rafmacalaba/data-use-mentions-extended huggingface dataset:
population is being questioned. Recent research is increasingly showing that an economy could grow for sustained periods of time, while leaving large sections of its population behind and sometimes even worse off. Policies to ensure more inclusiveness are hence needed.

The main aim of this paper was to provide an informed assessment of how to prioritize policies in a low-income country with sparse micro data, putting special emphasis on income growth for lower-income households. Drawing on the growing availability of standardized country data on income distribution, this paper examined the drivers of the income of the two poorest quintiles for a sample of 117 countries between 1967 and 2011.

The results suggest that maintaining macroeconomic stability as well as investing in human and physical capital would not only accelerate overall economic growth, but benefit more particularly the poorest segments of the population. This paper shows that there thus need not be a trade-off between inequality and growth: the overall pie can grow larger as well as the slice allocated to the poorest households, all with the same set of policies. Governments can foster faster economic growth while at the same time increasing inclusiveness, ensuring that everyone can live up to their potential.

While our results, derived from a simple cross-country analysis, have limitations, we see our approach as a starting point to identify and prioritize broad policy areas. Financial development, for instance, is a complex process. The literature has been ambiguous about the channels through which finance may be associated with lower poverty (deposits or credit). Measuring credit to the private sector may be thus a too crude indicator and a finer analysis would be called for as alternative financial indicators become available for longer time periods.

Our analysis cannot substitute either for country-specific considerations and studies. It offers nevertheless some guidance where additional evidence could be most helpful when existing data and micro-evidence are scarce. Neither does our methodology provide any guidelines which specific policies should be implemented to make progress in certain policy dimensions. Furthermore, large gaps do not automatically imply that they are quickly or cheaply closed either.<sup
**_<mark>(3) Connectivity to global knowledge.</mark>_** <mark>A range of activities related to participation in international markets may have beneficial impacts on firm outcomes, especially those related to international buyers/suppliers that bestow knowledge spillovers from commercial interaction (learning by exporting/importing) or firm-specific abilities to access and utilize knowledge flows and best practices via global connectivity through the internet or technology licenses. Sutton (2007) argues that developing economies benefit through the vertical transfer of capabilities from foreign to domestic firms. Gorodnichenko, Svejnar and Terrell (2010) explore whether firms that supply a larger share of sales to MNCs innovate more than firms that sell more to the domestic market, and find that these vertical relationships induce innovation by domestic firms. As part of exploring the range of vertical capability transfers between domestic and foreign firms, they also simultaneously include export and import indicator variables, as well as market competition indicator variables. We relate the following knowledge-related indicators to outcomes:</mark>

- **<mark>Access to knowledge and learning through trade</mark>** <mark>: Firms can acquire more advanced technologies and learning capabilities by exporting to technologically more advanced markets and engaging with customers in those markets, with foreign buyers often providing their local subcontractors with technology, capital equipment and training so that the latter can produce to higher specifications and standards. While the traditional evidence has been inconclusive (see Wagner 2007 for a review of the literature on exports and TFP), Kraay (2006) finds evidence that past export experience helps explain current TFP for Chinese firms, and Lederman (2009) finds that a firm's export status is positively correlated with the probability of innovating. For India based on plant-level data for over 20,000 firms over 1989-2008, Mukim (2010) finds that exporting improves firm performance, though Tabrizy and Trofimenko (2010) do not (they find that self-selection of more productive firms into exporting explains the productivity
progress, less likely to drop out of high school, and have significantly higher test scores. Longerterm studies also find higher rates of college attendance (for whites) and lower rates of delinquency and crime (for blacks) among program participants (Garces et al. 2002).

A number of studies have analyzed the effect of Head Start on child socio-emotional development. This is important because child attributes as basic as being able to sit still and pay attention are necessary for any reasonable amount of learning to take place in school. Children who are appropriately socialized tend to turn into better-adjusted adults, and the labor market returns to various non-cognitive skills, including motivation, enthusiasm, cooperation, and teamwork may be as large as or larger than those to “IQ” and other dimensions of cognitive development (Carneiro and Heckman 2003; Heckman and Rubinstein 2001). Behavioral problems in early childhood have also been shown to be strong predictors of high school dropout and delinquency.

