rafmacalaba/datause-encoder-data huggingface dataset:
The implementation of these new rules and regulations has been uneven across different countries in the region. Figure 17 below shows the percentage of countries in each ECA group that have implemented new capital and liquidity requirements according to the latest BRSS survey. Russia and Turkey, where some of the largest banks are located, have imposed new capital surcharges for banks deemed systemically important. Additional liquidity requirements are still to be implemented in the majority of ECA countries.

**Figure 17: Capital and Liqudity Requirements on SIFIs**

<!-- Start of picture text --> 100100 100<br>71<br>64<br>50<br>40<br>33 33 33<br>29<br>17<br>15<br>0 0 0 0 0<br>EU CE & BC WB SC CA Russia Turkey EE RoW<br>Additional capital requirements Additional liquidity requirements<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: Bank Regulation and Supervision Survey conducted by the World Bank. Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe; **ROW for** the rest of the world **.** Another important element of new regulations has been to provide a robust framework for the resolution of systemically important banks. The bankruptcy of Lehman Brothers in 2008 highlighted the many difficulties in resolving large banks with multiple business lines in different countries subject to local regulations in those countries. Moreover, without a specific resolution regime for large/interconnected financial institutions, supervisory authorities lack the tools to intervene in the preinsolvency stage and activate contingent plans to resolve large financial institutions in an orderly manner, preventing major disruptions to the financial system and the real economy, and minimizing the risk of loss for taxpayers. Figure 18 below shows the percentage of countries in each region that have implemented policies for the resolution of large financial institutions. The ECA region lags behind the rest of the world in addressing resolution of SIFIs. This is especially a concern for Turkey and Russia which host some of the largest banks that are internationally active in the ECA region.

21
support for strengthening women’s rights. Only 27 percent overestimate the public reform support. The remaining 13 percent guessed correctly.

# **4.1.3 Main Outcomes**

The proposal to reform gender discriminatory inheritance law is unpopular in Tunisia. Aggregating both genders, I find that 37 percent of Tunisians are favorable to the introduction of a gender equal inheritance law regime.<sup>13</sup> The other measured outcomes provide a consistent picture of respondents’ attitudes towards gender equality in inheritance as illustrated by figure 7. Among the respondents in the control group, 35 percent of respondents prefer a gender equal inheritance split in the hypothetical bequest scenario. By contrast, reported willingness to gift one’s daughter or another female relative is high: 66 percent of all respondents in the control group declared that they would be willing to use gifting for achieving a more gender equal inheritance split among their children.

Demand for gender equal inheritance arrangements differs by gender: women are significantly more likely to choose the ideal bequest (p-value _<_ 0.05) and significantly more likely to support legal reform (p-value _<_ 0.01). There is no statistically significant gender difference in the willingness to use gifting. The share is the same among respondents with a low score on the social desirability index, which suggests that this finding is not driven by experimenter demand effects. Table 2 shows the average marginal effects from a logit model regressing the main outcomes on respondents’ socio-economic characteristics, as well as second-order beliefs at baseline in the control group. Women are more likely to demand gender equality than men: they are almost 16 percentage points more likely to choose a gender equal ideal bequest and 19 percentage points more likely to favor legal reform, a finding which is significant at the 1 percent level.

Other respondent characteristics matter: differences across age cohorts are not statistically significant but on average, older respondents tend to endorse less gender progressive attitudes than younger respondents. Support for gender equality in inheritance significantly decreases with educational attainment and religiosity. University graduates are 18 percentage points less likely to support legal reform; the decrease is 4 percentage points for highly religious individuals (significant at the 1 percent and 5 percent level). Importantly, respon-

> 13This percentage mirrors the result of my initial survey which was conducted one year earlier in March 2022 and served as the basis for the misperception correction performed in T2.

23
Policy Research Working Paper 7732

# **Abstract**

This paper assesses the impact of the demobilization, reinsertion and reintegration program in post-war Burundi. Two major rebel groups benefited from cash and in-kind transfers, the CNDD-FDD from 2004, and the FNL from 2010. A panel data of households collected in 2006 and 2010 is combined with official records from the National Commission for Demobilization, Reinsertion and Reintegration. Regression analysis shows that the cash payments received by FNL demobilized households had a positive impact on consumption, nonfood spending and investments. The program also generated positive spillovers in the villages where FNL combatants returned. Ex-combatants indeed spent a large part of their allowance on consumption goods and clothing, thereby generating a short-run economic boom in villages. However, the long-run evolution of consumption indicators is negative for CNDD-FDD households, as well as for villages where CNDD-FDD combatants returned, suggesting that the direct impact and the spillovers of the program vanished in the long run.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at olivier.sterck@economics.ox.ac.uk.

