rafmacalaba/datause-encoder-data huggingface dataset:
Table 8: Effect of Average Temperature Difference on Firm Performance: Heterogeneity in GDP and Security Index

||LIC +|LMIC|U|MC||HIC|
|---|---|---|---|---|---|---|
||Low|High|Low|High|Low|High|
|Log(Revenue)|-0.174|-0.348|0.054|-0.102|0.238|0.096|
||(0.085)|(0.084)|(0.053)|(0.051)|(0.074)|(0.072)|
|Log(Rev. per worker)|-0.07|-0.23|0.058|-0.06|0.162|0.076|
||(0.051)|(0.048)|(0.031)|(0.029)|(0.042)|(0.04)|
|Invest (binary)|0|-0.006|0|-0.006|0.001|-0.005|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|
|Log(Investment)|-0.175|-0.083|-0.062|-0.075|0.029|-0.069|
||(0.14)|(0.132)|(0.083)|(0.075)|(0.108)|(0.1)|
|Log(Avg. wage)|-0.017|-0.154|0.029|-0.054|0.067|0.026|
||(0.041)|(0.04)|(0.026)|(0.024)|(0.036)|(0.034)|
|Log(Capacity Util)|-0.002|0.025|-0.007|0.014|-0.011|0.004|
||(0.021)|(0.02)|(0.013)|(0.012)|(0.018)|(0.017)|
|Log(Energy Intensity)|-0.022|0.135|-0.016|0.104|-0.012|0.08|
||(0.06)|(0.057)|(0.04)|(0.039)|(0.058)|(0.056)|
|Innovation|-0.029|-0.014|-0.016|-0.008|-0.007|-0.003|
||(0.015)|(0.014)|(0.009)|(0.009)|(0.013)|(0.012)|

Notes: Each row represents a different regression of a given firm performance variable on a variation of the model specified in equation 1, where we model heterogeneity by GDP and an index of security. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of the security index and columns labeled “high” evaluate the estimated model at the 90th percentile of the security index within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

43
News Sentiment in Destination Countries and Migration Choices: Evidence from Libya<sup>∗</sup> Michele Di Maio<sup>†</sup> Nelly Elmallakh<sup>‡</sup> Valerio Leone Sciabolazza<sup>§</sup> University and IZA World Bank Sapienza University Sapienza University and IZA

**Keywords:** Migration, news sentiment, GDELT, Libya, Europe **JEL codes:** F22, J61, L82.

> ∗We thank the participants to the 16 _th_ AFD-World Bank Conference on Migration and Development (Boston University), the Conflicts and the Economy Workshop (Universit`a Cattolica of Milan), and the EBRD Research Seminar (London) for their comments and suggestions. We also thank Margherita Bove, Francesca Calamunci, and Cecilia Nardi for excellent research assistance. The authors gratefully acknowledge the financial support from the Office of the Chief Economist for the Middle East and North Africa (MNACE) under the regional Labor and Gender Research Programs. The views expressed in this paper are those of the authors and do not necessarily reflect the position of the World Bank. All errors are our own.

> †michele.dimaio@uniroma1.it, Sapienza University of Rome (Italy) and IZA

> ‡nelmallakh@worldbank.org, World Bank: _Corresponding Author_

> §valerio.leonesciabolazza@uniroma1.it, Sapienza University of Rome (Italy)
Policy Research Working Paper 5110

