rafmacalaba/datause-encoder-data huggingface dataset:
_Table 3. Overall scenario design in the paper_

|||**Climate change **<br>||
|---|---|---|---|
|**Socioeconomic**<br>**pathway**<br>**(2000‐2100)**|**Impacts**<br>**–**<br>**Mitigation**<br>**Policy**<br>**Present climate**|**Low climate change**<br>**RCP2.6**|**High climate change**<br>**RCP8.5**|
|**SSP4**|**Impacts**<br>1 reference SSP4|5 climate impact<br>scenarios for 450 ppm<br>with CO2effects|5 climate impact<br>scenarios for 1370<br>ppm with CO2effects<br>+ 1 scenario without<br>CO2effects|
||**Mitigation**<br>**policy**|3 stabilization<br>scenarios at 450 ppm<br>stabilization with<br>alternative<br>technological<br>assumptions||
|**SSP5**|**Impacts**<br>1 reference SSP5|5 climate impact<br>scenarios for 450 ppm<br>with CO2effects|5 climate impact<br>scenarios for 1370<br>ppm with CO2effects<br>+ 1 scenario without<br>CO2effects|
||**Mitigation**<br>**policy**|3 stabilization<br>scenarios at 450 ppm<br>stabilization with<br>alternative<br>technological<br>assumptions||

# **4. Results**

The GLOBIOM model is used to assess the impact of climate change and climate change mitigation scenarios between 2000 and 2080. In the sections below, we focus our results on primarily short term impacts (2030) and long term impacts (2080) for six large regions dominated by low and middle income countries (LMI): Latin America, Eastern Europe & Central Asia, North Africa & Middle‐East, Sub‐Saharan Africa, South Asia and Eastern Asia & Pacific. More details on the regional grouping are provided in the appendix.

# **4.1. Climate change impacts**

The scope of our analysis goes from climate change impacts on yield, through adjustments in producer strategies up to market rebalancing, which translates into price adjustments and ultimately food availability at the consumer level. This logic is also followed in the presentation of our results below. Climate shocks are modeled by shocking the 17 crop yields, as well as grass productivity in each grid cell of the model. This leads to change in level of production, market prices and consumption in the different economic regions.

## **4.1.1. Impact of climate change on yield**

The magnitude of impacts on average crop yields globally is presented in Figure 3 for the different scenarios. The climate change impacts are represented as relative change between the projected yield in 2030 or 2080 with climate change effects, compared to projected yield without these effects.

16
good will benefit, on average, from price increases, whether they come from global market fluctuations or from international policy reforms such as the international liberalization of agricultural trade policies or subsidies; but net importers will probably be hurt by those changes. In the context of the recent food crisis, for instance, this implies that countries that are net importers of food were in principle hurt, while net-exporters were the winners.

# **3.2 Households, Net-Consumers, Net-Producers and Inequality**

The net-consumer/net-producer result was introduced by Deaton (1989a), who launched a literature to study the distributional effects of price changes. Deaton (1989a) used data from the Thailand Socioeconomic Survey of 1981-82 to explore the distributional consequences of an export tax on rice across Thai households. He found that an increase in the price of rice (resulting from the elimination of the export tax) would benefit the _average_ household. The average poor, as well as the average rich, would benefit little. The benefits for the poor are small because they tend to both consume and produce lots of rice, thus selling little. The average benefit to the rich is also small because while sellers are often large, there are few of them among the rich. The gains would be much higher in the middle of the income distribution, indicating that the middle class would gain the most from higher rice prices.<sup>7</sup> The ideas introduced in Deaton’s work have been, and still are, extensively utilized in the literature. Early examples include: Deaton (1989b), who reviews applications for Cote d’Ivoire, Indonesia, and Morocco; Budd (1993), who investigates food prices and rural welfare in Cote d’Ivoire; Benjamin and Deaton (1993), who study cocoa and coffee in Cote d’Ivoire, too; Barret and Dorosh (1996), who look at rice prices in Madagascar; and Sahn and Sarris (1991), who examine structural adjustments in several Sub-Saharan African countries. Deaton (1997) provides an account of the early use of these techniques in distributional analysis of pricing policies.

