rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Were you able to return home? Why did you decide to return?<br>economic necessity<br>Yes family/friends<br>security improved<br>No destination worse<br>state helped return<br>0% 20% 40% 60% 0% 10% 20% 30% 40% 50%<br>Percent of respondents Percent of respondents<br>(N = 730) (N = 399)<br>Length of time until return Why were you unable<br>(in years) to return?<br>50<br>security concerns<br>40<br>30 property destroyed<br>20 can't recover land<br>10<br>destination better<br>0<br>0 5 10 15 20 25 30 35 40 45 50 55 0% 10% 20% 30% 40% 50%<br>Percent of respondents<br>(N = 327)<br><!-- End of picture text --> Figure 4: Summary of descriptivecharacteristics concerningability ofdisplaced toreturn home.

Among those who never return, security concerns and an inability to recover property (either because it was destroyed, or because of legal issues) are the dominant obstacles.<sup>23</sup> Insecurity in the countryside is driven by various factors, including the violent expansion of armed groupsintoareaspreviouslycontrolledbytheFARC(Premetal., 2020). Suchinsecuritycanlimit the ability of victims to return, even for those who are already relatively near. Team interviews with a _Yupka_ indigenous community in the department of Cesar, for instance, highlighted how paramilitary groups continue to occupy Yupka land and forbid access to state officials working to map out the boundaries of the community’s territory.<sup>24</sup> The return of IDPs to areas where property rights are characterized by informality can also generate social conflict between different groups laying claim to the same land. In Colombia, landconflictsbetweenindigenousgroupsandpeasantshavesurgedoverquestionsofwhoowns what (de Benito, 2015). These patterns were echoed in our interviews with members of the _Yupka_ community, who argued that returning IDPs were creating conflict because “they [the 23ThesefindingsechoreportsbytheConsultancyonHumanRightsandDisplacement(CODHES),whoina2014 study found that security concerns are among the most important barriers to IDP return (CODHES, 2015). 24Research team focus group discussions in Valledupar, Cesar. March 27th, 2017.

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# **TRADE REFORMS AND WELFARE: AN EX-POST DECOMPOSITION OF INCOME IN VIETNAM**

## **Aylin Isik-Dikmelik**<sup>∗</sup>

## **Abstract**

This paper analyzes the impact of trade reforms on household welfare. In particular, the importance of each of the links that together constitute the impact is studied using data from the Vietnamese experience in the 1990s. The implementation of trade reforms in the 1990s, most noteworthy of which was the liberalization of rice, resulted in a substantial improvement in welfare as evidenced by the drastic decline in poverty. Using analytical and empirical methods, we examine the role of each channel (direct vs. indirect) in this improvement for different groups of households. Results indicate that the growth has been broad based and pro-poor. Poorer households experienced more growth for each and every group analyzed. Moreover, contrary to the standard literature, net buyer households had more growth compared to net sellers, emphasizing the importance of the indirect links. Decomposition of the growth illustrates that for rural households, both the direct effects and the multiplier effect drive growth while the multiplier effect was key in urban areas. The importance of the secondary effects underscores the need for a broader model to estimate the impact of trade reforms fully.

World Bank Policy Research Working Paper 4049, November 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ∗ Development Economics Research Group, World Bank. Email: aisikdikmelik@worldbank.org. I would like to thank Ataman Aksoy, Alessandro Nicita, and Marcelo Olarreaga and an anonymous referee for helpful comments.

