rafmacalaba/data-use-mentions-extended huggingface dataset:
countries because PovcalNet does not yet use spatially adjusted welfare aggregates for many countries. Finally, the issue of negative and zero incomes is only relevant for countries of the European Union and Latin American and Caribbean (LAC). In LAC, negative incomes are recoded as missing and therefore, the EUSILC database was used to test the impact of negative incomes, while LAC countries were used to test the impact of zero incomes. The sections below investigate how changes in these particular adjustments may affect the shared prosperity index.

# **3.1. Grouped versus Microdata**

There is a systematic difference between estimates using grouped and microdata of mean consumption per capita of the bottom 40 percent. In almost all cases, except the first year in Armenia and the second year in Peru, means from grouped data are lower than means from microdata. The difference ranges from a very small negative 0.12 percent in the case of Iraq to negative 1.41 percent in the case of Rwanda. Given that the impact of using grouped data affects both means in two periods of time, the indicator of shared prosperity is not expected to be very much affected. In a way, the effect is similar to multiplying means in the shared prosperity formula by a constant number, which should not affect the rates of growth.

**Table 2. Grouped and microdata based daily mean consumption per capita of the bottom 40 percent of the population in $/PPP 2005 and difference between them in percent**

||**micro**|**grouped**|**micro**|**grouped**|**differen**<br>**(grouped/m**|**ce in %:**<br>**icro‐1)*100**|
|---|---|---|---|---|---|---|
||**b40
on return for Syrian refugees. In doing so, it also considers displacement in Syria as a lens through which to view the difficulties returning refugees may face. Though many have analyzed attitudes around return with surveys (Ghosn et al. (2021), Alrababa’h et al. (2020), Krishnan (2020)), this paper is, to our knowledge, the first exploration using observational social media text and image data on the prospects for refugee return in northern Syria. Northern Syria is a territory still militarily contested by the Syrian Opposition with the support of Turkey; the Assad regime with the support of Russia; and the Kurdish Autonomous Authority with the support of the US. Neighboring Turkey has also been vocal in plans for re-settling refugees in Turkish-controlled areas of Syria (Hoffman and Makovsky (2021)). The paper draws on original social media data from the three most popular social media platforms in Syria: Telegram, Facebook, and Twitter. Our focus is on attitudes, behaviors, and information dissemination around topics of internal displacement and refugee return.

To identify discussions and conditions that reflect internal displacement and return we use unsupervised machine learning (ML) methods to cluster topics of interest from the text and image data. We also run ‘seeded’ topic models, models run on subsets of the data filtered for keywords related to displacement, local governance, service provision, and return. Using insights from the topic models we run two sets of analysis. First, we use mixed effects models to identify changes in topic discussion post-return. Second, we use mediation analysis to understand the role that social media plays in the relationship between local violence and return.

We find that discussions of violence are more prevalent in areas with neither returnees nor IDPs, with regime military action, air strike warnings and the anti-ISIS campaign 29–52% more prevalent,<sup>1</sup> whereas discussions of services and the economy are more prevalent in areas with returnees and IDPs (an increase between 16%
A. and E. Duflo. (2008). “What is middle class about the middle classes around the world?” _Journal of Economic Perspectives,_ 22(2).

- Baumol, W. (1968). “Entrepreneurship in Economic Theory.” _American Economic Review_ , _Papers and Proceedings_ , 58 (2).

- Betcherman, G., K. Olivas, and A. Dar. (2004). "Impacts of Active Labor Market Programs: New Evidence from Evaluations with Particular Attention to Developing and Transition Countries." Social Protection and Labor Discussion Paper No. 0402. Washington, D.C., World Bank

- Betcherman, G., M. Godfrey, S. Puerto, F. Rother, and A. Stavreska (2007) “A Review of Interventions to Support Young Workers: Findings of the Youth Employment Inventory.” Social Protection and Labor Discussion Paper No. 0715. Washington, D.C., World Bank.

- Blattman, C. and J. Annan. (2011). “Reintegrating and employing high risk youth in Liberia: Lessons from a randomized evaluation of a landmine Action and agricultural training program for ex-combatants”. IPA, Yale University.