Recently, there has been a rebirth of this type of work motivated by the food-price “crisis” associated with skyrocketing prices in global markets. Ivanic and Martin (2008)

> 7Notice, however, that this analysis does not take into account the fiscal implications of eliminating the tax.

10
# B. The FAIS impact on poverty

||_Food P_|_overty_|_Capabilitie_|_s poverty_|_Asset_|_poverty_|
|---|---|---|---|---|---|---|
||_Unweighted_|_Weighted_|_Unweighted_|_Weighted_|_Unweighted_|_Weighted_|
|FAIS ∞|−0.998|−1.560|−0.617|−0.913|0.849|1.210|
||(0.921)|(1.162)|(0.918)|(1.332)|(0.824)|(1.521)|
|Own revenues ∞|−0.339|−0.654*|−0.258|−0.857**|0.0452|−1.090**|
||(0.289)|(0.361)|(0.273)|(0.409)|(0.205)|(0.462)|
|Unconditional revenue ∞|−0.427|0.923|−0.895|0.815|−2.059***|0.162|
||(0.803)|(1.054)|(0.801)|(1.214)|(0.710)|(1.342)|
|Conditional revenue ∞|0.846**|0.738*|0.962**|0.857*|0.981***|1.114**|
||(0.410)|(0.423)|(0.415)|(0.463)|(0.378)|(0.566)|
|Direct workers ‡|−12.67*|−14.16***|−13.78*|−10.64|−10.68|4.684|
||(7.116)|(5.261)|(7.757)|(6.756)|(7.875)|(10.66)|
|Indirect workers ‡|10.10|28.44***|9.447|27.95**|6.082|25.03|
||(11.34)|(10.74)|(12.80)|(12.66)|(12.75)|(17.22)|
|Productivity per occupied ‡‡|0.488|0.715*|0.559|0.719|0.612**|0.620|
||(0.354)|(0.375)|(0.353)|(0.439)|(0.303)|(0.530)|
|Private business assets ‡‡|<br>−0.310|<br>−0.277|<br>−0.331|<br>−0.328|<br>−0.330|<br>−0.320|
||(0.294)|(0.283)|(0.292)|(0.329)|(0.248)|(0.421)|
|Semiurban|−2.824|−2.280|−3.386|−2.828|−3.933**|−3.548*|
||(2.244)|(2.328)|(2.241)|(2.338)|(1.919)|(2.054)|
|City|−4.110*|−4.591*|−4.655*|−5.428**|−4.999**|−6.180***|
||(2.433)|(2.540)|(2.440)|(2.567)|(2.107)|(2.281)|
|Year 2010|0.879***|0.999***|1.444***|2.096***|2.745***|4.804***|
||(0.337)|(0.364)|(0.343)|(0.428)|(0.307)|(0.511)|
|Year 2014|<br>−1.441***|<br>0.422|<br>−1.457***|<br>0.806|<br>−1.349***|<br>0.798|
||(0.390)|(0.458)|(0.397)|(0.506)|(0.353)|(0.562)|
|N|<br>6,107|<br>6,107|<br>6,107|<br>6,107|<br>6,107|<br>6,107|
|R-2|0.024|0.031|0.033|0.057|0.070|0.204|
|Municipalities|2,118|2,118|2,118|2,118|2,118|2,118|

_Note:_ Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants. The average income corresponds to the average per capita household income in each municipality in logarithms. The deciles of income correspond to the cut-off points of dividing in each municipality the households into ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Model with fixed effects by the municipality and by year for 2005, 2010, and 2014, also using the FAIS formulated as the instrumental variable. Weighed results show the same model adding importance by the size of the municipal population. ∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 40
**Figure 2: NEET Rates in MENA Countries**