1
**Entrepreneurship and the Extensive Margin in Export Growth: A Microeconomic Accounting of Costa Rica’s Export Growth during 1997-2007**<sup></sup>

Daniel Lederman World Bank Andrés Rodríguez-Clare Penn State University Daniel Yi Xu New York University

>  The authors gratefully acknowledge financial support from the World Bank’s Latin American and Caribbean Regional Studies Program under the project on the “Quality of Trade” led by William F. Maloney and from the Costa Rica Competitiveness Project led by José Luis Guasch, Thomas Haven, and José Guilherme Reis. The following colleagues provided useful insights and guidance during preliminary discussions: José Luis Guasch, Porfirio Guevara, Bill Maloney, Martha Denise Pierolo, Roberto Alvarez, and Augusto de la Torre. Javier Cravino and Oana Luca provided stellar research assistance at various stages. Ronald Arce (PROCOMER), Francisco Gamboa (Research Director, PROCOMER), and Ricardo Matarrita (INCAE, formerly with PROCOMER) spent time providing and explaining the PROCOMER data during recent years.
regulator. The third-party inspector follows the same procedure described above, taking three samples and measuring approximately 200 individuals in total. The landings data are lower resolution than the electronic logbook data because third-party inspectors measure percentage juvenile from the sum of anchoveta caught by all sets on a fishing trip (average number of sets per trip is 2.2), whereas fishers measure percentage juvenile after each set in the electronic logbook data. However, unlike vessels in the electronic logbook data, the closures policy does not give third-party inspectors an incentive to misreport percentage juvenile because the regulator does not use landings data to determine closures during my study period. Third-party inspectors are from one of three international firms and tend to have more rigorous technical training in measurement and sampling than fishers (PRODUCE, 2018 _d_ ).

Even if percentage juvenile reported by vessels in the electronic logbook data is unbiased, I cannot calculate juvenile catch as percentage juvenile times tons caught because the weight of an anchoveta is approximately cubic in its length (IMARPE, 2019). I therefore obtained a supplementary electronic logbook dataset for a group of vessels that report length distribution data to their owners (but not to the regulator) (Englander, 2022). These vessels represent 56% of tons landed. For each two-week-of-sample by two-degree grid cell, I calculate the individuals-weighted average proportion of individuals in each half-cm interval caught by these vessels. Then I impute length distributions for sets by vessels that do not report these data to their owners. For sets with percentage juvenile above (below) the individuals-weighted average percentage juvenile for their two-week-of-sample by two-degree grid cell, I inflate (deflate) the proportion of individuals below 12 cm and deflate (inflate) the proportion of individuals above 12 cm so that the imputed length distribution for each set implies a percentage juvenile equal to the percentage juvenile reported for that set.

Next, I match all sets to landing events in order to correct the percentage juvenile, length distribution, and number of individuals caught by all sets. When the individuals-weighted average percentage juvenile across sets on a trip does not equal the percentage juvenile 14
With a clear allusion to the social dynamic that generated the “Arab spring,” Richard Dowden on The Guardian (2013) reminds us that “young middle class Africans are not enjoying the benefits of this optimism in Africa.” He goes on to describe young middle class Africans as “unhappy with the poor levels of education, the lack of electricity, and corruption,” and the generalized sense of dissatisfaction with the government’s action. The author concludes that there is a large number of unemployable young people being churned out of badly-managed state education systems, adding to the increasing number of poor in the population and as the title states, this middle class “calls for revolution.”

# _Middle class in Nigeria_

The discussion on the middle class is particularly vibrant in Nigeria. In 2007 the National Bureau of Statistics (NBS) released a report titled “The Middle Class in Nigeria: Analysis of Profile, Determinants and Characteristics” (1980-2007). The study divides the middle class into lower-middle and upper-middle class and defines the middle class profile based on consumption patterns, and the typical occupation and educational attainment. Among the relevant findings, the report highlights that in recent times, due to unemployment and government shrinking, the middle class cuts across formal, informal, public and private sectors, while before the nineties they were mainly concentrated in the public sector.

A more recent report edited by McKinsey (2014) is very optimistic about the Nigerian middle class’s (here called consumer class) future prospects. In 2013, the report estimates eight million households with incomes of more than $7,500 per year (about 4$ per capita), a level sufficient to meet all basic necessities and have money left over to start buying more and better food as well as health and education services. By 2030, McKinsey estimates that about 35 million households or about 60% of future Nigerian Population could be living above this threshold.