27
3 FOREIGN DIRECT INVESTMENT IN KENYA <!-- Start of picture text --> 800<br>600<br>400<br>200<br>0<br>China France South Africa Switzerland United Kingdom United States<br>Country<br>FDI instock (USD Millions)<br><!-- End of picture text --> Figure 21: China and UK hold the most FDI stock in Kenya (2012) Source: UNCTAD FDI/TNC database 2015

**Chinese firms are well-represented in large-scale infrastructure projects** China constructed 905.5 km of road in 2006 and invested AC200 million (US $227.6 million) to rehabilitate the NairobiMombasa road (Fiott 2006). In 2012, three kilometeters of road at the KWS Gate/Bomas Junction have been built for KES 2.67 billion (US $27.1 million) by the China Wu Yi Co., and many Chinese firms have been pre-qualified by the government to develop 2,000 km of road in various counties (Kenya Urban Roads Authority 2015). For the mega-projects, the China Road and Bridge Corporation is building a 609 km section of the Standard Gauge Railway (SGR) linking Nairobi and Mombasa for US $3.6 billion, and the China Communication Construction company is building three berths at the port on Lamu island for US $467 million (Financial Times 2014). With the slowdown of the Chinese economy, China’s low cost infrastructure providers will seek to market their services outside of China, including in Africa, so Chinese infrastructure projects in Kenya will likely continue.

**But the bulk of Chinese work in infrastructure is not investment, but contracting** The SGR and the berths at Lamu are not investments, but projects financed by China’s EXIM bank: the bank will finance 90 percent of the SGR, making SGR a Kenyan investment. Kenya has taken on debt for many major construction projects, and Chinese companies typically have no equity
to the border and coast are not significantly correlated to either measure of mobility. This is in line with Alesina et al. (2021) in the case of the border but differ relative to their results for the coast. However, distance to the capital is negatively (positively) correlated to upward (downward) mobility although weakly (statistically significant at the 5% for upward and at 10% for downward mobility).

# **V Final Remarks**

This paper examines intergenerational educational mobility for countries in Latin America and the Caribbean at a disaggregated regional level using census data spanning more than half a century. I investigate mobility in education at the bottom of the educational attainment distribution by focusing on the likelihood of completion of primary education for those whose parents did not complete the level, which can be measured with a high degree of con29
results in three poverty lines: a food poverty line and two (total) poverty lines that incorporate adjustments for nonfood basic needs to the food poverty line and are commonly called the lower and upper poverty line. The approach usually involves the following steps. First, the food poverty line (FPL) is estimated. The FPL conceptually stipulates the cost of achieving a minimum threshold of caloric consumption in the reference population. The first component – a minimum calorie requirement – is obtained from the country’s nutritional tables, recommendations from the Ministry of Health, or another local authority on the subject. Depending on data availability, the minimum calorie requirement is obtained from international sources that may be applicable to the context at hand. The cost of achieving this minimum calorie requirement is then usually calculated on the basis of information from a household expenditure survey by estimating the average cost of a calorie. Such an average is estimated from the value of food consumption (and its associated expenditure) in the household survey and from the calories associated with the observed level of consumption. Calories consumed by food item are generally obtained from a reliable external source. The cost per calorie of different food items is thus estimated and aggregated to get a representative value. Notably, the cost per calorie is based on information from a reference population. This reference group is chosen because it is deemed that its consumption patterns (including the basket of goods and services it purchases) are most relevant to inform the estimation of

> 3 The poverty line used for targeting of social programs is based on the CBN (World Bank 2021).

3
analysis of fiscal choices offers valuable insights on how policies affect poverty and equity.** This is in line with calls to improve the fairness, effectiveness, and efficiency of public expenditure while prioritizing support to the most vulnerable.<sup>7</sup> The analysis complements ongoing reform initiatives under the World Bank’s current Development Policy Grant. Moreover, analyzing the distributional impact of fiscal policies on households’ welfare is an important part of monitoring the Sustainable Development Goal 10.4.2, which measures how countries’ fiscal systems contribute to inequality reduction.

