rafmacalaba/data-use-mentions-extended huggingface dataset:
percent of GDP (while the median was about 71 percent of GDP). Note that the weighted average of public debt across LMCs in the region by 2018 is 53 percent. Excluding Nigeria, the public debt levels of LMCs totaled 83 percent of GDP in 2018, an amount that is 36 percentage points of GDP higher than its level in 2013 ( **Figure 3** ). The pace of public debt accumulation for low-income countries (LICs) and upper-middle-income countries (UMCs) was slower: the (weighted) average public debt of LICs increased by 15 percentage points to 50 percent of GDP in 2018 while that of UMCs increased by 12 percentage points to 54 percent over the same time period ( **Figure 3** ).

**Figure 3. Public Debt of SSA, by income level** **_(% GDP)_**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Low-Income Lower-Middle Income Upper-Middle Income<br>2007 2013 2018<br><!-- End of picture text --> _Source: The figure reports GDP-weighted averages by region. LIC=Low income countries, LMIC=Lower-middle income countries, UMIC=Upper-middle income countries. IMF – World Economic Outlook Database, October 2018_ The increase in public debt levels affected both resource poor and resource rich countries in the region but that rise in public debt was the most pronounced in oil abundant countries ( **Figure 4** ). From 2013 until the end of 2018, the median public debt of resource rich countries increased by 20 percentage points (to 47 percent of GDP), as the commodity price boom came to an end and fiscal positions deteriorated. The debt dynamics were even worse for oil exporters, where median debt-to-GDP grew from 31 to 54 percent of GDP over the span of five years. The debt levels of
most informal workers reside in the east and the periphery of the city.

Third, I exploit the construction of a new subway line that connected remote locations with the center of Mexico City to provide causal evidence that transit infrastructure leads to a decrease in informality rates. Specifically, I estimate a series of difference-in-differences specifications that use variation in access to new transit. These specifications control for initial characteristics of census tracts and capture changes in informality after the transit shock in locations close to the new subway line. The key identification assumption is that the opening dates of these new commuting links were unrelated to other local demand- or supply-side shocks that affected locations near the new line. This assumption is supported by the decades-long planning horizon in which part of the line was included, and several unexpected and multi-year delays in the opening schedule. I further corroborate this assumption by documenting no apparent pre-trends among the most affected locations in the preceding periods.

The main finding is that informality rates decrease in locations close to the new subway stations. I find that the ratio of formal to informal residents increases by approximately 6%-7% in locations close to the new stations after the shock. Similarly, Workers’ informality rates decrease by 2 to 4 percentage points after the construction of the new line, and firms’ informality rates decrease by 1 to 3 percentage points. These estimates represent a 6.2% decrease in workers’ informality rates, using the average informality rate in the baseline year as a benchmark. I also construct bounds of the effect to control for the sorting of workers using retrospective questions. In particular, even under the extreme assumption that all workers who moved from the central areas to the treated locations were formal in the pre-period, the effect is still 4.5%.

To check the robustness of the difference-in-differences specification, I compare the new line with similar planned metro lines that were not completed over this period for unrelated reasons, using an expansion plan from 1980. Reassuringly, this robustness check yields similar estimates to
CUSFTA as a case of bilateral trade liberalization process, Bernard et al. (2010) test their model based on firm-product level data for the US using a difference-in-difference framework. Their findings show that firms concentrate their production in their core competencies (their best selling products) after trade liberalization. They find that firms that experienced larger Canadian tariff cuts (above the median) reduce the number of products they produced for the domestic market relative to firms experiencing below median Canadian tariffs reductions.

The nature of a unilateral trade liberalization episode is different than the bilateral trade liberalization. Under a bilateral trade agreement like the CUSFTA, both countries that undertake the reform will experience an increase in market access. Thereby, a bilateral trade reform between symmetric countries, affects mainly the market potential of exporting firms belonging to the FTA. Differently, the unilateral trade reform, by affecting all countries in the world, gives rise to two opposite mechanisms.

The first mechanism is related to market access expansion. The export patterns of foreign firms selling their goods to the liberalizing market are affected by the increase in the foreign demand. The second mechanism is related to the intensification of foreign competition in the liberalizing market. This paper aims at quantifying these two effects associated to a unilateral liberalization episode. More specifically, we propose to evaluate how Chinese unilateral trade liberalization affected the pattern of French exporters. Chinese import tariff reductions, by boosting import demand in China, should induce French firms to export more products toward the Chinese market. Nevertheless, since Chinese import tariff reductions were addressed to all countries, also firms located in other countries should have taken advantage from the new export opportunities in the Chinese market. This process enhances competitive pressures in the Chinese market and thus curbs exports by French firms.

In the econometric analysis, we disentangle these two channels by using different measures of foreign competition at the firm level. In the next section, we present some descriptive evidence on how Chinese tariff cuts might shape French firms’ export patterns.