There is some controversy about the effects of center-based programs like Head Start on child socio-emotional development. At what age, and under what circumstances, is it beneficial for young children to spend large amounts of time away from their mothers? The analysis of the Perry Preschool Project and the Carolina Abecederian Project suggest that, whatever the negative effects of the intervention may be on mother-child attachment and (possibly) child socioemotional development, these are offset by the positive effects on academic achievement, wages and criminality—at least for the high-risk populations that participated in these model programs. That being said, it is not clear how easy it is to extrapolate from such model programs to large, publicly-funded or implemented programs like Head Start. Two recent papers are here informative. A randomized evaluation of the Early Head Start program, which combines centerbased preschool with parent-child group socialization activities and parent education, either center-based or during home visits, suggests that children participating in Early Head Start exhibit less aggressive behavior, less negative behavior towards parents, and are better able to pay sustained attention during play
3.3 Product-Related Trade Variables**

The export survival rate in the US market and all trade-related control variables at the product level (initial export, total export, number of suppliers, a multiple spell dummy) are computed using the BACI dataset developed by the CEPII and described in Gaulier and Zignago (2010). The dataset provides harmonized bilateral trade flows for more than 5,000 HS 6-digit products and 143 countries. Export flows are reported annually 15
# **2. Institutional Background and Data**

In this section, we describe the main features of the Brazilian credit market and describe our comprehensive dataset of loans to firms.

# **2.1. Brazil’s Credit Market**

The credit market of Brazil is characterized by an extensive presence of government-driven lending. Public interventions include a complex web of price and quantity regulations, reserve requirements, tax exemptions, and forced savings schemes to target credit to specific projects. Lending directed by these initiatives is known as earmarked credit. Earmarked loans in Brazil complement the usual market for credit provided by commercial banks. Over the years, earmarked lending has grown substantially, exceeding 50% of total credit by 2015. Earmarked credit has been used as a policy response to alleviate credit constraints and promote access to credit among individuals and firms, including micro, small, and medium-sized businesses.

Earmarked credit to firms is largely directed to finance specific projects that include fixed investments, infrastructure, development projects, and rural activities.<sup>10</sup> Among all the earmarked loans issued to firms between 2005 and 2016, 88% financed fixed-asset purchases such as new

> 10 Earmarked credit to households mostly targets real estate financing.

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based on the date of arrival of the head of household. Results displayed at the 95% Confidence Interval _Source_ : LFS 2009-2014. World Bank author’s calculations.

It is important to understand which characteristics or circumstances may be driving the large differences in poverty between our six comparison groups. Changes over time are also worthy to note since the number of foreign households in the LFS increases dramatically in 2013 and 2014. Table 10 illustrates the mean summary statistics for our comparison groups in 2009, 2013, and 2014 that are located near the Syrian border.<sup>12</sup> Table 11 shows complementary statistics for households located in the rest of the country.

Recent migrant households are younger and have more children, and a higher dependency ratio. Dependency in migrant households result not from the elderly but from children. Families of recent migrants are much larger. In 2013, recent migrant households near the Syrian border had on average 3.1 adults, 2.4 children, and 0.4 elderly. The large number of children is indicative of entire families moving to Turkey from Syria. The composition is in contrast to working age individuals who are more likely to be economic migrants. These individuals would migrate without families, and send remittances home to their

> 12 The years 2009 and 2014 are the earliest and latest available surveys in the LFS that are usable for measuring migration. However, due to data availability on Syrian refugees and changes to the survey in 2014, regression analysis will use only 2011 and 2013 LFS data.

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used as the household level indicator of wellbeing.

We then shift to the analysis of relative labor deprivation where the sample is limited to those individuals aged between 15 and 64 who are not enrolled in school. This restricts the sample to 6,451 individuals – 3,339 females and 3,112 males. In the last stage, we further restrict the sample to the 2,128 youth (aged 15-29) – 1,085 females and 973 males – who are not enrolled in school.

> 4A World Bank (2011) report “Promoting Youth Opportunities and Participation” is based on this survey, as well as on a qualitative study.

> 5 To adjust nominal expenditure across rural and urban households in the absence of CPI data that covers prices in rural areas or information on unit values from the household survey, we take a simpler approach. We use the ratio of the official urban and rural poverty line of 2007 as reported by the HCP to adjust nominal expenditures across rural and urban households. Urban prices are assumed to be 7.4 percent higher than rural prices (the ratio of the urban and rural poverty line or 3834 / 3569).

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sex-occupation categories. Deducting this estimated value from the observed value of log weight for height ( _lnh_ ) yields a health measurement that includes an individual’s unobserved health endowment ( _μ_ ) and an aggregate unmeasured effect of time-invariant household characteristics ( _ρ_ ) (e.g. the effect of spouses’ characteristics, such as education, assets, unobserved preferences, household landholding, short-run time-invariant living and hygiene conditions, drinking water source, etc. as discussed above):

> time-varying occupation data, I could obtain consistent estimate of its impact on health using IFE without this assumption.

