rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 10. Performance on different elements of the data governance framework, by country income**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>High income Upper middle Lower middle Low income<br>income income<br>Existence of laws (e-commerce, personal data protection, ATI/open data,<br>cybercrime)<br>Robustness of regulatory framework<br>Administrative aspects<br>Average scores<br><!-- End of picture text --> _Source:_ Author’s calculation based on results from the diagnostic.

_Note:_ Administrative aspects include existence of digital ID system for e-services, mandatory use of common technical standards to ensure government system interoperability, mandatory use of data classification categories across government systems, adoption of open licensing regime, private sector ability to digitally verify/authenticate ID, and existence of mandate for voluntary licensing of access to essential data on FRAND basis. Robustness of regulatory framework is based on the average score of all regulator attributes measured under the Global Data Regulation diagnostic except for the administrative aspects questions, as well as the questions on the existence of e-commerce law, personal data protection law, ATI law, open data law or policy, and cybercrime provisions.

**Figure 11. Comparison between existence of key legislation and its coverage of regulatory good practice**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Existence of e- Robustness of e- Existence of Robustness of Cybercrime Cybersecurity<br>commerce commerce law personal data personal data<br>protection law protection law<br>High income Upper middle income Lower middle income Low income<br>Percentage of countries<br><!-- End of picture text --> _Source:_ Author’s calculation based on results from the diagnostic. _Note: Robustness_ means that the corresponding legal framework contains all the regulatory attributes (getting a full score on all relevant questions) assessed under the Global Data Regulation diagnostic.

The contrast between the existence of laws and the robustness of those laws is particularly salient for data protection legislation (figure 11). Though more than 60 percent of upper-middle-income countries have legal frameworks on data protection, none of them has adopted all the regulatory good practices measured 25
# **Job Displacement and Reallocation Failure: Evidence from Climate Shocks in Morocco**<sup>∗</sup>

Federica Alfani<sup>†</sup> Vasco Molini<sup>‡</sup> Giacomo Pallante<sup>§</sup> Alessandro Palma<sup>¶</sup>

## **J.E.L. codes:** Q1, Q54, J21

**Keywords:** Drought, Climate change, Job displacement, SPEI, Unemployment.

> ∗The authors acknowledge financial support from the World Bank. We thank colleagues from the Haut Commissariat au Plan of Morocco (HCP) for their excellent data preparation. We also wish to thank the participants at the IAERE 2022 and IFAD 2022 conferences for their suggestions. We also benefited from useful discussions with Luca Citino, Daniele Curzi, Salvatore Di Falco, and Giuseppe Maggio.

> †World Bank, E-mail:falfani@worldbank.org

> ‡World Bank E-mail: vmolini@worldbank.org

> §School of International Studies, University of Trento, E-mail: giacomo.pallante@unitn.it

> ¶Corresponding author. Gran Sasso Science Institute (GSSI), Via Francesco Crispi 7, 67100 L’Aquila, Italy; CEIS Tor Vergata, E-mail: alessandro.palma@gssi.it
# **Table 11:  GGU estimation results by sector (fixed effects)**

||% of Emi<br>|ssions-Wei<br>|ghted Estimatio<br>|n Results<br>|% of Priority<br>|
|---|---|---|---|---|---|
|Sector<sup>a</sup>|No Trend|Mixed|Decreasing|Increasing|Emissions|
|Soils|0.0|9.2|0.6|90.2|20.4|
|Livestock|2.9|28.8|4.6|63.6|19.9|
|Coal|5.7|22.5|5.1|66.7|25.1|
|Gas|4.5|25.5|44.7|25.3|14.5|
|Oil|26.7|38.9|23.8|10.6|12.7|
|Landfills|13.0|16.5|18.0|52.4|3.1|
|Wastewater<br>aSectoral abbrevi<br>Coal [Fuel Exploita<br>[Solid Waste Land|1.0<br>ations (from Ta<br>tion – Coal]; G<br>fills]; Wastewat|19.7<br>ble 1): Soils<br>as [Fuel Expl<br>er [Waste W|18.3<br>[Agricultural Soil<br>oitation – Gas]; O<br>ater Handling]|61.0<br>s]; Livestock [En<br>il [Fuel Exploita|4.2<br>teric Fermentation];<br>tion – Oil]; Landfills|

# **6.3 Results by World Bank Income Group**

Our results by World Bank income group (Table 12) suggest that global accountability has begun to affect methane emissions intensity in high-income countries. They have an imbalance toward Decreasing (34.6% vs. 24.0%), but they account for only 14.5 % of EDGAR emissions for our priority GGUs. The other three income groups have very large imbalances toward Increasing. The greatest imbalance (65.2% vs. 9.0%) is in upper-middle-income countries, which also dominate methane emissions (56.5% of the total).