# **Abstract**

By 2050, two-thirds of the world’s population will live in cities, and the greatest growth in urban populations will take place in the least developed countries. This presents many governments with considerable challenges related to urban governance and the provision of services and opportunities to a burgeoning urban population. Among the concerns is that large youth bulges in urban centers could be a source of political instability and violence. Here, we assess this claim empirically using newly collected data on city-level urban social disorder, ranging from non-violent actions, such as demonstrations and strikes, to violent political actions, such as riots, terrorism, and armed conflict. The dataset covers 55 major cities in Asia and Sub-Saharan Africa for 1960–2006. The study also utilizes a new United Nations Population Division dataset on urban populations by age and sex. The study further considers factors that could condition the effect of age structure, in particular the level of informal employment, economic growth, education, and gender imbalances. The analysis finds that large male youth bulges aged 15–24 are not generally associated with increased risks of either violent or non-violent social disturbance. Furthermore, the proxy measures of “youth exclusion” do not seem to increase the risk that large urban male youth bulges are associated with either form of disturbance. However, several other factors that may be associated with higher levels of youth exclusion—notably absence of democratic institutions, low economic growth, and low levels of secondary educational attainment—are significantly and robustly associated with increasing levels of urban social disturbance.

This paper—a product of the Post Conflict & Social Development Unit, Africa Region, with support from the Trust Fund for Environmentally and Socially Sustainable Development and the Norwegian Ministry of Foreign Affairs—is part of a larger effort to build a solid body of theoretical and applied research on the links between youth exclusion and political violence. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at henriku@prio.no and krihoe@prio.no.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_51_ 35. Considering that a similar policy had to be abandoned when it was applied in C6te d'Ivoire in 1990, and in light of the discussion below on the political sustainability of adjustment packages, one cannot but help but wonder whether this policy could be implemented in a less authoritarian regime than Morocco's.

36. We do not report on the devaluation experiments for Malaysia since devaluation is also accompanied by less fiscal restraint. It turns out that this package has a slightly negative effect on the balance of payments, probably because devaluation is accompanied by a less restrictive fiscal policy. The package also results in slightly higher growth than in the base run.

37. It is interestmg to contrast this outcome with the more direct alternative of raising export taxes. Lambert, Schneider and Suwa show that raising export taxes generates significant indirect effects, which dampen the initial fiscal improvement.

38. The controversy on the effects of devaluation on output (and even more so on income distribution ) is far from settled. For a recent survey of the theoretical debate see Lizondo and Montiel (1989). For empirical results, see Edwards (1989). It should therefore not be surprising that country studies find different results.

39. In the Ecuadorian case, adjustment through a cut in current expenditures is also found to be preferable to that through a cut in capital expenditures on distributional and efficiency grounds. This is because a cut in capital expenditures reduces the real incomes of unskilled workers whereas a cut in current expenditures results in a cut in living standards for skilled workers. See the article in this issue by de Janvry, Sadoulet, and Fargeix (figure 1).

40. Interestingly, the simulations show that th3 amnount of foreign aid required is only about twothirds of the required transfer because of the growth-inducing effects of foreign aid (see de Janvry, Sadoulet, and Fargeix (sectionl Sb). This result, which implies that timely aid can make the difference between a sustainable adjustment package and one that fails, is often overlooked. In the Ecuadorian case, timely debt relief did not follow the earthquake in March 1987.
# **_3.  An Empirical Illustration_**

We now use household level data for Guinea to illustrate the use of the above indicators to identify commodities or groups of commodities the taxation of which would be more or less (at the margin) desirable from the standpoint of poverty reduction. The database is a nationally representative sample of 4,416 households from the 1994 Integrated Household Survey.

**Table 3.1: A Poverty Profile for Guinea (1994)**

||Headcount|PovertyGap|Squared PovertyGap|Watts|
|---|---|---|---|---|
|Conakry|6.70|1.30|0.38|1.56|
|Other Urban|24.32|6.58|2.59|8.60|
|Rural|52.52|17.77|7.86|24.08|
|Guinea|40.27|13.19|5.75|17.80|

Source: Author’s calculations (results in percentage)

**<u>Figure 3.1 Guinea (1994):A Normalized TIP Curve</u>**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br><!-- End of picture text --> <u>Cumulative Percentage of Population</u> Source: Author’s calculations 16
TABLE 5 EGYPT 1975-80 THE DIRECT IMPACT OF **VEANING** ON NEXT PREGNANCY OF<sup>MOTHER (before</sup><sup>**age**60 months)</sup> % per cent weaned (ceased breastfeeding prior to next pregnancy) RR relative risk of pregnancy of mother (weaned<sup>/not weaned)</sup> AR attributable risk of pregnancy of<sup>mother</sup>

