<br><!-- End of picture text --> _Source:_ Authors’ estimates based on 2021 EU-SILC, 2021 HBS and CEQ Methodology. _Note:_ Original or pre-fiscal income refers to the income before any taxes and transfers are accounted for. The original income is Market Income plus pensions for the pension as deferred income (PDI) scenario. The original income is Market Income for the PGT (Pensions as Government transfer) scenario. Panel A captures changes in Gini between market income plus pensions and final income. In contrast, panel b captures changes between market income plus pensions and disposable, so inequality reduction in both panels is not strictly comparable.

22
<!-- Start of picture text --> Figure 10, Average Rule of Law in Countries<br>wlth or without FOIA (ICRG Indicator)<br>4.5<br>4<br>3.5<br>2.5 _,<br>2. = _ - - t--- ---<br>0 1._ l~r I _<br>no FOIA FOIA<br><!-- End of picture text -->

# **Regression Results**

Tables 7a-b shows the most parsimonious specification of a regression of transparency on institutional quality. It shows that controlling for GDP per capita, the transparency index is strongly correlated with governance (many at the 1% confidence level). More transparent governments govern better for a wide number of governance indicators such as government effectiveness, regulatory burden, corruption (both KKZ and ICRG), voice and accountability, the rule of law, bureaucratic efficiency, contract repudiation, expropriation risk and a composite ICRG index.

This result holds also when a subset of countries in the sample - that composed of over 100 developing countries is considered as shown in Tables 8a-b.

**Table 7a. Transparency and Governance**

<!-- Start of picture text --> DATA SET: KA UFMANN, KRAA YAND ZOIDO-L OBA TON<br>How authorities are selected and Capacity of the state to Respect of the state and<br>r laced implement sound policies the citizens for the rules<br>Voice and Political Instability Government Regulatory Rule of Graift<br>Accountability and Violence Effectiveness Burden Law<br>Income per capita *39*** *55*** .4** .30*** .63*** .54***<br>_____________ (5.11) (8.44) (802 (6.13) (11.49) (8.7)<br>Transparency -.17** -.06 ..14* ~ .14*** -.04 .<br>_______________(-2.14) (-1.16) (-.9 (-2.74) (-.76) (-1.85<br>Constan t -2.71 *e -. 444*** -3.4*** .l.99*** -5.08*** -4.26**<br>_______________ (-3.48) .(-6.84) (-6.2) (-3.95) (-9.41) (-.2<br>#obs . 136 125 126 131 131 125<br>____ R___ __ .42 .52 .55 .45 .64 .57<br><!-- End of picture text -->

**23**
<!-- Start of picture text --> Figure 3.3: External Debt-Creating Flows<br>(All countries, in percent of GDP)<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>-30<br>2006 2007 2008 2009 2010 2011 2012 2013<br>Current account deficit Real GDP growth<br>Nominal interest rate Price and exchange rate changes<br>Net FDI (negative = inflow) Residual (including exceptional financing)<br>Change in external debt<br><!-- End of picture text --> Current account deficits driven by higher imports and lower official transfers have persistently caused higher external indebtedness, though the impact has been moderated by net foreign direct investment (FDI) inflows. Since the global financial crisis current account deficits widened from an average of 4.8 percent of GDP in 2006-07, to 7.7 percent of GDP during 2008-09, and further to 11 percent of GDP by 2013.<sup>11</sup> On the other hand, FDI inflows were fairly stable during 2006-13, contributing to a decrease in external debt of about 5-6 percentage points each year. The exception was 2012, when the contribution of FDI inflows increased to 7 percentage points. In terms of the endogenous debt dynamics, GDP growth alleviated the external debt burden by 4.3 percentage points in 2006, but its contribution decreased in 2007, staying stable at about 1.6 percentage points on average each year since. The residual that includes exceptional financing,<sup>12</sup> that is, changes in arrears and debt relief, shows again the importance of debt relief under the HIPC and the MDR Initiatives in 2006 and 2007, and again in 2009.

> 11 The size of the CAD contribution does not differ significantly if measured by the medians instead of the means, as shown in the following table (numbers in table represent percentage points of GDP): 2006 2007 2008 2009 2010 2011 2012 2013

> Mean 4.0 5.5 7.6 7.9 7.2 9.2 11.7 11.1 Median 4.6 6.3 9.0 7.0 8.5 7.4 8.1 10.0

> 12 The residual also includes changes in gross foreign assets and valuation adjustments.

12