**Table 12: GGU estimation results by World Bank income group**

||% of E<br>|missions-Wei<br>|ghted Estimation<br>|Results<br>|% of Priority<br>|
|---|---|---|---|---|---|
|Income Group|No Trend|Mixed|Decreasing|Increasing|Emissions|
|Low|4.9|27.5|15.1|52.6|1.7|
|Lower Middle|8.0|23.8|10.4|57.8|27.3|
|Upper Middle|6.2|19.6|9.0|65.2|56.5|
|High|5.0|36.4|34.6|24.0|14.5|

27
There are other aspects of the data that cast further doubt on PILE, at least for Vietnam as a whole. We have seen that for rural Vietnam as a whole, living standards tend to be higher for the landless. Suppose that a change in the share of the rural population that is landless comes about without any change in the distributions of consumption within each of the two groups. In other words, a representative household among farmers is transformed into a representative household among the landless. Rising landlessness must then cause a fall in the poverty rate. To see why, note that _H_ = _H L_ > 0 + ( _H L_ = 0 − _H L_ > 0 ) _LL_ where (as before) _LL_ is the proportion who are landless. Then, under the within-group neutrality assumption, ∂ _H_ / ∂ _LL_ = _H L_ = 0 − _H L_ > 0 . This is negative for rural Vietnam in all years (Table 1) but (again) the MD is an exception; rising landlessness holding within-group distribution constant will be poverty-increasing in the MD.

We can use a difference-in-difference model to relax the within-group neutrality assumption in the above test. While the absence of household panel data spanning the period precludes use of a household fixed-effects specification, we can construct a geographic panel (within the limits of the sample sizes). In particular, we regress the log headcount index for each of the two groups (those with land and those without) for region _i_ at date _t_ , on the landlessness rate allowing for regional and time effects: where π _i_ is a regional effect,<sup>δ</sup> _t_<sup>is a time effect (such as due to macroeconomic or national agro-</sup> climatic conditions) and ε _it_ is a white-noise error term. We estimate a similar regression for the landless, with dependent variable: ln _H itL_ = 0 .<sup>46</sup>

> 46 We estimated this model using both the linear headcount index and its log, though the log specification gave a better fit for those with land.

31
**Table 2 Incidence of Povert** **<mark>y</mark> amon** **<mark>g</mark> the Different Po** **<mark>p</mark> ulation Sub** **<mark>g</mark> rou** **<mark>p</mark> s**

|Population Subgroups|Population|Per-Capita<br>Consumption<br>(Balboas)|Skilled<br>Population<br>(%)|Extreme<br>Poverty<br>(%)|Moderate<br>Poverty<br>(%)|
|---|---|---|---|---|---|
|**1997**||||||
|Non-Agricultural Formal|970,524|2,551|32|3.8|17.7|
|Non-Agricultural Informal|1,095,408|1,860|22|10.5|29.8|
|Agricultural|461,532|859|4.9|40.1|70.6|
|Indigenous|205,675|330|2.3|86.3|95.4|
|Total|2,733,139|1821|21.2|18.8|37.3|
|**2003**||||||
|Non-Agricultural Formal|985,429|2631|35.3|3.7|17.7|
|Non-Agricultural Informal|1,310,731|1904|25|6.7|28.7|
|Agricultural|530,514|961|8|32.1|65.1|
|Indigenous|236,800|310|5.7|90|98.4|
|Total|3,063,474|1851|23.9|16.6|36.8|

* Notes: The figures are computed using the ECV ( _Encuesta de Condiciones de Vida_ ) databases for years 1997 and 2003. The unit of analysis is the household and the welfare measure is consumption per capita. The extreme and moderate poverty lines are equal to 533 Balboas and 953 Balboas, respectively, which correspond to the official poverty lines used by the government of Panama. Informality is defined as the employers and employees in firms with less than 6 workers that do not contribute to the social security system, non-professional self-employed, and household workers. A worker is classified as skilled when he/she completed at least one year of secondary school.

