rafmacalaba/datause-encoder-data huggingface dataset:
We emphasize that our results also hold when assuming that utility is _strictly_ increasing in relative income, instead of _weakly_ increasing. Indeed, Theorem 1 would be unchanged if the subset _U_<sup>_σ_ ̄</sup> excludes _u_<sup>0</sup> .<sup>41</sup>

# **6 Empirical Application**

In this section we apply our proposed index, the hierarchical headcount ratio ( _HHS_ ), to measure global poverty. The goal of this exercise is to illustrate how the switch of index can affect the assessment of poverty. We compare the evaluation of poverty according to the hierarchical headcount ratio both with the societal headcount ratio ( _HS_ ) using the same global poverty line, and with the absolute headcount ratio ( _HA_ ), arguably the most well-known poverty measure. We compare poverty at the country level as well as the evolution and distribution of global poverty. Besides the theoretical arguments for moving away from the societal headcount ratio, ultimately, such a change of index would be justified only if it significantly affects the measurement of global poverty.

## **6.1 Data and Parameters**

Our source of data is PovcalNet,<sup>42</sup> the on-line tool for poverty measurement developed by the World Bank, which offers income or consumption data from more than 1500 household surveys across more than 160 countries. In order to allow for cross-country comparisons, the World Bank converts income or consumption using the 2011 PPP exchange rates for household consumption from the International Comparison Program. Moreover, for the purpose of performing multi-country aggregations the World Bank defines reference years aligning survey estimates from different years. To analyze poverty at the global or regional level, we only use reference years.<sup>43</sup> We use data from 1999 up to 2015, the most recent reference year available. We take 1999 as our base year because before that, we have _HHS_ = _HS_ in several populous countries that still had median income below _<u>y</u>_<sup>_~~z~~_</sup> . Our dataset includes 168 countries.<sup>44</sup>

> 41Details can be found in online Appendix S11.

> 42 PovcalNet can be found at: http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx. The data was retrieved in April 2021. All data is in per-capita terms.

> 43The reference years available from 1999 on are: 1999, 2002, 2005, 2008, 2010-2013 and 2015.

> 44China, India, and Indonesia compile data for rural and urban areas separately. Thus, we have 171 units in total. A few countries do not have data for all reference years. These countries are: Kosovo (missing data in 1999), Nauru (missing data before 2005), Somalia (missing data before 2011), South Sudan (missing data before 2008), Timor-Leste (missing data in 1999), and

33
49

**Table 5.3: Evolution of (Real) Per Capita Expenditure by Type and Geographical region (Poverty Line=N395.41)**

||**NORTHE**|**RN BELT**|**MIDDL**|**E BELT**|
|---|---|---|---|---|
|**Expenditure Type**|**85/86**|**92/93**|**85/86**|**92/93**|
|Own Consumption|54.72|168.24|49.77|344.92|
|Food Cash Expend.|309.14|271.23|324.37|142.50|
|All Food Expend.|363.86|439.47|374.14|487.42|
|Non Food Expend.|174.54|194.44|177.50|266.14|
|P. C. Expend.|538.40|633.91|551.64|753.56|
||**SOUTHE**|**RN BELT**|**ALL NI**|**GERIA**|
|Own Consumption|39.46|304.18|44.74|263.55|
|Food Cash Expend.|371.10|317.15|346.89|267.27|
|All Food Expend.|410.56|621.33|391.62|530.82|
|NonFoodExpend.|245.86|313.15|201.19|261.82|
|P. C. Expend.|656.42|934.48|592.81|792.64|
|Source: NCS, 1985/86 and|1992/93.||||

# **6. CONCLUDING REMARKS**

Poverty declined sharply from 43 percent of the population in 1985 to 34 percent of the population in 1992. However we observe that, despite this, some households suffered a loss in incomes. The top 10 percent of the population had more than 47 percent increase while the bottom 20 percent of households experienced absolute decline by more than 20 percent. The latter group form the extreme poor of Nigeria who have been adversely affected in terms of poverty and welfare during this period Although growth reduced poverty, the distribution of income has worsened during the same period. This is particularly true for the rural sector and the extreme poor for whom the depth and severity of poverty increased between 1985-92. If income distribution had remained unchanged, the national incidence of poverty would have declined further by another 4 percent. The Lagos urban area is the only region where income distribution improved. Hence, for all other regions growth without improvements in income distribution have adversely affected the extreme poor in Nigeria.

Poverty declined in Nigeria mainly due to the 34 percent growth in household expenditures which was a result of economic growth. However, the regional pattern of household expenditure growth is varied in different parts of the country. While the south and middle zones had higher growth rates of household expenditures, the northern zone experienced hardly any growth. This particularly evident in the rural areas of the north.