Like other African countries, the growth of the Nigerian middle class is mirrored by a widespread modification of consumption patterns. An increasing number of foreign restaurants and lifestyle companies are entering the country, in particular supermarkets and restaurant chains, undoubtedly the epitome of Western-type middle class consumerism. In the last years, some Nigerians have shifted from shopping at local markets and street vendors to progressively purchasing at the local-based commercial centers, malls and markets. The Palms in Lagos Lekki Peninsular was the first western-style mall built in Nigeria, now there is also the Lagos City Mall and the Silverbird Galleria in Lagos and Abuja. In Abuja, there is also 8
**31**

seek to identify an optimal package of pension plan practices for all systems across all time periods. However, it did indicate that professional actuarial and investment counsellors must be sought, rather than relying on employees to chose investment strategies which will guarantee eventual benefit payments. (There was no persuasive evidence that hiring a money manager from the most active segment of the market would generate much better portfolio performance). The research also suggested some of the enormously complicated issues that must be confronted when seeking to establish funding norms for defined benefit pension plans in the public sector. The Government Accounting Standards Board in the U.S. has devoted several years to the development of a standardized framework to be used for reporting public pension plan liabilities and assets; their work would be beneficially reviewed by those designing public pensions in developing countries.20 In general, if full, or near full, funding of public pension plans is contemplated, it would appear useful to require standardization of pension reporting practices. This is particularly true regarding the myriad assumptions needed to cost out a defined benefit plan's promised obligations, including the amortization penod over which past liabilities must be met. This is often a difficult task, inasmuch as defined benefit pension rules determining benefit eligibility and amounts frequently differ from one group of employees to another, as well as across cohorts. Sometimes benefit and contribution regulations have internally inconsistent objectives. Frequently in a developing country there may be insufficient actuarial data to derive assumptions needed for projecting expected labor force patterns, and forecasts of economic trends are also inherently uncertain. For all Olivia S. Mitchell - Public Pension Governance and Performance
**<u>Table 9. The heterogenous impact of the program on nonfarm employment of individuals</u>**

||||Dependent var|iable is the du|mmyof having|nonfarm work|||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program * Post * Male|0.1027***<br>(0.0149)||||||||
|Program * Post * Ethnic minorities||-0.0876**<br>(0.0346)|||||||
|Program * Post * The number of|||0.0086***||||||
|schooling years|||(0.0019)||||||
|Program * Post * Dummy variable||||0.0377*|||||
|indicating VHLSSs since 2006||||(0.0217)|||||
|Program * Post * log of distance|||||-0.0084||||
|from village to the nearest town|||||(0.0108)||||
|Program * Post * Village has all-||||||0.0128|||
|weather roads||||||(0.0257)|||
|Program * Post * log of distance|||||||-0.0167||
|from village to the nearest market|||||||(0.0110)||
|Program * Post * log of distance||||||||-0.0076|
|from village to the nearest bank||||||||(0.0098)|
|Program * Post-program period|0.0366|0.1603***|0.0343|0.0677|0.1136*|0.0830|0.1338**|0.1153**|
||(0.0512)|(0.0518)|(0.0566)|(0.0520)|(0.0603)|(0.0529)|(0.0601)|(0.0569)|
|(Poverty rate – 50) * Post-program|-0.0095**|-0.0095**|-0.0099**|-0.0094**|-0.0094**|-0.0094**|-0.0094*|-0.0098**|
|period|(0.0047)|(0.0047)|(0.0047)|(0.0047)|(0.0047)|(0.0046)|(0.0051)|(0.0047)|
|Program * (Poverty rate – 50) *|0.0072|0.0075|0.0082|0.0072|0.0073|0.0071|0.0066|0.0072|
|Post-program period|(0.0052)|(0.0052)|(0.0053)|(0.0052)|(0.0052)|(0.0052)|(0.0056)|(0.0052)|
|Interacted variables|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Other control variables|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|-0.0460**|-0.0619**|-0.2006***|-0.0547**|-0.0136|-0.0747***|-0.0406|-0.0218|
||(0.0233)|(0.0238)|(0.0249)|(0.0235)|(0.0301)|(0.0235)|(0.0258)|(0.0273)|
|Observations|128,446|128,446|128,446|128,446|128,446|128,446|103,971|128,446|
|R-squared|0.198|0.197|0.212|0.196|0.197|0.197|0.189|0.199|