<!-- Start of picture text --> 50.<br>37.5<br>25.<br>12.5<br>0.<br>Yemen Iraq West Morocco Egypt Tunisia Lebanon Algeria Saudi Arabia U.A. Emirates<br>Bank/Gaza<br><!-- End of picture text --> _Source_ : ILOSTAT, <u>https://ilostat.ilo.org/.</u>

# 3. Research Findings on the Status of NEETs Worldwide

As mentioned in the introduction, a quantitative investigation of the condition and dynamics of NEETs in Morocco is rather new, compared to other parts of developing world where new studies have already become available. Employing longitudinal data, Ranzani and Rosati (2013) present evidence concerning the extent, characteristics, and evolution of the NEET phenomenon in Mexico over a 10-year period. In addition, they investigate the existence and extent of state dependence by disentangling unobserved heterogeneity from genuine state dependence. For example, they find that, compared to other NEETs, female and lower-educated youth are more likely to remain in this status than be employed.

Looking at Turkey, Bilgen Susanli (2016) examines the determinants of its NEET picture, drawing on data from the Household Labor Force Surveys over the 2004-2013 period. A logit analysis indicates that gender and educational attainment are key factors in explaining who is or is not in NEET. A greater number of household members who are in employment is associated with a lower likelihood of NEET. Transition matrixes analysis reveals that NEET status remains highly persistent despite the substantial fall over the sample period. In South Africa, Akinyemi and Mushunje (2017), investigating the determinants of rural youth participation in agricultural activities, show that 21 percent of youth are NEET, 77 percent of them in the 20–24 age bracket. Variables such as age, government funding and parent participation in farming increase the likelihood of young people’s participation in agricultural activities. By contrast, being married, having young children, and receiving social grants reduce the likelihood.

Cabral (2018), focusing on Senegal, shows that about 40 percent of young people are NEET. In his analysis, the key factors affecting the probability of being NEET are the existence of a physical and 8
<!-- Start of picture text --> it<br>st<br>1.5 os<br>1 :<br>5<br>M a<br>0 - = - SS ~<br>25 -2 -15 -l -.5 0 5 1 1.5<br>Reading Test Score (in SD)<br>—— Outcome ===" Model!<br>( Model I+Z = Model I+Z+S<br><!-- End of picture text --> <!-- Start of picture text --> na<br>1.5 ms<br>1 \<br>5<br>”<br>0 . ¢“ =<br>-2 -1.5 -1 -.5 0 5 1 1.5<br>Quantitative Reasoning Test Score (in SD)<br>—— Outcome ===" Modell<br>() Model I+Z = Model I+Z+S<br><!-- End of picture text --> <!-- Start of picture text --> 2.5<br>2<br>1.5<br>1<br>5<br>-.5 0 5 1<br>Graduation Rate<br>—— Outcome ===" Model!<br>WI Model I+Z -—— Model I+Z+S<br><!-- End of picture text --> <!-- Start of picture text --> 2.5<br>2<br>a<br>2 .<br>fp<br>1.5 f<br>4 y<br>iv A<br>1 eN<br>By<br>A<br>5<br>0<br>-1 -.5 0 5 1<br>Formal Employment Rate<br>—— Outcome ====* Model I<br>Wi Model +Z —-——— Model I+Z+S<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>3 ¥<br>\<br>‘<br>2<br>E<br>1<br>y<br>or<br>6 ° s 1] 9 os 10 103<br>Wages (in log)<br>—— Outcome <-===* Modell<br>WU) Model +Z = Model I+Z+S<br><!-- End of picture text -->
Policy Research Working Paper 8786