-------------FERTILITY CONTROL--------------ALL (N-7375 children) NO (N-4714 children) YES (N-2661 children) EST. (95 % CI) EST. (95 % CI) EST. (95 % CI)

% 34.58 (33.49 - 35.66) 25.82 (24.57 - 27.07) 50.09 (48.19 - 51.99) RR 3.02 ( 2.60 - 3.50) 5.21 ( 4.04 - 6.71) 1.94 ( 1.59 - 2.37) AR 41.08 (35.38 - 46.28) 52.07 (43.45 - 59.38) 31.97 (22.31 - 40.43) WEANED before 12 months % 8.98 ( 8.32 - 9.63) 5.71 ( 5.04 - 6.37) 14.77 (13.42 - 16.12) RR 2.91 ( 2.43 - 3.47) 4.69 ( 3.43 - 6.42) 2.06 ( 1.63 - 2.60) AR 14.60 (11.04 - 18.02) 17.39 (11.20 - 23.14) 13.51 ( 7.89 - 18.79) WEANED at 12 months or later % 42.19 (40.74 - 43.64) 37.66 (35.77 - 39.56) 48.01 (45.80 - 50.22) RR 3.07 ( 2.48 - 3.79) 6.06 ( 4.04 - 9.10) 1.84 ( 1.41 - 2.41) AR 46.62 (38.15 - 53.93) 65.60 (52.60 - 75.04) 28.74 (15.52 - 39.90) WEANED before 12 months based on log-logistic model controlling for: child death, parity, age of mother, preceding birth interval<sup>length,</sup> sex, region, urban/rural residence, fertility<sup>control,</sup> education of mother, education of father based on data from the Egyptian Fertility Survey 1980 restrictions: children born within 60 months of survey to<sup>married women</sup> last two children only no multiple births no marital disruption prior to child death Muslim only note: some children still breastfeeding at survey (not weaned) some children breastfed until died (not weaned)

*-not statistically distinguishable from 0.0 for per cent (%) and attributable risk (AR) and from 1.0 for relative risk (RR)

37
has insignificant or small labor supply impacts.

Our paper contributes to this literature by shedding new light on the relative impacts of in-kind or cash transfers on consumption, and labor supply, using data from a conditional cash and food transfer program in the poor rural areas of Southern Mexico called PAL ( _Programa Apoyo Alimentario or Food Support Program_ ). The principal objective of the program is to improve the food and nutrition conditions of targeted households living in rural poor communities with a population less than 2,500 and with a high and very high marginality index. The program is targeted to localities that are not covered by other federal programs with a nutritional component such as _Oportunidades_ or _Abasto Social de Leche Liconsa._ The original program transfer consists of a monthly food basket with a value of 150 Mexican pesos or about US$13 for the federal government (median share of transfer to pre-program consumption is 8.9%, mean is 11.5%) and it is accompanied by an educational component (the requirement to attend diet, nutrition, and health-related educational sessions).

In this paper we do not evaluate the extent to which the nutritional objectives of the program are attained, but rather we examine the impacts of cash and in-kind transfers on a few key dimensions of household welfare as measured by food and total consumption, poverty, and labor supply.<sup>4</sup> A distinguishing feature of the PAL data is that they are based on a randomized design, with randomization of the type of program benefit received at the local level.

Specifically, for the purpose of evaluating PAL, the selected communities were randomly assigned into a control group (C) and three treatment groups: a group (T1) that received the

> 4 An analysis of the nutritional impacts of the PAL program entails a more detailed analysis of the relative impacts of cash and in-kind transfers on the consumption of micronutrient-rich food groups such as fruits and vegetables and on the anthropometric measures of children and adults. A more detailed description of the program, the evaluation design and estimates of the impact of the program on key nutritional outcomes can be found in Gonzalez-Cossio et al. (2006).