**This report represents the first comprehensive fiscal incidence analysis in the West Bank and Gaza.** It uses the Commitment to Equity (CEQ) methodology to assess distributional repercussions of the main tax interventions and public social expenditures, including direct and indirect taxes, direct transfers and indirect subsidies, and in-kind benefits from the provision of public health and education services. The analysis is anchored in data from the most recent nationally representative Palestinian Expenditure and Consumption Survey (PECS) (2016/2017), as well as administrative data for the main taxes and social expenditure items in the West Bank and Gaza.

## **The analysis presented in this report seeks to answer four key questions:**

- How does the combination of taxes and transfers modeled affect poverty and inequality in the West Bank and Gaza?

- Which fiscal interventions are the most important in driving these effects?

- How progressive or regressive is each component of the fiscal system?

> 3 For further detail on the fiscal situation, see: World Bank (2022a), World Bank (September 2022).

> 4 Clearance revenues refer to revenue collected by Israel on behalf of and transferred to the Palestinian Authority.

> 5 World Bank (March 2023).

> 6 World Bank (March 2023).

> 7 World Bank (September 2022).

5
and, by motivating their decision to instead spend time on housework, increases the proportion of Ninis in the population. Finally, the authors conclude that household income is an important correlate of being a Nini for men but not for women. Using different sources of cross-sectional data, Águila et al. (2013) find similar results, but emphasize the fact that the majority of Ninis are women with children, living in high poverty contexts. Using longitudinal data from the Mexican Family Life Survey and a cross-section from the 2011 Social Mobility Survey, Arceo-Gómez and CamposVázquez (2014) find evidence to substantiate this hypothesis. They observe that teenage pregnancy generates a decrease of between 0.6 and 0.8 years of schooling and lower school attendance, fewer hours of work, and a higher marriage rate in the short run. Moreover, in the long-term teenage pregnancy results in a loss of between 1 and 1.2 years of education, with the related consequences of later educational achievement and lower household income, heightening vulnerability and increasing the risk of falling into poverty.

Tuirán and Ávila (2012) analyze several hypotheses and preconceptions about Ninis in Mexico. Studying 12 to 29 year olds using data from the National Youth Survey (ENJ, by its acronym 4
in the course of the upswing in consumption expenditures, some households fell behind, while others shifted toward the top, joining the ranks of those whose consumption put them in the top decile in 1991/92.

To summarize these changes, we present in Figure 3 the set of relative polarization indices computed from the GLSS data using Equations(7)-(9).<sup>17</sup> These indices track changes in the shape of the distribution only, and they code the direction as well as the magnitude of the change. The overall index (MRP) rises continuously and the rise is statistically significant from the outset, thus confirming the visual impression from Figure 1(d). Decomposing the MRP into the contributions from the lower and upper tails of the distribution, it also appears that “downgrading” dominated “upgrading” in the – polarization upswing the value of the LRP is indeed always greater than that of the URP.

# **3.2 Temporal decomposition**

To get a more compact picture of the timing and nature of the polarization trend described above, we can break the 21-year period into 3 sub-periods – 1991-98, 1998-2005, and 2005-12 – and highlight the changes that took place _within_ each of them. The top three panels of Figure 4 show the relative distribution for each sub-period. In contrast to the 21-year decile series, which takes 1991/92 as the reference distribution for all waves, each panel here takes the beginning year of the sub-period for the reference distribution and the end year for the comparison. The displays clearly point to the median up-shift in household consumption expenditure as the dominant trend for each sub-period. These are the images of a “rising tide that lifts all boats”, i.e. the effect of a location shift that was the most influential contributor to the overall pattern during all sub-periods. The differences due to the median shift – representing what the relative density would have looked like if there had been no change in distributional shape – are
)|(0.86)||(0.82)|(0.85)||
|Food Consumption Score<sup>++++</sup>|57.32|57.12|0.21|57.23|57.34|-0.10|57.70|57.03|0.67|
||(29.47)|(32.34)||(29.11)|(33.07)||(28.41)|(36.10)||
|Reduced Coping Strategies Index<sup>++++</sup>|13.44<br>|12.68<br>|0.76|13.42<br>|12.48<br>|0.94*|14.15<br>|13.02<br>|1.14*|
||(17.46)|(20.39)||(17.78)|(21.64)||(18.23)|(21.38)||
|Livelihoods Coping Strategy Index<sup>++++</sup>|<br>4.59|<br>4.70|-0.11|<br>4.67|<br>4.66|0.02|<br>4.69|<br>4.79|-0.10|
||(4.44)|(5.76)||(4.61)|(5.47)||(4.62)|(5.99)||
|Household has some amount of debt|0.77|0.78|-0.01|0.78|0.78|0.01|0.80|0.78
- Callen, M., S. Gulzar, A. Hasanain, Y. Khan, and A. Rezaee, 2015. "Personalities and Public Sector Performance: Evidence from a Health Experiment in Pakistan." _NBER working paper_ 21180