_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
international food price increases for virtually most of the MENA countries. Only Algeria and Tunisia have low pass-through coefficients.

To get a sense of robustness of our estimates, we use Monte Carlo simulation (1,000 iterations) and bootstrap standard errors for the 6 and 12-month food price pass-through coefficients, shown in Table 3. For many countries, including Bahrain, Kuwait, Qatar, UAE, Iraq, Syria, Egypt, Jordan, Morocco, and WBG, the estimated 12-month pass-through elasticities are statistically significant at the 5 percent level. For other countries, the pass-through coefficients are not significant, possibly due to limited price transmission (i.e. policy interventions in the cases of Algeria and Tunisia) or relatively short time-series rendering the coefficients insignificant (i.e. Lebanon, Djibouti and Yemen).

We also tested the robustness of the results to the choice of the food price index. We used the FAO world food price index, which is an alternative to the World Bank‟s food price index. Both indices are similar, but the FAO index shows higher peaks in 2011. The main effect of using the FAO index is to increase the standard errors of the coefficients, while preserving the pass-through coefficients. Using disaggregated world price index data for cereal, from the FAO and World Bank, produces lower pass-through coefficients than those obtained from aggregated indices. This result is consistent with a lower share of cereal than total food consumption in household expenditures.

The market exchange rates vis-à-vis the euro work best because even in oilproducing MENA countries, a significant share of food imports is denominated in euro. The nominal effective exchange rate, which is a trade-weighted average of the nominal exchange rate, might not be a good proxy for import prices because it also contains export data. Nevertheless, the type of exchange rate choice does not impact significantly the size of the pass-through coefficients.

As it is visible in Table 3, a decline in international food prices does not transmit into domestic food markets and virtually in all MENA countries prices are highly downward-sticky.<sup>16</sup> Commodity price volatility is thus having an asymmetric effect on domestic prices: only price increases are transmitted, whereas consumers do not benefit from a decline in food prices. These results are consistent with the findings

> 16 The UAE and Yemen are the only exceptions.

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_have no freedom of choice and control at all and 10 means you have a great deal of freedom of choice and control, please indicate how much freedom of choice and control you feel you have over the way your life turns out._ ” Due to its brevity, this measure is increasingly used in household surveys in development settings. It has also been included in the World Values Survey (WVS) since its first wave in 1981.<sup>11</sup> The WVS consists of nationally representative surveys conducted in nearly 100 countries, and is the most comprehensive source available of cross-national, time series data on human beliefs and values. This is of particular interest for analyzing gender differences in sense of agency, complementing the results from the measures described above, which show indicative but unsystematic evidence of women’s lower sense of agency compared to men. We use longitudinal individual-level data to estimate the following basic specification: (perceived freedom of choice and control) = α + β (female) + **X’** γ + **ε** (1) The vector X includes socio-demographic factors that might influence perceived control. Table 3 reports Ordinary Least Squares (OLS) estimates of the coefficients in equation (1). The original World Values Survey variable names are provided in parentheses. For all the specifications, the analysis is restricted to the period 1989-2014, as many demographic variables of interest are unavailable in the first WVS wave. Column (1) studies the simple relationship between the binary variable female and the variable _perceived freedom of choice and control_ . Results indicate that, on average, women score 0.13 points lower on the 10-point perceived control scale: 6.92 for men versus 6.79 for women<sup>12</sup> . As expected, the R-squared is very low (0.001), indicating that gender by itself explains very little of the variation in the outcome of interest.

In this vein, Column (2) analyzes the relationship between gender and the perceived control scale variable while including an additional set of demographic and socio-economic controls. Results here show that this negative association decreases but remains significant when including controls for marital status, education, age, religiosity, relative income level and employment status. We find that being married and being a housewife (the category is gender-neutral, though 97 percent are women) have a negative and significant effect on perceived control (0.18 and 0.23 points lower respectively). The effect of age is negative and significant, though the magnitude is small. The set of variables that capture unstable employment have a negative and, in most cases, significant impact on the perceived control score. These

> 11 Simple comparisons across waves should be made carefully. As Stevenson and Wolfers (2008) note, WVS data are strongly influenced by methodological changes between waves.

> 12 The gender difference in perceived control using all survey years is slightly higher at  0.17.

21
are lower than China‘s. _The origin of the investors does not matter though it will become increasing critical for regions such as SSA where investment capital is scarce._

# **4.4 Significant potential for relocation of jobs to low wage countries**

Due to the sheer size of its labor market, the number of jobs China‘s ongoing industrialization will potentially create for low-income countries is huge. Several factors will matter.