4
Combining equation this with equations 8 and 10 yields: This decomposition breaks changes in the adjusted headcount ratio into changes in the number of poor attributed to each dimension, _Sd_ , changes in the intensity of multidimensional poverty attributed to each dimension, _SA_ , and an interaction effect. The logic behind larger contributions of dimensions in explaining the changes in intensity is very clear: if the majority of poor are no longer deprived in a particular dimension, this dimension will contribute more to the decrease in intensity.

In the case of panel data, larger contributions of a dimension to changes in _H_ are also intuitive. As an illustrative example, consider a case where the change in the headcount ratio occurs over a short period of time so that the demographic variables z remain constant. Assume that many dimensions change, but each individual only experiences changes in one dimension. If all individuals whose deprivation share crossed the multidimensional cut-off experienced change in the same dimension, then this dimension would be the unique contributor to the change. Indeed, Apablaza and Yalonetzky (2013) show that the change in the headcount in the case of panel data can be decomposed into a weighted difference in the transition probabilities of moving in and out poverty. Individuals at the margin of transition may be more prone to changes in particular dimensions, which would influence the transition probabilities and would contribute more to the variation in the headcount.

In the case of repeated cross-section data, changes in the headcount biased towards a particular dimension may arise due to the inclusion of people with different deprivation profiles in the sample, at one of the points of time. Thus, changes would be biased by the inclusion of larger shares of people deprived in one dimension. In applied work, dimensions do not change one at a time for each individual; dimensions may be correlated; and cross-sectional data is the rule and not the exception, especially in the surveys used to measure poverty in developing countries. A general framework to decompose changes in _H_ should thus take into account the notion that dimensions are jointly distributed, that certain demographic profiles are more likely to experience changes in particular dimensions, and that only individuals with 10
Lewis and Lockheed

**_Other correlates of exclusion._** The socially excluded also suffer on account of geographical isolation, weak home support for schooling due to past inequities that limited educational opportunities for parents, cultural factors, and – above all – poverty.

Socially excluded communities tend to be isolated geographically, which affects both whether a school is available in the community and the quality of that school. This, in turn, affects children’s school participation and learning. In Lao, PDR, the poor quality of rural schools contrasted sharply with schools in urban areas, and was negatively related to girls’ probability of attending school (King and van de Walle 2007). In some countries, rural residence is confounded with other bases of exclusion (such as ethnicity, caste, tribe, and poverty), so that controlling statistically for these characteristics often completely eliminates the independent association between rural residence and school participation. For example, analyses of household data from India and Nigeria that controlled for household wealth and other factors found that rural children were not disadvantaged in attending school (UIS 2006).

The low educational attainment of parents or the household head affects enrollment in most circumstances. Recent studies from Guatemala, Lao PDR, Nepal, Pakistan and China show that where schooling is not universal, higher levels of parental education have sizable and significant effects on children’s enrollment (Lewis and Lockheed 2007). In Nepal, children whose parents had some formal education were 2.5 times more likely to attend school than children whose parents lacked formal education (Stash and Hannum 2001). In Lao PDR, children of more educated parents were more likely to be attending school (King and van de Walle 2007).

In many countries cultural factors work to remove rural and indigenous girls from school, particularly after primary school. Parental concerns over allowing adolescent girls to mix with boys and the lack of separate latrines are often paramount for keeping girls at home (Herz and Sperling 2004). Finally, parental beliefs about the current and future occupational and social roles of boys versus girls affect their expectations for children’s schooling. For example in Bangladesh and rural China some parents think that a good marriage is more important than a good job, which affects their expectations for daughters’ schooling (Mahmud and Amin 2006; Li and Tsang 2005).