> 10Morris (1997) finds that traditions in Bangladesh often inhibit a woman’s ability to obtain employment outside of home. Purdah, or female seclusion, is an Islamic tradition routinely practiced in Bangladesh among the Muslim majority, which limits women’s labor market participation.

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Although the interaction term for the household size and the number of pensioners is not statistically significant in both the pooled and BUC regressions (Appendix A, Table A.12), the total effect of pensioners is statistically significant and positive in the BUC model (Appendix A, Table A.13). But the inclusion of pensioners does not change the estimated scale parameters significantly: the adult scale parameter still varies between 0.4-0.6 and the child scale parameter is about 0.05-0.06 (Table 5, row 6).

# **6.3. Alternative Functional Form**

As an alternative to Equation (1), we can estimate a non-parametrical function recently proposed by Biewen and Juhasz (2017) as follows The estimated parameters for this scale are given in Appendix A, Table A.14. The table shows that the “non-parametric” scales for household types are smaller than those estimated using the parametric functional form as in Equation (1). The estimated equivalence weight of a second adult is 24 percent of the first adult, and the estimated equivalence weight of a child is 13 percent, or about half of the second adult. The child scale parameter is similar to our BUC estimates, and also to those obtained by Biewen and Juhasz (2017) for Germany.

# **7. Conclusion**

We estimate equivalence scales using unique subjective wealth data from Russia, and apply these scale adjustments to examine new poverty lines as well as the sensitivity of poverty dynamics. Our findings suggest that the country’s official poverty threshold ranges from 50 21
s Temporal Disaggregation Methodology (Chow and Lin 1971) is employed to disaggregate the annual data into a higher frequency quarterly series. This methodology generates the best linear unbiased interpolation, distribution, and extrapolation of time series by using a related series called the indicator variable. The Chow-Lin method uses quarterly indicators to generate the quarterly volatility and transforms annual frequency data to quarterly data. To generate a quarterly series of aggregate private savings, the quarterly series of non-government demand and time deposits (obtained from the IMF – IFS 2015) was used as an indicator variable.<sup>11</sup> The generated quarterly series was deflated using CPI and divided by the population count to obtain the real gross private saving per capita in logarithm.

Motivated by the Life Cycle Model, it follows a brief account on relevant variable selection and the hypothesis to be tested with respect to the saving behavior in Egypt.

> 10 See  ElSayed (1993), Touny (2008) and the literature review section.

> 11 Full details of the matlab code and output is provided in the annex.

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household members (aged 15 to 64 years) who would participate in the experiment, stratifying them by age and gender. Each randomly selected member (or the member’s proxy respondent for LOA or C) provided information solicited via the three main modules: education, access to technologies, and labor. Only respondents assigned to the LOA treatment responded to that survey module. We made sure that the three relevant modules were collected at the beginning of the experiment and from all of the randomly selected self- or proxy respondents within the three treatment groups.

# **3.2 Outcomes**

We measure the effects of these alternative survey methods on employment and work as outcome variables. For this paper, the working-age population includes individuals between the ages of 15 to 64 years.

_Employment:_ We use data collected from all study participants via the standard labor module. According to the 19<sup>th</sup> International Conference of Labor Statisticians (ICLS), being employed is defined as working for pay or profit. Individuals who have engaged in work only for their own use in the past week are not considered employed. We define this variable as a dummy indicator that takes the value of 1 if the person engaged in at least one activity for pay or for profit in the last week, and 0 if the person reports either not engaging in any activity for pay or profit or engaging only in activities intended for own use during the past week (Durazo et al, 2021).

_Work:_ A person considered working must be involved in producing goods or providing services in the last week. The output of the activities could be for own use (e.g., farming produce for household consumption without the intention to sell) or for pay, sale and profits. We define this variable as a dummy indicator that takes the value of 1 if the person reported engaging in at least one activity during the previous week (e.g., farming for own consumption or sale
the public sector maintains a positive ERP, after adjusting for NTBs, because it employs relatively more domestic resources (mainly labor) and the resulting share of tradable inputs relative to gross output is much smaller. While the value added over tradable inputs is reduced in the public industry with the removal of interventions, it still remains positive, unlike the industry in the private sector. In fact, using the CAPMAS 2006/2007 Annual Bulletin of Industrial Production Statistics, the wage bill for the private and public tire and tube industries are practically the same, but the wage bill represents only 5 percent in the private sector and 20 percent in the public. (We note also that the public industry represent about one-fifth of the gross output of the private tire and tube industry.) Intuitively, one would think that there must be a subsidy to the public firm in this industry, but no explicit subsidy appears in the CAPMAS statistics going to the public enterprise, although the statistics show a subsidy to the private firms in this industry. The inference is that returns to capital in the public industry is very low, representing an implicit subsidy.