- Cunha, F. and J. Heckman, 2010. “Investing in our Young People.” _NBER Working Paper_ No. 16201.

- Cunha, F., Heckman, J., and Schennach, S., 2010. Estimating the technology of cognitive and non-cognitive skill formation. _Econometrica_ 78(3):883-931.

- Dal Bo, E., F. Finan & M.A. Rossi, 2013. "Strengthening State Capabilities: The Role of Financial Incentives in the Call to Public Service," The _Quarterly Journal of Economics_ , vol. 128(3), pages 1169-1218

- de Janvry, A., E. Sadoulet  and T. Suri, 2016. “Field Experiments in Developing Country Agriculture”, Banerjee, A. and E. Duflo, (eds.), _Handbook of Economic Field Experiments_ . Volume 1. Elsevier.

- Delavalande, A., X. Gine ́ and D. Mckenzie, 2011. “Measuring Subjective Expectations in Developing Countries: A Critical Review and New Evidence”, _Journal of Development Economics_ , 94 (2): 151–63.

- Fischer R., and T. L. Milfont, 2010. “Standardization in psychological research.” _International Journal of Psychological Research_ . 3, 88–96.

- Gertler, P., J. Heckman, R. Pinto, A. Zanolini, C.Vermeersch, S. Walker, S. M. Chang, S. Grantham-McGregor, 2014. “Labor market returns to an early childhood stimulation intervention in Jamaica”, _Science_ 30: (344): 6187 pp. 998-1001.

- Ghosal, S., S. Jana, A. Mani, S. Mitra and S. Roy, 2016. “ <mark>
- <mark>2) Investment climate reform can aim both at visibility and impact. If visibility is the goal, reforms should be undertaken that will result in the largest gains on the overall DB ranking. If impact is the goal, the following additional advice can be given:</mark>

- <mark>a. The DBIs with the largest impact on economic growth are ‘Getting Credit’ and ‘Enforcing Contracts’.</mark>

- <mark>b. Other DBIs with a statistically detectable impact on economic growth are ‘Starting a Business’, ‘Registering Property’, and ‘Protecting Investors’.</mark>

- <mark>c. Sub-indicators focusing on cost, and to an extent time, have the largest impact on economic growth across all nine DBIs examined.</mark>

<mark>It should be borne in mind that some of this advice is based on the statistical analysis of a large crosssection of countries. Naturally, this paints an aggregate picture at the expense of country-specific detail. The results from the analysis do not provide a magic wand for fostering economic growth. They only provide additional guidance which should always complement an in-depth analysis of the situation on the ground (which should involve information from the domestic business community through, for</mark> example, the ‘Enterprise Surveys’) in each country embarking on investment climate reform.

<mark>Whilst the analysis is comprehensive and includes recent data in the study of the business climate and economic growth, there is scope for further investigation. The results in this paper are based on a time period that was overshadowed by the most dramatic financial and economic crisis since the 1930s. Future research should investigate the robustness of these results in a post-crisis world. Moreover, the DBIs are constantly updated, with the ‘Getting Electricity’ indicator included in the latest DB Report. Its mettle should be tested empirically, as should any other indicator that may be added to the current</mark>
.4|-28.9|50.2|85.9|-35.7|
|Mongolia 2000|41.0|58.0|-17.0|47.1|72.6|-25.5|
|Mozambique 1996<sup>#</sup>|34.2|49.2|-15.0|29.3|48.4|-19.1|
|Romania1995<sup>#</sup>|57.7|79.2|-21.5|35.7|83.7|-48.0|

_Note_ :<sup>+</sup> Maximum age is 14 in Burundi.<sup>#</sup> Survey includes vision, hearing, speech, and physical disabilities.