Note: This table reports district fixed-effect RDD-DD regressions of nonfarm employment of individuals using individual-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

48
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 22. South Sudan’s infrastructure spending needs in a regional context, as a share of GDP**

Estimated infrastructure spending needed to meet targets, as percentage of GDP <!-- Start of picture text --> SSA<br>LIC-Fragile<br>COMESA<br>LIC-NoFragile<br>Resource-Rich<br>MIC<br>Congo, Dem. Rep.<br>Ethiopia<br>South Sudan<br>Kenya<br>North Sudan<br>Uganda Capex O&M<br>South Africa<br>Botswana<br>% of GDP 0 10 20 30 40 50 60 70<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009. _Note:_ Values excludes irrigation. LIC = low-income country; MIC = middle-income country; COMESA = Common Market for Eastern and Southern Africa; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure.

China has an active presence in neighboring countries and is yet to make substantial investments in South Sudan. The largest single financier of infrastructure in Sudan is China, followed by India and Arab countries. Together these account for 40 percent of Sudan’s total annual investments, almost entirely allocated to the power sector.

**Table 20. Financial flows to South Sudan’s infrastructure, average 2001 to 2005**

||O&M||Ca|pital expenditure||||GDP shares|
|---|---|---|---|---|---|---|---|---|
|$ millions per year|Public<br>sector|Public<br>sector|ODA|Non-OECD<br>financiers|PPI|Total<br>CAPEX|Total<br>spending|Total<br>spending|
|ICT|0.6|0.0|—|0.0|49.5|49.5|50.2|0.83|
|Irrigation|—|—|—|—|—|—|—|—|
|Power|18.4|12.6|—|0.0|0.0|12.6|31.0|0.51|
|Transport|13.0|349.7|—|0.0|3.3|353.1|366.0|6.08|
|WSS|4.2|0.2|—|0.0|0.0|0.2|4.4|0.07|
|Total|36.2|362.5|—|0.0|52.9|415.4|451.6|7.50|

_Source:_ Derived from Foster and Briceño-Garmendia (2009).

_Note:_ O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development; WSS = water supply and sanitation; ICT = information and communication technology; GDP = gross domestic product.

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education, a policy of efficiency pricing (i.e., where the proportion of subsidy was highest for primary school and lowest for university) could also yield a major reduction in the sectoral deficit. (This ignores distributional concerna, which are discussed below).

# Shadow Pricing

66. In an, economy with major price distortions, the efficiency price of a public sector good &ust be calculated on the basis of the real resource cost, or shadow price, of its inputs or replacement. This calculation is needed both for least-cost evaluations and as a benchmark for domestic pricing policy. Of course, where economy-wide price distortions are large, second-best considerations may require that the domestic price diverge from its efficiency level. Nonetheless, the efficiency price of natural **gas** in **a** case **where** it substitutes for imported fuel oil at the margin, **is the** border price of the fuel oil, not its domestic market price. The efficiency price of electricity produced in that economy with fuel oil would also be based on the border price of fuel oil even though the power company actually **pays the** domestic market price. If the electricity generators **are** imported, their cost should be calculated at the true border price of foreign **exchange** for **the** economy, which may be different from the official exchange rate.

67. These principles are well known, and about 45% of the SARs reviewed used at least one shadow price in their internal rate of return calculation. Labor was shadow priced in 28% of the projects, including 100t of the irrigation projects. A foreign exchange conversion factor was used in 11% of the projects, and some form of direct border price was used in a further 29% of projects. However, it **is** curious that none of the 31 health and education projects reviewed mentioned shadow pricing. This may be because rates of return are not generally calculated in these sectors, and little disaggregated cost data were available in the SARs. Table 2 sumarizes the results of shadow pricing by sector.