# **7. Results: ERPs Incorporating Energy Subsidies for 23 Sectors from 2006/2007 Input-Output Information**

Table 5a shows the ERP calculations for 23 industrial sectors in Egypt, using applied tariffs, net output subsidies and subsidies on energy inputs. Table 5b shows the ERP calculations including the NTB estimates. Table 6 shows the industrial categories used to construct the average tariffs associated with each sector. In some cases, for example LPG, specific tariff lines are available. Where specific tariff lines are not used, sector tariff are calculated by using averages over tariff lines in the appropriate category. Sector-level tariffs are shown in the first column of figures in Tables 5a and 5b. The second column of figures in Table 5b shows the ad valorem addition due to NTBs. The reader should note that due to the extremely high tariffs on alcoholic beverages (as seen 14
**Agriculture.** Despite growing urbanization in developing countries, poverty continues to be a largely rural phenomenon. Three‐quarters of the poor in developing countries are located in rural areas.<sup>14</sup> Among the 3 billion rural inhabitants in developing countries, 86 percent rely on agriculture for their livelihoods. Consequently, this sector has a critical role to play in poverty alleviation efforts. The 2008 World Development Report found that GDP growth in agriculture is at least twice as effective in reducing poverty as non‐agricultural growth.

Agricultural productivity increases are not only a source of economic growth but also catalyze the development of related industries—accounting, in some cases, for about a third of the GDP—and the broader nonfarm economy. The linkages between agriculture and industry illustrate the importance of agricultural growth as a labor‐intensive process. Productivity growth in agriculture keeps food prices from rising, resulting in low wages that contribute to the competitiveness of non‐agricultural sectors. Agricultural growth is also known to generate multiplier effects on the rest of the economy. It is not surprising that the most successful development stories begin with agriculture.

**Figure 5: Rising labor income is the main driver of poverty reduction**

<!-- Start of picture text --> 140 Decomposition of Changes in Moderate Poverty, by Income Level, in Selected Developing<br>Countries, 2000s<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>US$1.25 a day US$4 a day US$5 a<br>day<br>Share of working-age family members Employment+earnings Nonlabor income Consumption-to-income ratio<br>Source: Figure 1.1 (page 3) in Inchauste and J. Saavedra‐Chanduvi, (2014); using Data from SEDLAC,<br>various years; FAO n.
These data are the result of data collection and collaboration across multiple entities to ensure the most current, widest coverage and creativity of the users to apply it. However, persistent challenges to data collection remain, including limited geographic levels and temporal lag at low frequencies. Also, the reference year and spatial resolution of the local AgGDP estimates are limited to the contemporaneous availability of the economic statistics and components such as the crop production model. We often have to consider the fitnessfor-use while considering the accuracy; the model has lower average spatial resolution in areas where we have little data, however these same areas may benefit from the availability of these estimates to inform policy. Predictions are dependent on the availability and quality of the training data on which the model is based and the modeling process is flexible to update individual countries as the data are available. In the near future, we hope to increase the currency and number of countries with subnational data as updates of the regional account data and models of the components upon which the model relies become available.

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Memo Items:**|||||||||||||
|Other Developing (3)|9|37|..|38|16|87|25|162|1|..|3|4|
|Developed (13)|1,017|20,963|133|11,460|44|1,728|1,194|34,151|604|74|11|689|
|All countries (41)|2,601|28,937|170|11,860|122|2,683|2,893|43,480|1,338|80|34|1,452|

Notes: .. = 0 or data not available

*  Number of measures in force based on notifications to WTO as of June 30, 2011.

** Total products affected by AD, CV, and SG are based on number of products on raw data files at HS 6-, 8-, 9-, 10-digit level or mixed  together in reporting countries.

Sources: WTO Antidumping database and Trade Profiles data; and Bown, C (2012), Temporary Trade Barriers (TTB) database (product data).

# **_The Overall Use of Non-Tariff Measures_**

The overall use of non-tariff measures by developing countries during the GATT and WTO sub-periods is presented in Table 8. Column one shows country per capita income in 2011. Column two shows an openness index for each country calculated as the ratio of exports plus imports divided by GDP also in 2011. Column three shows the presence of tariff peaks in the latest tariff schedule. Column four shows the total number of non-tariff measures in place as of June 2011. The other columns show total frequency ratios (Tfm ) for 'core' non-tariff measures (see Appendix B for a discussion of the meaning of total frequency ratios) for different periods.

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