12 The countries included in the Wagstaff (2000) study are Brazil, Cote d’Ivoire, Ghana, Nepal, Nicaragua, Pakistan, Philippines, South Africa, Vietnam.

9
# **C Data**

The data used in estimation were extracted from the US Early Childhood Longitudinal Study birth cohort (ECLS-B). This study follows from birth through kindergarten a nationally representative sample of children born in 2001. The data were collected over several waves to capture child development. We focus on the second wave, collected between January and December 2003, when the children were two years old.

## **C.1 Estimation Sample**

As mentioned in Section 3, we arrive at our estimation sample by imposing three main restrictions: we focus on two-parent and one-parent households with only one child, who is two years old; we restrict one-parent household to cases where the parent is the mother; we focus on households in which the father, if present, works full time. In addition, we impose other restrictions that are less costly in terms of observations lost. We drop households where parents’ main jobs include shift work; we eliminate households using non-relative or relative caregivers that take care of multiple children at the same time; we drop two-parent households where both parents work full-time but do not use outside child care; we drop single-mother households where the mother works full- or part-time but does not use outside child care; and we trim the top and bottom 0.5 percent of hourly earnings conditional on gender and education.

In Table C.1 we report how the main restrictions affect our estimation sample as they are progressively applied to two-parent and single-mother households, respectively. Per the first restriction (fourth and fifth columns), we keep households with three or two members– mother, father (if married), and a two-year old child. We do not drop households that go on to have more children after our sample period but drop households in which the two-year old child already has other siblings (biologically or through adoption) during our sample period, or that include additional adults.

Per the second restriction (sixth and seventh columns), from the previous set we select
explanatory variables used. For our present topic the important result to note is that the "net" effect of state ownership is to raise the level of earnings by 32-35 per cent over the level of private sector firms with African ownership. This effect is larger than foreign ownership-which is also significant but raises earnings by only 17 to 18 per cent compared to the African-owned firms.

# **3.3.3 The Distribution of Earnings**

It is not enough to consider merely the difference in mean earnings between the public and private sectors, even if we are able to control for differences in the quality of labor to some extent. The wage structure in the public sector, as it has evolved during the large changes in nominal wages in the last decade is of major interest. On the one hand, we would want to see if the earnings of workers at the bottom of the distribution have fallen so low, relative to the other sectors, that efficiency of performance is impaired. On the other hand, it is desirable that the structure of earnings should be wide enough to allow for the possibility that higher skills and responsibilities are adequately rewarded.

We refer back to the comparative distributions of per capita incomes in different sectors, including public employment, produced by Appleton and Collier from the data set of the Social Dimensions of Adjustment Survey conducted in 1988, and presented above in Table 1.2 above. It will be recalled that the researchers allowed for multiple occupations. The statistics reported are the per capita household income, pooling the income of earners of members from all sources-and not the distribution of earnings in the particular sector.

The data presented show that while mean and median per capita incomes of households in the "government" group were significantly higher than those in the other groups, they also had a smaller proportion of households in the "poor" or bottom three deciles, and a larger proportion of households in the top deciles than the over-all sample of households surveyed. We can conclude from these distributions that
Keith, D. Heat has larger impacts on labor in poorer areas. _Environmental Research Communications_ **3,** 095001 (2021).

35. Dechezleprˆetre, A., Rivers, N. & Stadler, B. The economic cost of air pollution: Evidence from Europe (2019).

36. Rutstein, S. O. & Johnson, K. The DHS wealth index. DHS comparative reports no. 6. _Calverton, Md: ORC Macro_ (2004).

37. Cattaneo, A., Nelson, A. & McMenomy, T. Global mapping of urban–rural catchment areas reveals unequal access to services. _Proceedings of the National Academy of Sciences_ **118** (2021).

19