Table 2. Percent of grojects using shadow Drices

||in IRR<br>calculation|for<br>labor<br>1Rdi|for forei<br>border|gn<br>exchange<br>conversion<br>factor|
|---|---|---|---|---|
|Irrigation|100|100|100|0|
|Railways|86|43|43|28|
|Coal|80|80|80|0|
|Oil/gas|70|0|60|10|
|Power|65|40|55|5|
|Ports|62|25|12|38|
|Fertilizer|60|60|60|0|
|Roads|36|4|4|27|
|Telecommunications|33|17|0|17|
|Water/sewerage|29|29|0|7|
|Housing|14|14|0|14|
|Education|0|0|0|0|
|Health|0|0|0|0|
linked to indicators of financial development.<sup>9</sup> We use the average from 1981 to 2006 (rather than from the actual year of the WBES survey) because data on trust are available for distinct years for different countries, and relying on the survey year would lead to too many missing values for trust for our countries. Averaging also has the benefit of smoothing the measurement errors of trust.

Zak and Knack also note that their framework can help account for other findings from the cross-country growth literature. For example, the negative link between ethnic fractionalization and growth found in Easterly and Levine (1997) and between income inequality and growth in Easterly (1999) are both substantially weakened when levels of interpersonal trust are included in regressions. Although the channels through which social ties and the institutional environment affect trust are more clearly laid out than for the polarization measures, we include measures of ethnic and linguistic fractionalization among the set of potential instruments. We expect them to be negatively linked to financial development indicators.

# **_Legal Origin_**

LaPorta, Lopez-de-Silanes, Shleifer, and Vishny (LLSV) (1997, 1998) first documented that the legal protection of outside investors was stronger in common law countries (English legal origin) than in civil law countries rooted in Roman legal traditions, particularly those of French legal origin. Their interpretation was that English common law traditions evolved to protect private property and support private market outcomes, whereas French civil law tended to unify the legal system and cement state control of the judicial system often to the detriment of private market development. Because legal traditions were introduced in most countries by outside colonizers, LLSV argued that legal origin could be treated as exogenous in regressions explaining financial development and growth. Using legal origin as an instrument for investor protection, they then demonstrated a strong link between those protections and measures of financial development. We therefore include dummy variables for English and French legal origins in the set of potential instruments. We expect English (French) legal origin to be positively (negatively) linked to financial development.

> 9 The measure is based on information from the World Values Survey.  See Knack and Zak (2001) for details of its construction.

14
# **Figure C.1. Probabilities of Female-Headed Households Escaping Poverty in Second Year Conditional on Being Poor in First Year (percentage)**

<!-- Start of picture text --> Egypt 2017-2020 Iraq 2007-2012<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>Jordan 2010-2013 Mauritania 2014-2019<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>Palestine 2011-2017 Tunisia 2015-2021<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>FHH wo/children FHH w/children Non-FHH<br>75<br>45<br>70<br>40<br>65<br>35<br>Percentage (%) Percentage (%)<br>60<br>30<br>60 55<br>55 50<br>50<br>45<br>Percentage (%) Percentage (%)<br>45<br>40<br>40 55<br>35<br>50<br>30<br>45<br>25<br>Percentage (%) Percentage (%)<br>20 40<br><!-- End of picture text --> Note: Estimates are obtained with synthetic panel data and weighted with population weights where the second survey round is used as the base year. The figure shows the percentage of the population that moves out of poverty in the second year. FHH characteristics are measured in second period. Dashed red lines represent the national average for each period. Household heads' ages are restricted to 25-55 for the first survey round and adjusted accordingly for the second survey round. Standard errors are estimated with 1,000 bootstraps. ’s are estimated using Equation (C6). ρρ 92