rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 2000<br>1500<br>.<br>a§ 1000<br>5<br>500<br>0<br>1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040<br>a > a vc ME vem<br>fe cs IND ssa<br><!-- End of picture text --> <!-- Start of picture text --> 800<br>'<br>!<br>600<br>+ 500<br>a5 400<br>5 500<br>200<br>100<br>0<br>2020 2025 2030 2035 2040<br><!-- End of picture text --> <!-- Start of picture text --> 700 1 Povertyheadcount<br>|<br>|<br>600<br>500<br>8 400 “> + OECD<br>§ Ma EM<br>= Ml BB<br>= 300<br>200<br>100<br>0<br>2020 2025 2030 2035 2040<br><!-- End of picture text -->
Table 8b: Extensions of Table 7 with alternative dependent variables for services, 2001-2006

||Indicator variable<br>for increase in<br>unorg. sector's<br>employment share|Indicator variable<br>for increase in<br>unorg. sector's<br>employment level|Change in unorg.<br>sector's employment<br>share relative to<br>average values|<br>Change in unorg.<br>sector's employment<br>level relative to<br>average values|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Employment growth in organized sector for state-industry|-0.192+++|0.028|-0.360+++|0.109++|
|across the time period relative to starting total employment|(0.020)|(0.021)|(0.032)|(0.043)|
|Indicator for employment growth in organized sector being|-0.240+++|-0.039|-0.085+|-0.080|
|greatest in small establishments (39 or fewer employees)|(0.052)|(0.049)|(0.045)|(0.067)|
|Indicator for employment growth in organized sector being|-0.097|0.013|-0.046|-0.049|
|greatest in large establishments (100 or more employees)|(0.079)|(0.071)|(0.084)|(0.106)|
|Indicator for entrepreneurship level in organized sector in|-0.030|-0.014|-0.056|-0.012|
|state-industry at the start of period being above median|(0.046)|(0.044)|(0.044)|(0.067)|
|Observations|525|525|525|525|
|Adjusted R-squared|0.283|0.285|0.455|0.405|
|State and industry fixed effects|Yes|Yes|Yes|Yes|

Notes: See Table 7.
Mexican Census. Section 3 discusses our identification strategy and other econometric issues such as censoring and the presence of nonlinearities in education decisions. Section 4 provides a broad theoretical framework that outlines how the main effects of migration on the feasible and desired amounts of education balance out at different wealth levels. The results on school attendance and education attainments are presented in Section 5. Section 6 then asks what children in migrant households are doing instead of going to school and Section 7 concludes.

# **2. Data**

This paper uses data from the 1997 Encuesta Nacional de la Dinámica Demográfica (ENADID) (National Survey of Demographic Dynamics) conducted by Mexico’s national statistical agency (INEGI) in the last quarter of 1997.<sup>7</sup> The ENADID is a large nationally representative demographic survey, with approximately 2000 households surveyed in each state, resulting in a total sample of 73,412 households. We restrict our analysis to rural communities, defined here to be municipalities which are outside of cities of population 50,000 or more. Our main results are robust to lowering this threshold to cities with population below 15,000. Within these communities we have a sample of 20,388 children aged 12 to 18 years, living in 12,980 households.

The key variables of interest are migration and schooling. The ENADID asks several questions concerning migration, including whether household members have _ever_ been to the United States in search of work. This question is asked of all household members who normally live in the household, even if they are temporarily studying or working elsewhere. Additional questions ask whether any household 7 Survey methodology, summary tables, and questionnaires are contained in INEGI (1999).

6
# **Who Escaped Poverty and Who Was Left Behind? A Non-Parametric Approach to Explore Welfare Dynamics Using CrossSections** 

Leonardo Lucchetti The World Bank _Keywords_ : Poverty, Welfare dynamics; Poverty transitions; Synthetic panels.

_JEL classification_ : O15, I32. _Sector Board_ : POV I am grateful to Tanida Arayavechkit, Oscar Calvo, Andres Castaneda, Christoph Lakner, Moritz Meyer, Emmanuel Skoufias, and participants at a World Bank seminar whose suggestions greatly improved earlier drafts of the paper. All remaining errors are mine.