Table 2 shows that the fastest-growing population group in Panama has been nonagricultural informal workers (an increase of 20 percent), while the number of people earning their primary income from formal activities hardly changed. Since wages in informal activities are significantly below formal earnings (Table 1), it is perhaps not surprising that, as the proportion of non-farm informal population rose, the incidence of moderate poverty among non-agricultural workers increased from 15.3 percent to 20 percent during this period. Hence the period was characterized by what Ravallion, Chen and Sangraula (2007) call the _urbanization of poverty_ , with internal migration resulting in reductions in rural poverty, an increase in urban poverty, and little to no overall poverty effects.

# **_2.2. The Canal expansion_**

The expansion of the Canal appears to be a large ̳shock‘ for Panama, with the total cost of the investment project estimated to reach 5,250 million balboas (approximately 40 percent of GDP in 2003).<sup>3</sup> However, the year-by-year impact of the surge in investment is likely to be much smaller because the construction activities will take place over a 7 year horizon (Figure 1). Furthermore, Panama‘s real GDP has been growing at an average annual rate of 8.0 percent per year between 2003 and 2010, and growth is expected to decelerate only slightly in the short- and medium-term. Taking into account these growth

> 3 This estimate is provided by the Panama Canal Authority in its document on the expansion of the Canal. The full document can be found here: http://www.pancanal.com/esp/plan/temas/plan-maestro/ .

4
younger workers. There are three main observations on labor market transition dynamics based on Appendix Table 1:

- _Young people have high persistent unemployment spells._ In Bulgaria more than 57 percent (63 percent) of males (females) less than 30 years of age remained unemployment between 1991 and 1992.  In Poland, two-thirds of the young unemployed in 2000 were found to remain unemployed 4 years after.

- _The youth who are employed are more likely than adults to leave employment and become either unemployed or out of the labor force_ .  In Poland, those aged 15 to 24 are twice more likely to become unemployed, while in Turkey, employed youth are more than 50 percent more likely to become unemployed relative to their adult counterparts.

- _Finally, the youth, particularly young women, are more likely to become discouraged and leave the labor force completely_ .  In Turkey, more that 90 percent of young women were like to leave the workforce between 2000 and 2001.

# **IV. Empirical Findings**

# **A.** **_Early transitions_**

In Bosnia and Herzegovina, despite the end of the civil conflict in the late 1990s, the youth have had significant difficulties entering the labor market and have experienced excessive instability in their early years of the transition to work. In 2004, the unemployment rate was 62 percent for those between 15-19 years old, and 37 percent for those between 20-24 years old, compared to 22 percent for adults in the same year.

<!-- Start of picture text --> Figure 1. Unemployment by Age Group: 2001-04 Figure 2. Unemployment By Gender and By Age Group: 2001-04<br>2001 2002 2004<br>2001 2002 2004<br>50<br>45 50<br>40 40<br>35<br>30<br>30<br>25 20<br>20<br>10<br>15<br>10 0<br>5 Men Women Men Women Men Women Men Women<br>0<br>Age 15-24 Age 25-44 Age 45-54 Age 55+<br>Age 15-24 Age 25-44 Age 45-54 Age 55+<br>Source: LSMS and authors' calculations.<br>Source: LSMS and authors' calculations.<br><!-- End of picture text --> Combining the 15-19 and 20-24 age groups together, the youth unemployment rate is higher than any other age group (Figure 1). On average, it is about twice the aggregate unemployment rate, although the disparity has narrowed somewhat over time.

9
3c) anocratic neighborhoods are low income phenomena. Therefore, no discernable impact across income levels should be obvious.

<u>Hypothesis 4:</u> Hypothesis three relates to the ability of neighboring attributes to influence the risk of recurrence of conflict within a state. Because the risk of recurrence is strongly related to political factors within a state, the influence of interactions between political entities and their surrounding states may be heightened. Interactions across income levels and political institutions should be particularly important as they note the propensity of outside interference and domestic stability.