As employment statistics for manufacturing is extremely sparse and tentative, we cannot provide any estimates of potential job relocation. However, rough calculations are informative and sufficient. Currently, China employs about 85 million workers in its manufacturing sector nationwide. Going forward, rising wages will force China to upgrade to higher value-added, more capital/technological intensive sectors and relocate the jobs in the existing sectors to countries that have lower wage rate than China‘s. India currently employs about 9 million workers and Brazil about 13 million (Table 4.1). These emerging market countries employ about 120 million workers whose jobs could be relocated to other developing countries in the coming years. Notably, India still has significant surplus labor which implies that its labor intensive industries will expand before they contract and relocate jobs. Its younger population will provide more workers for Indian industries in the future but also more potential jobs for lower wage countries.

**Table 4.1: Employment in Manufacturing, Potential Dragons, 2009/10**

||Brazil|China|India|Indonesia|Total|
|---|---|---|---|---|---|
|Employment share in||||||
|Manufacturing (million)|**13.1**|**85**|**8.7**|**12.5**|**119.3**|
|Population (million)|192|1,324|1,140|277|2,934|
|Outward FDI(USD billion)|11.5|68|14.6|2.7|96.8|

Source: ILO, Penn World Tables and UNCTAD, 2011 The employment of labor intensive jobs in low wage countries currently is also large but pales in comparison to the numbers in either China or the emerging market countries (Brazil, China and India) collectively. As an example, in 2009, all developing countries in Sub-Saharan Africa collectively had a labor force of 338 million. Most of it was employed in subsistence agriculture that fetches below poverty level wages. Assuming that about 3 percent are in manufacturing suggests that the sector employs about 10 million African workers. To achieve employment in manufacturing equivalent to about 20 percent of total employment implies that Sub-Saharan Africa needs a total of about 70 million jobs. In most industrial countries, the share of 47
_<u>Table 9: Factor price changes due to CAFTA</u>_

|<br>**_Non-Farm Segment:_**|<br>Δ**P**|<br>Δ(**P/CPI)**|**% of Full**<br>**Lib**|
|---|---|---|---|
|**Skilled Labor**|0.7|1.2|60|
|**Unskilled Labor**|1.0|1.6|55|
|**Capital**|0.9|1.5|85|
|**Sk/Unsk wage gap**||-0.4||
|**_Farm Segment:_**||||
|**Skilled Labor**|-2.0|-1.4|87|
|**Unskilled Labor**|-4.5|-3.9|100|
|**Capital**|4.1|4.7|92|
|**Sk/Unsk wage gap**||2.6||
|**_Price indexes:_**||||
|**Food price index**|-1.4||39|
|**Non food price index**|0.0||-1|
|**CPI**|-0.6||24|

Sources: Author calculations from model results. Notes: The first column, Δ **P** , represents the percent variation of the price of each factor with respect to the initial levels, and Δ **(S/CPI)** is the percent variation of the price deflated by the Consumer Price Index. The column, **% of Full Lib** , shows the percent ratio of the real price changes in the CAFTA scenario with respect to the unilateral non discriminatory full liberalization case.

In summary, the impact on factor remunerations of the examined trade reforms, full liberalization and CAFTA agreements, should be positive for urban workers, both wage-employed or self-employed with physical capital, but it may, at least temporary, be negative for wage workers of the rural sector. Furthermore, the regional agreement replicates this negative effect with potential worrying implications for inequality and poverty. For agricultural households receiving part of their income from capital and land, or even from non-farm activities, the unfavorable farm wage changes should have a less harmful effect. Notice also that the wage gap between skilled and unskilled workers does not significantly change with this kind of trade reform.<sup>22</sup>

# **_<u>Decomposing the CAFTA scenario</u>_**

In order to distinguish the effects of market access from those of own tariff unilateral abatement, the simulated reciprocal CAFTA trade agreement has been decomposed into two separate reforms: in the first, Nicaragua unilaterally eliminates all

> 22 This outcome may not hold under a different production specification where skilled workers, for example, are modeled as a complement to capital, rather than as substitutes.