Email: llucchetti@worldbank.org
# **1 Introduction**

Given the recent acceleration of digitization worldwide, high mobile phone diffusion, and improved access to internet, researchers and policy makers are increasingly interested in understanding the specific trade-offs of alternative methods for surveying individuals who are program beneficiaries, target groups for research studies, and the population in general (Abay et al., 2021).<sup>1</sup> This is especially true when in-person interviews are not feasible—such as during conflicts, refugee crises, and pandemic outbreaks—or expensive enough to make researchers seek alternative methods for data collection. Relative to in-person surveys, phone-based or online (self-completion) surveys can substantially reduce implementation costs (Garlick et al., 2020; Leo et al., 2015; Dabalen et al., 2016; Dillon, 2012; Mahfoud et al., 2015; Lamanna et al., 2019). However, among hard-to-reach groups, the data quality and sampling implications of phone or self-completion surveys are not fully understood, as documented in the literature (Mahfoud et al., 2015; Lamanna et al., 2019).

In terms of varying data-collection quality due to survey methods, two issues stand out. A first-order one is a non-random response rate, which can bias estimates substantially. For instance, online surveys cannot offer valid information about populations with little access to internet. Similarly, phone-based surveys yield high response rates only for populations with high rates of phone ownership and connectivity. Thus, inferences based on these kinds of surveys should be interpreted cautiously when describing specific groups.<sup>2</sup> Besides differences in access, another important concern regards the cultural likelihood of a response: some groups are especially reluctant or unwilling to respond to a particular survey method (Hersh et al., 2021).

Another problematic aspect concerns the reporting of sensitive information. On the one hand, the survey method can affect cognitive burden of respondents. This is especially true for those from hard-to-reach and vulnerable populations, many of whom may lack experience with self-completion or automated surveys (Pariyo et al., 2019). Moreover, the survey mode can also affect how people report sensitive issues.

> 1Although several surveys in high-income countries are now phone-based, there is still lack of evidence on what is the most effective survey method in low- and middle-income countries, where such surveys are more recent.

> 2(Abraham et al., 2009) show that non-response bias leads to an extensive overestimation of the rate of volunteering in the U.S.

2
- vi. the Tanzania National Panel Survey (TZNPS) 2008/09, 2010/11, 2012/13, 2014/15, 2019/20, and 2020/21 rounds.

- vii. the Viet Nam Household Living Standards Survey (VHLSS) 2010, 2012, 2014, and 2016 rounds.

The sample sizes hover around 3,000 to 5,000 households in each survey round for the LSMSISA surveys (including Nigeria and Tanzania), 5,500 to 7,000 households for the BIHSs and the ESS, 9,300 households for the VHLSSs, and over 12,000 households for the Malawi IHS. The consumption data are deflated in the same survey year’s prices and are comparable across survey rounds for each country.<sup>11</sup> The objective is to produce the imputation-based welfare estimates of interest as if we did not have consumption data and then evaluate these imputation-based estimates against those based on the actual survey data (i.e., the “true” welfare rates).

For the poverty line, we use the national poverty lines for Ethiopia, Malawi, Tanzania, and Viet Nam and the international poverty lines of $1.90 (in 2011 Purchasing Power Parity (PPP) prices) for Bangladesh and Nigeria.<sup>12</sup> The extreme poverty line is defined as US$1.25 (2011 PPP) per day per capita for Bangladesh and Nigeria and half of the national poverty line for Viet Nam and Ethiopia. For Malawi and Tanzania, we use the national food poverty lines as the extreme poverty lines.

> 11 In particular, for Bangladesh, Tanzania, and Viet Nam, consumption data are deflated to <mark>2018/19 prices, 2020/21 prices, 2010 prices</mark> respectively <mark>. For the Malawi</mark> IHPSs and IHSs, c <mark>onsumption data are deflated to 2013 prices and 2010/11 prices r</mark> espectively.