Poverty in Nigeria differs depending on socio-economic groupings. Those with no education comprise the poor and extreme poor, and this is very true in rural areas. If the household head is older, the probability of being poor increases. Wage workers and self-employed are often poor. Households which are large and families with large number of children are more frequently poor.
population level sampling weights.

The CPHS’ sample has evolved over time with household dropping out of the original panel and new replacement households being added. A notable number of households were deleted and added to the CPHS panel during the first five waves of data collection (Figure 1). For that reason, we begin our analysis of CPHS data from 2015-16.<sup>9</sup> There are large net additions to the rural panel during the third wave of 2017. The number of sampled districts increased from 422 to 503 between the second and the third wave of 2017. The newly added districts are concentrated in the comparatively poor and rural areas of the country (with a 2011 mean household consumption per capita that is 18 percent lower when compared to the districts that were already part of the sample).

Response rates in the CPHS vary between 80.6 and 87.6 percent over the 2014 to 2019 period. The highest non-response rates are observed during the pandemic-induced lockdown of 2020. The fraction of households from the first wave of 2014 that remained in the panel until December 2019 is 16.9 percent.<sup>10</sup> On average, the probability that a household will survive the panel is halved after about 7 waves of data collection. Further information on the sample is available on the CPHS’ official website.

# **2.2 NSS surveys and other data sources**

We use a range of secondary data sources to correct for biases in the CPHS and to validate our estimates of poverty and inequality for the 2015 to 2019 period.

_NSS consumption surveys_ : The 68<sup>th</sup> round of NSS conducted between July 2011 and June 2012, is the latest official source of consumption data publicly available for India. The survey reports consumption expenditure values with a 30-day recall period<sup>11</sup> and consists of a sample of over 100,000 households spread across all Indian states. Survey estimates are representative at the district level. The poverty headcount rate at the $1.90 poverty line is 22.49 percent and the Gini coefficient is 35.71 using consumption per capita based on uniform recall period. We also use select moments derived from the leaked cumulative distribution function that is estimated from the 2017 NSS consumption expenditure survey round for robustness checks.

_Other official surveys_ : Despite there being no contemporaneous NSS and CPHS expenditure surveys, there are three official non-expenditure surveys that allow us to 9Vyas (2020) offers a detailed account of the execution challenges by the survey team until the first wave of 2015, especially related to inclusion of excess CEBs in the urban sample.

> 10CMIE makes an attempt to revisit households that are locked on the same day or sometimes the next day in villages. In urban areas, repeated re-visits are conducted spread over several days. If households are consistently locked or unoccupied over three waves, they are dropped from the panel.

> 11We continue the existing practice of measuring poverty and inequality from older NSS rounds based on the uniform recall period (URP)

6
- (a) You: <u>$</u>

- (b) Fundaci ́on Juntos se Puede. Organization that supports Venezuelan immigrants to access health, education, and legal advice: $

- (c) Un techo para mi pa ́ıs. Organization that builds houses for vulnerable populations in Colombia: <u>$</u>

# **VII. Trust**<sup>27</sup>

For the following statements please choose one answer:

1. One can trust other people.

- a. Strongly disagree

- b. Disagree

- c. Agree

- d. Strongly agree

2. In general, other people have good intentions toward me.

- a. Strongly disagree

- b. Disagree

- c. Agree

- d. Strongly agree

3. One can rely on other people, even if one does not know them well.

- a. Strongly disagree

- b. Disagree

- c. Agree

- d. Strongly agree

4. One can trust in Venezuelan migrants.

- a. Strongly disagree

- b. Disagree

- c. Agree

- d. Strongly agree

# **VII. Empathic Concern and Perspective Taking Scales**<sup>28</sup>

The following statements inquire about your thoughts and feelings in a variety of situations. For each item, indicate how well it describes you by choosing the appropriate letter. On the scale, where A “Describes me very well” and E “Does not describe me well”. Please read each item carefully before responding. Answer as honestly as you can.

participants of this study to each organization. While donating, REBEL will explain to the organizations that the funds come from a study, without giving any details about who donated, or the relationship of the Inter-American Development Bank and the University of Southern California with the study. After doing this, REBEL will send a proof to all the participants that the donations have been done.

> 27Questions 1 to 3 are from Kosse et al. (2020) and have been experimentally validated by Fehr et al. (2002) and Falk et al. (2018)

> 28Questions are taken from Davis (1980, 1983)

68
- Quah D. (1997), "Empirics for growth and distribution: Stratification, polarization, and convergence clubs", _Journal of Economic Growth_ , Vol.2, No.1, pp.27-59.