# **Abstract**

Growth fluctuations exhibit substantial synchronization across countries, which has been viewed as reflecting a global business cycle driven by shocks with worldwide reach, or spillovers resulting from local real and/or financial linkages between countries. This paper brings these two perspectives together by analyzing international growth fluctuations in a setting that allows for both global shocks and spatial dependence. Using annual data for 117 countries over 1970–2016, the paper finds that the cross-country dependence of aggregate growth is the combined result of global shocks summarized by a latent common factor and spatial effects accruing through the growth of nearby countries—with proximity measured by bilateral trade linkages or geographic distance. The latent global factor shows a strong positive correlation with worldwide TFP growth. Countries’ exposure to global shocks rises with their openness to trade and the degree of commodity specialization of their economies. Despite its simplicity, the empirical model fits the data well, especially for advanced countries. Ignoring the cross-country dependence of growth, by omitting spatial effects or common shocks (or both) from the analysis, leads to a marked deterioration of the empirical model’s in-sample explanatory power and out-of-sample forecasting performance.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at lserven@worldbank.org.

_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
fewer hours (6.3 conditional on reporting working). Among the self-employed we detect a similar but marginally smaller reduction in the number of days worked conditional on working (of 0.6 days), but no reduction in the unconditional number of hours or days worked.

Assessing which labor market reports are most accurate is challenging because we do not observe true values.<sup>3</sup> While phone interviews may reduce social desirability bias by increasing anonymity, they could also elicit impatience and recall error. We are not in a position to ascertain which modality is to be preferred, but the survey-induced differences in labor market reporting are of interest in and of themselves in an era where researchers are progressively reliant on phonebased surveys (Dabalen et al, 2016). The survey modality sensitivity of labor market reports by certain types of self-employed workers also calls for caution when making inferences using indicators derived from surveys with different modalities, especially when making comparative statements about the labor market outcomes of different types of workers.

Our results regarding the impact of the reference period on reporting also point to the possibility that the reference period of one week (the norm in most labor force surveys) may systematically underestimate the employment rate of self-employed workers. It may also overestimate the number of days and hours worked for especially the wage employed. Put differently, shorter reference periods are associated with reporting more spells of shorter average duration, which suggests the possibility of more accurate recall, but more research is needed to confirm this hypothesis.

The rest of the paper is organized as follows. Section 2 reviews the literature on recall bias, telescoping and interview mode in labor surveys and elaborates on the paper’s hypotheses. Section 3 describes the experiment. Section 4 presents the data. Section 5 documents survey modality effects by comparing the reports of the weekly in person and weekly phone groups. Section 6 documents reference period effects by comparing the reports of the weekly phone group with the reports of those in the triweekly phone group.

> 3 In retrospect, our design for assessing the impact of shortening the reference period was not ideal; it would have been better to interview workers each day of the week. Time-use diaries are of course more of a gold standard, but they often have compliance issues which may require daily interviews, in any case, to ensure completion.  Such approaches are likely the closest possible approximations to respondents’ true labor supply, given that third-party verification of their time use is very challenging in this context of widespread informality and self-employment. Employer records, for instance, would only be available for a relatively small subsample of the population in this context.

4
# **<mark>Table 3B –Impact of FD Law on Bank Loan Margins to Local Governments Given State Public Debt</mark>**

<mark>This table reports OLS estimates of the impact of the FD Law on credit margins of bank loans to local governments of varying ex ante</mark> public indebtedness. The variable _Interest Rates,m_ is the annualized average interest rate (in percent) of the outstanding loans of state government _s_ in month _m_ . The variable _Collaterals,m_ is the average share of bank loans of state government _s_ in month _m_ that are guaranteed. _Maturitys,m_ is the average maturity (in logs) of the outstanding loans of state government _s_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the state-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent <u>levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||Interest<br>|Rates,m<br>|Collat<br>|erals,m<br>|Matu<br>|ritys,m<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Postm|1.49***||0.00***||-0.00||
||(0.07)||(0.00)||(0.02)||
|Postm*DebtStates,16Q1|0.86***|0.86***|0.00**|0.00***|0.01|0.01|
||(0.18)|(0.09)|(0.00)|(0.00)|(0.04)|(0.04)|
|Observations|1,080|1,080|1,080|1,080|1,080|1,080|
|R-squared|0.59|0.91|0.46|0.70|0.60|0.60|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Month FE|No|Yes|No|Yes|No|Yes|