> 12 For Bangladesh and Nigeria, we employ the international poverty line for analysis since official poverty data for these countries are based on different data sources that are not available to us, such as the Bangladesh <mark>Household Income Expenditure Survey (HIES) and the Nigeria Living Standards Surveys (NLSSs). However, note that Nigeria’s national poverty line calculated using NLSS 2018/19 is close to the international poverty line of $1.90 per person per day in 2011 PPP (Lain and Vishwanath, 2022). Furthermore, data comparability issues also exist with various rounds of the Bangladesh HIESs (Fernandez</mark> _<mark>et al.</mark>_ <mark>, 2024).</mark>

14
# I. Introduction

Nearly nine years after the Arab revolts, the Middle East and North Africa (MENA) region is at a crossroads.<sup>2</sup> Throughout the region, citizens have again taken to the streets—in Algeria, the Arab Republic of Egypt, Jordan, Kuwait, Lebanon, the Islamic Republic of Iran and Iraq. Although the triggering events of the protests vary from country to country, as does their intensity, the fundamental cause in all is a pervasive distrust of government. Young people in MENA countries are in despair and lack confidence in the ability of governments to improve livelihoods. These protests signal the need for transformative economic and social changes to spur economic growth and create the hundreds of millions of jobs needed to employ the young people who will enter the labor market over the next several decades. Yet little has been done to address those needs. Nearly all governments have failed to implement the deep structural reforms required to transform these dysfunctional economies, which are characterized by rent-seeking, into modern economies which are more diversified and innovative, and which also recognize the salutary impact of fair competition and genuine private enterprise (Arezki et al. 2019). This paper argues for a transformation of finance to support the economic and social transformation of MENA.

But before we proceed further, it is appropriate to ask why finance for MENA? The literature on the nexus between finance and growth has documented strong evidence for the key role that a well-developed financial system can play in fostering growth, employment creation

> 2 The paper covers the countries within the MENA region as categorized by the World Bank. MENA includes most of the non-European Union Mediterranean countries. Non-European Mediterranean countries include the Syrian Arab Republic, Cyprus, Lebanon, Israel, the West Bank and Gaza, the Arab Republic of Egypt, Libya, Tunisia, Algeria and Morocco. Table 1 provides relevant statistics on the financial systems of individual countries in the latter group.

2
**Figure 17.  One-Year Probability of Finding a Job by Age Group and Field of Education** (controlling for level of education, gender, ethnicity and residence)

<!-- Start of picture text --> UE<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>15-19 20-24 25-34 35-44 45-54 55-ret ret<br>age<br>IT & other technologies, construction, mathematics<br>transport & communications<br>Other<br><!-- End of picture text --> _Note:_ “Ret” stands for retirement age. _Source:_ Calculations based on LFS 2002.

**Ethnicity appears to be more important than gender** . While unemployed males and females of the same age, education and residence have had virtually equal chances to find a job within a year, for nonLatvians this probability was, on average, 0.08 lower than for otherwise similar Latvians (Figure 18).

**Figure 18. One-Year Probability of Finding a Job by Age Group and Ethnicity** (controlling for level and field of education, gender, and region)

<!-- Start of picture text --> UE<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>15-19 20-24 25-34 35-44 45-54 55-ret ret<br>age<br>Non-Latvian Latvian<br><!-- End of picture text --> _Note:_ “Ret” stands for retirement age. _Source:_ Calculations based on LFS 2002.

**Young people are more likely to find jobs than prime-age jobseekers, while the elderly are trapped in unemployment.** As Figures 16 and 18 show, the probability of finding a job falls with age. A very important finding is that, despite relatively high youth unemployment rates, young jobseekers are significantly more successful in job search than those aged 35-44 or 45-54. The group which faces the greatest difficulties is the age group between 55 and retirement age.

24
traditional projects in the more liberalized agricultural sectors. The priority sectors became health, education, and social assistance—or broadly speaking, human development. Infrastructure continued to be important but lost to “soft” sectors during the 1990s. There was also an increased interest in public-private partnerships and instruments to “crowd in” private infrastructure funding.

These sector shifts are shown in the table below, which presents the breakdown of IDA grants by sector of activity over the 50-year period.<sup>40</sup> Six major trends are noticeable.