- Quah D. (1999), "Ideas determining convergence clubs", site web _: http://econ.lse.ac.uk/staff/dquah/p/9907ccb.pdf_

- Redding S., Venables A.J. (2000), "Economic geography and international inequality", _CEPR working paper 2568._

- Renard M.F. ed. (2002), _China and it regions: Economic growth and reform in Chinese provinces_ , Edward Elgar.

- State Statistical Bureau of the People's Republic of China, _China Statistical Yearbook_ (19861999), China Statistics Press _,_ Beijing.

- Yilmaz S., Haynes K.E., Dinc M. (2002), “Geographic and network neighbours: spillover effects of telecommunications infrastructure”, _Journal of Regional Science_ , Vol.42, No.2, pp.339-360.

- Zhang Q., Felmingham B. (2002), “The role of FDI, exports and spillover effects in the regional development of China”, _Journal of Development Studies_ , Vol.38, No.4, pp.157-178.

26
with shocks to the local economy, but may yet be systematically related to other features of the local economy or village policy. As some minor differences in village characteristics are apparent in 1988, the modal year of ID distribution, we control for fixed village unobservables with a village fixed effect.

Apart from fixed unobservables, like geographic proximity to cities, one might remain concerned that the timing of ID distribution is systematically related to other time-varying village level policies or administrative capabilities. In turn, these policies may affect both migration and high school enrollment decisions. For example, village leaders have considerable control over implementation of grain procurement policy and land use by village residents, and so it is of interest to know whether decisions regarding these policies are systematically related to the timing of ID distribution. If a systematic relationship exists, the instruments could proxy for factors other than migrant opportunity that influence the high school enrollment decision.

One might imagine estimating logit hazards as in our test of whether rainfall shocks influence ID distribution, but such an approach has less appeal because we lack 30 years of village data to construct proxies for policies, and further, because policy changes may be endogenous. We thus look for direct evidence of whether our instrument is correlated with proxies for time-varying village policies and administrative capacity. These proxies, VPjt, are each regressed on the quartic in years-since-IDs, village fixed effects, province-year effects, and household and village characteristics: In Table 5, we report _F_ -tests on the quartic in years-since IDs were issued in specifications that both exclude (column 1) and include the vectors of household and village level characteristics (column 2). Initially, we examine local implementation of the grain quota policy. The grain quota, which was phased out between 2001 and 2004, was effectively a tax in which farm households were forced to provide some grain to the government at below market price. The quota also constrained the decisions of households in those villages in which households are unable to provide cash or purchased grain in lieu of grain that the family had produced. When the quota share of grain produced is closer to one, quota policy is more likely to be driving the decision to continue growing grain crops. In rows 1 and 2, we observe that the quota share of grain produced has no systematic relationship with the years-since-IDs were issued.

16
WPS4110

# **Factors Impacting Youth Development in Haiti**

By

**Michael Justesen and Dorte Verner**

World Bank<sup>1</sup>

## **Abstract**

Of the 1.6 million Haitian youth aged 15–24, only 13 percent are content with their lives. More than half of 20-year-olds have not completed secondary education and nearly half of youth in the labor market are unemployed. This paper investigates protective and risk factors predisposing youth to positive and negative behaviors. These factors, including poverty, gender, education, labor market, migration, family, health, and violence, are examined by the use of statistics and probability models based on Haiti’s first household living conditions survey. Key findings show that female youth need special attention because they are more likely than their male peers to drop out of school and to be unemployed or inactive. Role models, guidance, expectations, and contacts in the form of parents or household heads are decisive factors in keeping youth in school and to some extent in their finding employment. In addition, domestic migration has a positive impact on the probability of being employed and inactive (positive self-selection), while marriage, drug abuse, and domestic violence increase the probability of dropping out of school.

**Keywords:** Youth at risk, risk and protective factors, education, labor market, household survey, probability model.

World Bank Policy Research Working Paper 4110, January 2007 _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._

> 1 We are grateful to Susanna Shapiro for helpful comments and Willy Egset for support with the household dataset.
inequality and regional development policy has been the European Union (EU). As part of the economic and political integration process, the EU has put particular emphasis on regional inequality and devoted considerable resources to helping poorer regions catch up with richer ones, especially beginning in the late 1980s. The EU experience has been studied in detail by several different researchers over the past few decades. This has created a rich literature, and we do not intend to add to it an independent piece of analysis based on primary data. However, there is an opportunity for a holistic look at all the empirical evidence gathered and analyzed so far to draw out key insights that would address the two fundamental questions on regional inequality we have set out before. In this paper, we will attempt just that. We believe that this paper will not only add to the theoretical debate on regional inequality and regional development policy, but it will also help current and future policy makers as they grapple with regional development challenges both in the expanded EU and in many different countries of the world with large regional disparities.