Poverty is the most persistent and significant reason why marginalized girls do not enroll in or complete primary or secondary school. In Guatemala poor Mayan females have the lowest school 15
**Figure 7:  Evolution of export volumes and share for different sectors**

<!-- Start of picture text --> 160 80%<br>120 60%<br>80 40%<br>40 20%<br>0 0%<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Agriculture ($) Mining ($) Manufacturing ($)<br>Agriculture (%) Mining (%) Manufacturing (%)<br>Million USD ($)<br>Fraction of total (%)<br><!-- End of picture text --> Note: The solid lines show absolute export volumes (in USD) on the left-scale; the dashed lines show the relative share of the three sectors in the total (in percentage) on the right-scale. We do not report the absolute export volumes in 2016. These are notably lower for all three sectors, but we are uncertain to what extent this is due to incomplete reporting or an accurate reflection of the export evolution.

But the greater relative importance of manufacturing is a welcome evolution for several reasons. The upside potential is less likely to be capacity constrained than in the other two sectors. Compared to mining in particular, the greater labor intensity of manufacturing is good for the employment potential. Prices tend to be less volatile than in agriculture or mining and the aggregate export volumes shown in Figure 7 are least volatile for manufacturing. The more broadly based export market participation that we documented for manufacturing in Figures 5 and 6 can be one factor contributing for the greater stability. Exports in the other two sectors are more sensitive to idiosyncratic firm-specific successes or failures.

Using the sectoral data from EORA we can construct the growth rate in the share of output that is exported for the three sectors. For agriculture and mining the trend is not positive. The share expanded from 1995 to 2005, but contracted from 2005 to 2015. For manufacturing there is an increase from 2005 to 2015, but the share only goes from 1.4% to 2.0% of output which is barely higher than the share already attained in 1995 which was 1.8%. Of course, the aggregates in EORA includes the output of smaller firms and probably also an estimate for the informal sector where exports are likely to be negligible.

It is difficult to compare our findings for Rwanda with other countries because administrative data that covers all firms is rarely available. Ethiopia is one country with a high quality manufacturing firm census that several researchers have used. Recently, Siba and Gebreeyesus (2017) report that in 2008-2009 (the last year in their sample) only 4.0% of the Ethiopian manufacturing firms show positive exports.<sup>8</sup> While the share of exporters did not 8 Note that their sample only includes medium and large firms with at least ten employees.

14
which the three packages described above were randomly allocated among the eligible households.<sup>8</sup> Table 1 presents the randomization results for the sample of eligible households with children between 6 and 15 years old.<sup>9</sup> The differences between households in treatment and control communities are small and not statistically significant. Similarly, the differences between households with the productive investment package and households in the control communities are generally small and not significant.

# **III. Data, Descriptive Patterns, and Hypotheses**

# _III.1. Data_

The data comes from a household panel in the control and treatment communities. In treatment communities, data were collected from all households. In control communities, a random sample of households was selected to obtain a control group of equal size as each of the three intervention groups (of about 1000 households). The follow-up data was collected 9 months after the start of the program. The attrition rate of the second round was 1.3 percent of the original households.<sup>10</sup> The attrition is uncorrelated with treatment.

The main household survey, collected in both rounds, contains household and individual level data on various socio-economic indicators on approximately 4400 households. In the follow-up survey, additional questions on child labor were added, to capture in particular children’s work in chores (wood and water gathering) and domestic work. The quantitative data was complemented with qualitative work, based on focus groups and semi-structured interviews with a wide set of beneficiaries and other local actors in treatment and control communities (see Aguilera et al., 2006).

# _III.2. Child labor allocation patterns_

> 8 Participation by the invited beneficiaries to the assemblies and lotteries was near 100%.

> 9 Take-up of the overall program among eligible households was 95%, with the main attrition due to exclusion by leaders (see footnote 6). Take-up of the productive investment grant among households in the program was near 100%.

> 10 The low attrition rate was the result of tracking both households and individual household members that had moved since the baseline.

8