49
Examining a large-scale school construction program in Indonesia in the mid-to-late 1970s and identifying a child's exposure to the program through his (her) date of birth and the region of birth, Duflo (2001) estimates that the program resulted in an average increase of 0.1 to 0.2 years of education and roughly a 2 percent increase in wages around two decades later. Wantchekon _et al._ (2015) implement an innovative study by collecting longitudinal data on the first students in colonial schools in colonial times and their direct descendants and extended families in Benin. This study finds that Beninese who benefited from access to the first schools established were 96 percent and 10 percent respectively more likely to attain primary education and secondary education or more. These affected individuals and their descendants also had higher living standards, were less likely to be farmers, and were more likely to be politically active; furthermore, an uncle’s education also had positive impacts on his nephews and nieces.<sup>3</sup> Our study makes a fresh contribution to the existing literature in several aspects. First, we can specifically attribute the increased education achievement to the long-term impacts of schooling policies (that occurred in the context of the (First) Indochina War). During the Indochina War, Vietnam’s political system was turned into a bimodal regime, in which parts of the country were occupied by the French government while some other parts were under the Vietnamese government. This in turn resulted in a unique natural policy experiment where two different education systems were concurrently in operation: the new Vietnamese system provided free,

> 3 There are some other related studies on China that offer somewhat mixed results. Meng and Gregory (2002) study the life-changing Chinese Cultural Revolution, during which most schools in urban China ceased operation over the period 1966-1977 and find the affected cohorts less than half as likely to possess a university degree 20 years later. This study, however, just relies on cohorts’ temporal exposure for identification. Analyzing data on twins, Zhang, Liu, and Yung (2007) did not find the Chinese Cultural Revolution to have significantly negative effects on the returns to schooling.

4
personal assessments as revealed through surveys. According to Linn (2010) this tool could be useful in analyzing urban poverty issues, policies, instruments and political economy. So far the method has not been widely applied to inform policy and program design in developing countries and there are only few cases in which urban and specifically urban poverty issues have been addressed.<sup>10</sup> Linn therefore proposes a research study that systematically reviews and evaluates the happiness and life satisfaction literature to see whether and how the instrument can be effectively applied to address key urban poverty policy and political economy issues.

Impact analysis is a relatively new tool with which to measure in a scientifically reliable way the impact of policy, program and project interventions, by using an experimental approach in which treatment groups are compared with control groups without treatment after the implementation of an intervention (the treatment) against a common baseline before treatment was applied. This approach has been advocated and widely used in the health and education areas, much less so to date in the area of urban and urban poverty interventions. A research initiative to explore the potential for this tool in evaluating the impact of urban poverty programs represents a well‐defined and potentially high pay‐off undertaking.

Lastly, strategic planning tools are now widely employed to support improved urban development at the national and city level, but their links to broader national planning and poverty reduction strategy mechanisms are poorly articulated, and little is known about how effectively these planning tools serve the objective of addressing urban poverty issues. Therefore, a review of commonly used planning tools would provide useful inputs into national and urban strategy development and implementation.

# 5. <u>The imperative of scaling up in the fight against urban poverty</u>

In conclusion, we follow Linn (2010) in stressing the need for scaling up successful interventions in the fight against urban poverty. Too often, projects, programs and policy or institutional initiatives are one‐time and short‐lived, only to be replaced or superseded by other new interventions. Replication and scaling up of interventions that have been shown to work is something that needs to be pursued much more persistently by all actors on the ground: by national and local governments, by CSOs and by aid organizations. Research on urban poverty reduction should incorporate a search for an answer to the scaling up question: If an intervention is successful, how can it be replicated and scaled up to achieve comparable positive outcomes across a broader universe of the poor in a city, in a country and world‐wide.

After this overview of priority research issues related to urban poverty reduction, we now turn to review selected priority areas of research that were analyzed in greater depth as part of our urban research scoping exercise: land and housing

> 10 See Graham (2008) for further explanations of the approach and some applications to urban issues in Latin America.

12