We also only focus on studies that looked at the EU-15, as we believe that the literature on this group is more established. The enlargement from the EU-15 has been relatively recent and it will take some time for the effects to be seen and understood. In addition, there have been changes to EU regional development policies in the wake of the accession of the new countries, and this in itself could lead to different effects than past EU policies.

The rest of this paper is organized as follows. In section 2, we introduce the conceptual framework and hypotheses and present an overview of the theoretical debate around the two questions of market vs. interventionist policies and centralized vs. decentralized decision-making on regional development. Following this, we briefly introduce the history, objectives, structure and key elements of the EU regional policy in section 3. In section 4, we survey the literature and present a summary of the overall trends on regional convergence/divergence within the EU. In section 5, we discuss and synthesize the key findings from the literature that has assessed the effect of EU regional policy on such convergence/divergence through the lens of the conceptual framework we set up in section 2 and interpret the findings to see which hypotheses they support. In the final section, we conclude with a summary of our results and point to future research opportunities.

3
# 4. How have preferential agreements changed trade regimes?

Lack of progress in multilateral negotiations, among other reasons, has spurred tariff reductions through bilateral and regional preferential trade agreements.

Patterns of preferential liberalization In 2016, preferential trade agreements fully liberalized an additional 28 percent of global trade. This brings to 70 percent the share of global imports taking place duty free between countries in 2016. Only 5.5 percent of global imports are subject to positive tariffs under PTAs, of which one‐ fifth receive no preferences at all (see Figure 7). The overall trade‐weighted average tariff has been reduced from 5.0 to 2.7 percent.

Figure 7: More than half of the value of global trade took place under an agreement in 2015 <!-- Start of picture text --> Share of global imports<br>No Agreement Agreement<br>45% 55%<br>2.0% 21.3% 21.3% 21.0% 28.4% Missing<br>Missing MFN rate > 0 zero‐MFN rate zero‐MFN rate Preferential (Prf) tariff = 0 0.5%<br>2.3%<br>Zero tariif rates 3.2% 0 < Prf = MFN<br>70% 0 < Prf < MFN rate rate<br><!-- End of picture text --> Source: Authors’ calculation using ITC/World Bank database The extent of preferential liberalization varies across countries, but more than two‐thirds of countries have reduced trade‐weighted average tariffs to less than 5 percent. Multilateral liberalization efforts have been driven mainly by high‐income countries. This is reflected in their low preferential trade‐weighted applied MFN rates (mainly below 5 percent, see Figure 8). However, preferential liberalization has been widely spread across nations, with developing countries such as Rwanda, Burundi and Uganda reducing their average preferential trade‐ weighted rates by 40 percent.<sup>9</sup>

> 9 See Appendix **Error! Reference source not found.** .

9
-46-

receives a net resource transfer from its<sup>creditors but makes a net resource</sup> transfer back when output is high. In high<sup>output periods the debtor is</sup> indifferent between repaying and defaulting,<sup>but in low output periods (when</sup> it receives a net resource transfer from creditors)<sup>it is strictly better off</sup> than under financial autarky.

Once the enforcement constraint binds,<sup>it binds in every high output</sup> period thereafter, and the level of debt<sup>is the same in each subsequent high</sup> output period. How large the maximum<sup>debt level can be depends on the present</sup> value of the net resource transfer over the cycle<sup>that the debtor is prepared</sup> to make in order to avoid financial autarky.

Tables 2A and 2B provide magnitudes of<sup>indebtedness and net transfers (2A</sup> for the case in which output cycles between<sup>8 and 12, a 20 per cent standard</sup> deviation, and 2B for the case in which<sup>it cycles between 9 and 11, a 10 per</sup> cent standard deviation) for various values<sup>ofP("beta"), and R-</sup> l+r; CH and CL are consumption levels when output<sup>is high and low, respectively, while</sup> QH-CH and QL-CL are the corresponding net<sup>resource transfers from debtor to</sup> creditor. (The first is always positive<sup>and the second always negative.).</sup> The maximum amount of debt at the beginning<sup>of a high output period is given</sup> in the column DMAX.

The final column cortipares DMAX with the value of debt incurred<sup>in the</sup> previous (low-output) period times R. If the difference is<sup>positive then the</sup> debtor in fact can borrow enough before<sup>the enforcement constraint becomes</sup> binding to remain a net debtor thereafter.<sup>Otherwise, the enforcement</sup> constraint binds immediately and the debtor<sup>cannot initially borrow enough to</sup> remain a net debtor over the cycle. Instead,<sup>it can only borrow DMAX/R when</sup> output is low. The net transfer it then<sup>makes to creditors when output is</sup> high not only fully repays debt but includes,<sup>in addition, a net investment to</sup>