4a) All types of regimes within democratic neighborhoods should have a lower conflict risk, relative to other regime neighborhoods. Democratic states in democratic regions should have the lowest recurrence risk. Although lower income states should have an increased risk compared to higher income states.

4b) All regimes in autocratic neighborhoods should experience risks higher than those in democratic neighborhoods. This effect should be exacerbated by very low income levels. Autocratic or anocractic regimes in these neighborhoods should experience the highest risks.

4c) All regimes in an anocratic neighborhoods should experience their highest average risk of recurrence overall all other neighborhoods. This should be highly conditional on income, leading to the highest risks in the lowest income states, and a null effect in higher income states.

Across all hypotheses, I expect the strength of wars and regime effects to differ across countries. Nominally lower risk states may experience a marked decrease in the negative influence from neighbors. It is possible that such lower risk states may experience a positive ‘dividend’ from neighboring democracy and that the interaction between higher income democracies may create another type of democratic peace (in this case, civil peace).

# **3.0 Data and Research Design**

Data for this project are based on both country-year data from Fearon and Laitin’s Ethnicity, 1 Insurgency and Civil War Project (2003) and the PRIO/Uppsala Armed Conflict Data (P-UACD). The data are from 1946 to 2001 in country year format. In total, 161 countries with populations over 100,000 are included for 6820 country year observations.

The hypotheses call for two dependent variables. The first is civil war onset. Fearon and Laitin (2003) use a 1000 death threshold in their study while P-UACD has a 25 death threshold for

> 1 Paper and data available at http://www.stanford.edu/group/ethnic/

10
- _Weaker prospects for commodity demand._ To transform recent resource discoveries in LICs into strong, sustained economic growth will require continued robust commodity demand growth, as well as strong governance and institutions to manage the associated revenue windfalls (Addison and Roe 2018).<sup>13</sup> However, long-term prospects for commodity demand are weakening as growth in China—the largest source of commodity demand—slows and shifts towards less resource-intensive sectors (World Bank 2018a).

- _Debt vulnerabilities on the rise._ While government debt ratios in most of today’s LICs are significantly lower than in 2001—helped largely by debt relief initiatives—their general rise since 2013 has contributed to increased vulnerabilities (World Bank 2019a). The interest burden brought about by greater indebtedness could constrain poverty-reducing expenditures, particularly on health and education.

**Figure 7. Challenges LICs face in reducing poverty**

<!-- Start of picture text --> A. Projections of global extreme poverty B. LIC growth<br>Percent Historical growth Percent LICs Poverty-weighted LICs<br>2 x historical growth 7<br>15<br>8 percent growth<br>12 3 percent target<br>6<br>9<br>6<br>5<br>3<br>0<br>4<br>2012 2015 2018 2021 2024 2027 2030 2000-07 2010-18 2019-21<br><!-- End of picture text --> Source: World Bank, World Bank (2018b).

A. Data based on global real per capita growth. 8 percent growth assumes average annual growth in per capita incomes of 6 percent for all countries, but that incomes of the bottom 40 percent of the distribution grow at 8 percent, while those in the top 60 percent grow at 4.7 percent. B. Bars represent GDP-weighted aggregates. Diamonds represent GDP aggregates weighted according to each country’s share in total poverty.

# **5. Conclusion**

Growth in low-income countries has benefited from a confluence of favorable cyclical and structural developments since 2001 that have allowed the number of LICs to more than halve over this period, as many countries achieved MIC per capita income levels. These factors have, at various points, included a commodity price boom, cyclical rebounds from the collapse of centrally planned regimes in the early-1990s, debt relief, fewer armed conflicts in Africa, trade integration, and improved business climates and policy frameworks. However, the cyclical factors that contributed to LICs reaching MIC status were either unique events (transition rebounds) or are 13 Mozambique’s deep-water gas fields are estimated to hold more oil-equivalent reserves than are held by Angola or Nigeria—Sub-Saharan Africa’s two largest oil producers—and production is set to start by 2022/23 (World Bank 2015a). Oil reserves in Uganda are estimated to be the fourth-largest in Sub-Saharan Africa, and production could start within the next three years (Alkadiri et al. 2014). Large off-shore gas fields were recently discovered in Tanzania.