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# **Table 10**

# **5-year Panel Growth Regressions**

||(1)<br>**OLS**|(2)<br>**IV**|(3)<br>**FE**|(4)<br>**GMM**|(5)<br>**GMM**|(6)<br>**GMM**|(7)<br>**GMM**|(8)<br>**GMM**|
|---|---|---|---|---|---|---|---|---|
|Log of GDP pc, beg. period|-0.014***|-0.027***|-0.039***|-0.017***|-0.015**|-0.019***|-0.012|-0.007|
||(0.003)|(0.005)|(0.007)|(0.006)|(0.007)|(0.005)|(0.008)|(0.007)|
|Log of EXPY, beg. period|0.034***|0.071***|0.017|0.041***|0.046***|0.051***|0.038**|0.035**|
||(0.006)|(0.017)|(0.012)|(0.013)|(0.016)|(0.015)|(0.015)|(0.017)|
|Log of % secund. school att., beg. period|0.005***|0.001|-0.003|0.006|0.004|0.007|0.008*|0.009|
||(0.002)|(0.003)|(0.003)|(0.004)|(0.005)|(0.006)|(0.005)|(0.007)|
|Constant|-0.170***|-0.380***|0.221**|-0.206**|-0.268***|-0.291***|-0.223**|-0.247**|
||(0.034)|(0.101)|(0.091)|(0.086)|(0.099)|(0.107)|(0.095)|(0.115)|
|Observations|620|604|620|604|604|604|604|604|
|Number of group(ecode)|||79|77|77|77|77|77|
|Instruments||||91|70|52|31|28|
|R-squared|0.45|0.40|0.52||||||
|First stage F-statistic of excluded instrument p-value<br>(H0: underidentified/weakly identified)||0.000|||||||
|Hansen J statistic p-value (H0:overidentified)||0.009||0.750|0.320|0.213|0.019|0.022|
|Stock-Wright S-statistic p-value (H0: all endogenous<br>regressors==0 and overid. rest. valid, weak-instrument-<br>robust inference)||0.000|||||||
|AB test for AR(2) in first diff. p-value (H0: No<br>autocorrelation)||||0.066|0.179|0.785|0.063|0.192|

Robust standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% All equations include period dummies. IV regressions use log population and log land area as instruments. FE regressions include dummies for countries. GMM is Blundell-Bond System GMM estimator using lagged growth rates and levels as instruments as well as log population and log land area.

35
in the process of transition to a market economy, a consideration not captured in the table.

## **Table 8.  Identifying the role of inequality drivers with decomposition results**

- **Drivers** • **Decomposition by sources**

- **Decomposition by groups**

- Own wage inequality increase **↑**

- • 1. Wage decompression • Fall in the share of wages in and the growth of private incomes  ↓

- sector • Coefficient of concentration  for

- • 2. Restructuring , wages **↑**

- unemployment/or • Increase in share of informal reverting to subsistence incomes **↑**

- economy •

- • 3. Fiscal adjustment • Changes in the real value of affecting Government transfers↓ **↑** expenditure and taxation • Changes in targeting ↓ **↑**

- • 4. Price liberalization, • “Excess” inequality in wages inflation and arrears • Property incomes increase **↑**

- • 5. Asset transfer and • Entrepreneurial income increase property incomes **↑** • Imputed rents ↓

- • 6. Technological change and expansion of • Returns to education **↑** knowledge economy, • Increase in variation of returns **↑** migration

- Increase in private sector and in unemployment **↑**

- Increase in the number of subsistence farmers **↑**

- • Increase in the number of unemployed ↑

- • Inequality among transfer recipients **↑**

- • Excess inequality among fixed income recipients (transfer and State sector workers) ↓ **↑**

- • Increase in the number of selfemployed **↑**

- • Migration to urban areas ↓ • Education expansion ↓ • Premium for highly skilled **↑** • Inequality among the skilled **↑**

**↑-** Inequality increasing **,** ↓- inequality decreasing.

This complexity of interactions between the determinants of inequality results in a clear conclusion, viz., there are no common, all-encompassing explanations for the increase and, in some cases, subsequent decline in inequality in the transition countries across periods of economic decline and growth. The analysis of the paper also suggests that initial conditions and policy choices have been important in shaping the outcomes.

# **VI. CHANGING INEQUALITY IN CHINA**

This paper started with a comparison of rising inequality between China and Russia and the suggestion that these two phenomena may be more closely linked than usually thought. Table 9 demonstrates that increasing inequality in China is as firmly established a fact as rising inequality in the transition countries of Eastern Europe and the former Soviet Union.<sup>16</sup> In what follows, it should be noted however that, unlike in the case of

> 16 It is worth noting that figures in Table 9 are widely believed to be underestimates for urban areas.  This is because the urban sample of the national survey includes only permanent residents and migrants with

33
Do Natural Resource-Based Industrialization Strategies Convey Important (Unrecognized) Price Benefits for Commodity Exporting Developing Countries?

by Alexander J, Yeats

# Table of Ccntents

|1.<br>Introduction|I|
|---|---|
|11.<br>The Methodological Approach|3|
|Ill.<br>Secular Changes in Commodity Trade|5|
|IV.<br>Price Formation for Primary and Processed Commodities|1 9|
|V<br>Summary and Conclusion|23|
|References|25|
|Appendix|2 7|
# **Notes**

1 Richerson, Boyd, and Bettinger 2001. 2 Angel 2012. 3 Zeng 2010. 4 Ballon 2012. 5 Zhao 1993 6 UNDESA 2011, 2012. 7 World Bank 2012. 8 Clemens, Montenegro, and Pritchett 2008. 9 Clemens 2011.

11