- The importance of agriculture and rural development increases steadily until the early 1980s and then gradually declines.  It seems to have stabilized over the past decade at about 9 percent of total disbursements.

- <u>Infrastructure—including electric power and energy, roads and transport, and</u> telecommunications—which amounted to more than 40 percent of total IDA loans during the 1960s, has always been important but declined during the 1970s and 1980s and since the early 1990s has hovered around 20 percent of total grants.

- Grants for industry have accounted for about 5 percent of the total.  This included a number of industrialization projects in the first years of IDA. It also includes an (almost constant) number of projects in mining and extractive industries, major export revenue earning activities in many poor countries. There were two highs of 10 and 13 percent, respectively, in the first part of the 1970s (probably explained by a large number of newly independent nations) and the first part of the 1990s in transition countries for industrial and financial restructuring projects.

- <u>Social sectors—including education, population, health and nutrition, and social</u> protection—increased from 6 percent in the early 1960s to 17 percent in the early 1990s, and reached a high of 30 percent in 2000-2005, declining slightly to 28 percent in the last 5 years.

> 40  The first part of the table reports numbers on a commitment basis and is based on the data from the report _IDA in Retrospect_ published in 1982 on the occasion of the first twenty years of IDA.  The second part reports numbers on a disbursement basis and is based on the grant/loan database managed by the World Bank.

29
ILO’s request. Some countries, e.g. Honduras and the Philippines, report wages paid in an occupation from an establishment survey. Other countries, e.g. India, report legislated minimum wage rates for certain occupations. Still others, e.g. Germany, report minimum wage rates based on collective agreements on hourly, daily, weekly, or monthly wage rates, depending on the occupation. Moreover, data sources change over time. For example, up to 1985 the United States reported wage rates from trade unions and earnings from the Industry Wage Surveys. From 1986 to 1997, the United States reported median usual weekly earnings from the Current Population Survey. Since 1997, the United States has reported median wage rates from an employer-based survey. Some countries give male workers’ wages in certain occupations. Others report both male and female workers’ wages. Still others report female workers’ wages in certain occupations. And so on.

Another problem is that countries do not report consistently from year to year. In the 1983-99 period, 158 countries reported wages in at least one year, but only five countries reported wages 17 times (i.e. every year), 40 reported 10-16 times, 51 reported 5-9 times, 43 reported 2-4 times, and 19 reported just once. Looking back across the years, in 1983, 56 countries reported wages; in 1985, 71 reported wages; in 1990, 72 reported; in 1992, 60 reported; in 1995, 76 reported; in 1997, 66 reported; and finally in 1999, 45 countries reported wages. The uneven pattern in reporting makes it tricky to conduct time-series and trend analyses. In addition, over time the ILO has asked for data on different numbers of occupations, which makes trend comparisons difficult, particularly those between the post-1983 period and earlier years. Moreover, some countries do not provide national data but report data from particular regions instead, e.g. major cities or urban areas.

A third problem relates to the concepts of wages used in the October Inquiry. Information is requested on average wage or salary rates and average regular gross earnings, together with the relevant hours of work, with respect to the month of October. The October Inquiry does not seek to cover all components of earnings (irregular bonuses, including such important payments as the annual or bi-annual bonuses paid in Japan and some other Asian countries, may represent a significant part of total gross earnings). Nor does it seek to obtain information on all supplementary labour costs. To 6
**8** **<u>│</u>**

OECD 2008; World Bank, 1997). The lack of clear thinking around the scope of desirable outcomes across these different dimensions as well as limited comparability between different estimates makes it difficult to evaluate whether FDI leads on aggregate to positive, negative or neutral outcomes.

Another challenge lies in incompleteness of analysis at both the micro and macro levels (e.g. at the level of the firm vs. the industry or the economy). An aggregate effect at the industry or country level could hide substantial heterogeneity across sectors, regions, and individual firms which is critical for assessing FDI development outcomes. Evidence at the level of firms or individual transactions is, however, often incomplete, non-representative or confidential, suggesting problems in the validity of results and the ability to generalize conclusions. For a number of effects of interest, analysis at the levels of the economy, the sector or the firm choice is constrained by the availability of data: that is, data only exist from one source at a specific level. The choice is therefore guided by the degree of confidence in that source.