14
<!-- Start of picture text --> (% ofnew jobs infarm vs. nonfarm)<br>0% —_——_ii—_<br>200%<br>100%<br>0%<br>63 to 83 53 to 8S 39 to 04 03<br>-100%<br>-200%<br>-300%<br>sss<br>| paA?_?_?_?_?_<br>gFarn gNonfarm<br><!-- End of picture text -->
<!-- Start of picture text --> —Baseline scaling-up scenario<br>—Scenario with only capital inputs<br>Scenario with only labor inputs<br>5% —Scenario with only intermediary inputs<br>-- Scenario with all domestic inputs<br>5%0% He ~* —————er<br>-10% ss,“Nene ee cnet renee ee eee_.<br>-15%<br>-20%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> —Baseline scaling up scenario<br>—Scenario with only capital inputs<br>—Scenario with only labor inputs<br>—Scenario with only intermediary inputs<br>5% )~~Scenario with all domestic inputs<br>70%5% es= ceS — ————___———<br>710%=15% SS,alata<br>~20%<br>-25%<br>2012 2015 «2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 2% —Scenario with all domestic<br>0% inputsPp<br>—Sum of domestic<br>-2%4% intermediary,capitalital iinputs labor and<br>6%<br>-B%<br>~10%<br>-12%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 2% —Scenario with all domestic<br>0% input:<br>-2% —Sum of domestic<br>-4%6% playaCapital inputs labor and<br>8%Oy<br>-10%<br>12%<br>-14%<br>-16%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
# EXECUTIVE SUMMARY

In this paper, we use factory-level data provided by China's National Environmental Protection Agency (NEPA) and the Tianjin Environmental Protection Bureau to estimate water pollution abatement costs for Chinese industry. We utilize joint abatement cost functions which relate total costs to treatment volume and the simultaneous effect of reductions in Suspended Solids, Chemical Oxygen Demand, Biological Oxygen Demand and other pollutants. Tests of alternative functional forms suggest that a very sirnple (constant elasticity) model fits the data as well as a complex (translog) model, permitting sophisticated policy experiments vith relatively simple calculations.

Using our econometric results, we analyze the cost-effectiveness of current pollution control policy in China. Our basic conclusions are as follows:

- The benefits of stricter discharge standards should be weighed carefully against the costs. For our samnple of 260 factories, a shift across the existing range of standards entails a present-value difference of $330 million in abatement costs.

- Emissions charges as low as $1.00/ton would be sufficient to induce 80% abatement of suspended solids for cost-minimizing factories. Charges of $3, $15 and $30 per ton would be sufficient to induce 90% abatement of TSS, COD and BOD.

- The current regulatory system provides an economic incentive to abate by charging a levy on pollution in excess of the standard. However, our results suggest that changing to a full emissions charge system would greatly reduce overall abatement costs. For the 260 factories in our sample, the current overall abatement rate could be attained under a charge system at a reduced annual cost whose present value is $344 million. At a cost equivalent to that of the current system, uniform pollution charges could produce much higher environmental quality.
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28

# Data and Results

In this section, data from Cote d'Ivoire are used to examine the links between adjustment and child health suggested in the conceptual framework. Although the Ivorian economy and the structures of its government expenditures share many similarities with other Sub-Saharan African countries, adjustment policy packages vary from one country to another. Therefore, we abstain from generalization based on the Ivorian data.

EconQmic changes in Cote d'Ivoire: A Summary Cote d'Ivoire experienced important economic changes during the past two decades. For the purposes of this paper, it is useful to distinguish two periods: (i) the expansionary period of the 1970s, and (ii) the economic crisis of the 1980s.

During the 1970s, the Ivorian economy grew tremendously as a result of a dynamic agricultural sector and favorable international markets and rapidly growing manufacturing and service sectors (den Tuinder 1978; Zartman and Delgado 1984). Between 1975 and 1977, increasing international prices of cocoa and coffee, the country's major export crops, gave the economy a strong boost. But the confidence in the economy's future performance that the cocoa and coffee boom generated led to an explosion in government spending and increased borrowing in international financial markets at relatively hard terms.

After the cocoa and coffee boom, public revenues collapsed, but adjustment of government expenditures lagged somewhat. This led to major fiscal imbalances,