**Table 2. How clear is the evidence? FDI’s impact in selected categories**

|||Competit|iveness|||Inclus|iveness||
|---|---|---|---|---|---|---|---|---|
||Productivity &<br>Growth|<br>Exports &<br>Diversification|Innovation|Domestic<br>investment|Jobs|Wages|Gender<br>inclusiveness|Supply<br>Linkages|
|Number of<br>studies|High|Low|Low|Medium|High|High|Low|Medium|
|Geographic<br>range of<br>studies|High|Medium|Low|High|High|High|Medium|Medium|
|Conclusivene<br>ss of results|High|Medium|Low|Low|Medium|Medium|Low|Low|

Source: Authors’ elaboration.

Note: The estimates come from an overall view of the literature while relying in part, though not entirely, on the leading or most complete study considered in this review, the study with the longest time horizon, the greatest number of economies sampled, or the most complete data<sup>1</sup>

> 1 For productivity, see Havranek and Iršová (2011), which finds strong evidence of positive vertical productivity effects, but no evidence of significant horizontal productivity effects; the potentially negative effects in the figure come from negative horizontal spillovers in other leading studies (e.g. Wooster and Diebel, 2010). For exports, see Harding and Javorcik (2012). For wages and employment, see Hijzen et al. (2013), though the possibility for negative effects come from other leading studies (e.g. Heyman et al., 2007). For women’s employment, see Chen et al. (2012), though again the possibility for negative effects comes from other studies (e.g. Braunstein and Brenner, 2007). For domestic investment, see Wang (2010). Finally, for innovation see Cheung and Lin (2004).
**Figure 2:** Self-Reported yield overestimation of crop-cut yield over quintiles of crop-cut yields and key inputs. _Notes:_ The y-axis represents the amount by which SR yields overestimate CC yields ( _Y_ = _SR − CC_ ) in kg/ha. The x-axis shows the quintile of the crop-cut yields or of the input per ha.

**Crop−cut Yields**

**GPS Measured Area**

<!-- Start of picture text --> 1 2 3 4 5<br>300<br>100<br>0<br>−200<br><!-- End of picture text --> <!-- Start of picture text --> 1 2 3 4 5<br>500<br>300<br>100<br>−100<br><!-- End of picture text -->

**Quantity of Seeds per Ha**

**Days of Household Labor per Ha**

<!-- Start of picture text --> 1 2 3 4 5<br>600<br>400<br>200<br>0<br><!-- End of picture text --> <!-- Start of picture text --> 1 2 3 4 5<br>600<br>400<br>200<br>0<br><!-- End of picture text --> 13
#

|**Dependent**|**fossil fuel subsid**|**y as a share of GDP**|
|---|---|---|
|**Variable:**<br>|**MENA**|**MENA oil Group**|
|**populaton**|-6.85e-17<br>(-1.08)|-7.18e-17<br>(-1.12)|
|**GDP**|1.33e-21<br>(1.33)|1.60e-21<br>(1.57)|
|**MENA x world**<br>**oil price**|1.00e-10<sup>*</sup><br>(1.75)||
|**lag MENA x**<br>**world oil price**|9.12e-11<br>(1.64)||
|**MENA oil group**<br>**x world oil price**||1.09e-10<br>(1.39)|
|**lag MENA oil**<br>**group x world oil**<br>**price**||1.47e-10<sup>*</sup><br>(1.92)|
|**constant**|1.19e-08<sup>**</sup><br>(2.56)|1.28e-08<sup>**</sup><br>(2.61)|
|**_N_**|328|328|

_t_ statistics in parentheses.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. An increase on world oil price has a mild effect (significant at 10%) on increasing the level of subsidies for both MENA and with a lag, MENA oil exporters, confirming the theory. See section 3. Both regressions control for country & time fixed effect & use robust error. Because World oil price is the _same_ for all countries, it must only be interacted with other variables as it would otherwise drop out due to time fixed effects. For this reason, price x MENA and price x MENA oil exporters is being presented in the full sample as interaction effect.

40