6
**|0.169|0.579***|0.545***|
|||(0.167)|(0.291)|(0.181)|(0.204)|
|Observations|658|5,834|6,405|8,021|6,865|
|Number of countries||211|214|189|195|

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The dependent variable in column (1) is the dummy “fragile peace”, which take a value of 1 if a year is peaceful, within five years since the last year of conflict, and revert back to conflict within the next 5 years. Data used in the first stage estimation (column (1)) is gathered from several sources: “share of the population that is excluded from political power” and “share of the population that is discriminated” variables are provided by GROWup dataset; information about “refugees” is provided by UNHCR dataset. In column (2)-(5) the dependent variable is “positive net investment flows”. Data is gathered from four different data sources: OECD data in column (2); Dutch investment data in column (3); UNCTAD in column (4) and World Bank in column (5). Fitted values of “fragile peace” is coming from a regression of “fragile peace” on “share of the population excluded from political power”, “share of the population that is discriminated” and “refugees per 1000 population”. “Peace” is a dummy that indicates when a country is not in conflict, i.e. violence intensity is above a threshold of 0.008 battle-related deaths per 1000 inhabitants. “First five years of recovery” is a dummy that indicates if the
_Deposit Insurance Fund, Act No 156, 1994_ ) The Deposit Insurance Fund of the Czech Republic was established in December 1994. It is government legislated and privately administered. The insurance is granted for savings and checking accounts as well as certificates of deposit and foreign currency deposits; whereas, promissory notes, inter-bank deposits and other securities are not covered. The scheme covers 90% of all insured deposits up to the equivalent of EUR 25,000 per depositor per institution. Membership to the Fund is compulsory and the premium rates are 0.1% of all insured deposits including accrued interest for banks; whereas 0.05% for building savings banks. With regards to the government participation in funding, a law (no 156/1994) mandates that the state will provide 50% of the funds needed for compensation of depositors by the DIF. The central bank and the government would equally make loans to cover any shortfall in funding.

_Sources_ : Garcia (1999), IADI Survey: Czech Republic (2003), Institute of International Bankers (1997), Kyei (1995).

**Denmark.** ( _Deposit Guarantee Fund, Act 850, 1987; Order 118, 1988_ ) The Guarantee Fund of Denmark was established in 1987. The system is government legislated and privately administered. The fund can borrow from banks with a possible guarantee from the government. The maximum coverage limit of DKK 250,000 was raised to DKK 300,000 (about EUR 40,000) effective September 1995. The fund covers registered deposits net of loans and other liabilities of the depositor vis-à-vis the bank per depositor per institution and membership to the fund is mandatory. Certain accounts established according to law such as pension accounts, children’s saving accounts and attorneys’ client accounts are covered in full. _Sources_ : Own survey of deposit insurers, IADI Survey: Denmark (2002), Institute of International Bankers (1996).

**Dominican Republic.** ( _Savings Account Insurance, National Housing Bank Law_ )
mental health conditions. The Trauma Symptoms Checklist for Young Children (TSCYC) is a comprehensive 90-item questionnaire administered to caregivers of children aged 3 to 12 years. It is specifically crafted to assess both acute and chronic posttraumatic symptoms in this age group. In our study, the TSCYC is employed with children aged 5 to 10 years. Each query within the checklist offers four potential response options, with scoring determined accordingly. Scores between 65 and 70 are indicative of potential concerns, while scores surpassing 70 suggest clinically significant elevations. The Strengths and Difficulties Questionnaire (SDQ) is a comprehensive 25-item behavioral screening tool administered to caregivers. It is tailored to evaluate emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship problems, and prosocial behaviors among minors aged 2 to 17 years (in our sample, 5 to 17 years). Following the characteristics of the regional population, the SDQ has delineated cutoff points for interpreting scores as ”normal” (0-13), ”borderline” (14-16), and ”abnormal” (17-40). The General Anxiety Disorder Scale (GAD-7) is a brief 7-item assessment tool utilized for screening generalized anxiety disorder. Scores on this scale, spanning from 0 to 4, signify minimal levels of anxiety, whereas scores ranging between 5 and 9 indicate mild anxiety. The Patient Health Questionnaire (PHQ-9) is a self-report instrument comprising nine items, administered to adolescents aged 11 to 17 years. Its primary purpose is to gauge the severity of depression symptoms and assess treatment response. Control variables include children’s sex and age, the maximum level of education completed by the most educated parent and grandparent, and the wealth index is the pre-migration wealth index for the Venezuelan sample and the current wealth index for the Colombian sample. Standard errors are clustered at the household level *** * p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

39
100% 150% 200%<br>MSCI-EM<br>2.00<br>Gold<br>1.97<br>Commod.<br>1.63<br>GBI-EM<br>1.28<br>EMBI<br>0.93<br>MSCI-AW<br>0.84<br>US HY<br>0.81<br>S&P500 0.74 Avg. annualized<br>ELMI+ monthly return<br>0.58<br>GBI-DM Annualized std.<br>0.54 deviation<br>US HG<br>0.36<br>Sharpe ratio<br>UST<br>Source: Calculations based on data from JP Morgan<br>Figure 6 : Correlation of annualized monthly returns,<br>Feb 2002-12<br>GBI-<br>EM<br>GBI-EM 1.00 GBI-DM<br>GBI-DM 0.50 1.00 EMBI<br>EMBI 0.65 0.41 1.00 UST<br>UST -0.01 0.61 0.19 1.00 US HG<br>US HG 0.40 0.61 0.66 0.58 1.00 US HY<br>US HY 0.52 0.09 0.63 -0.28 0.45 1.00 ELMI+<br>ELMI+ 0.91 0.49 0.61 -0.08 0.31 0.52 1.00 MSCI-AW<br>MSCI-AW 0.74 0.24 0.67 -0.23 0.30 0.64 0.75 1.00 MSCI-EM<br>MSCI-EM 0.69 0.16 0.55 -0.21 0.22 0.56 0.71 0.87 1.00 S&P500<br>S&P500 0.68 0.17 0.64 -
# **Uncertainty in Ex-Ante Poverty and Income Distribution**

_By_ Fabian Mendez Ramos<sup>1</sup>

**JEL Classification:** E170, I3, O4, O11, Q0

**Keywords:** Poverty, economic growth, shared prosperity, income inequality, natural resources, uncertainty.

> 1 Fabian Mendez Ramos–The World Bank, Development Research Group (DECRG), email: fmendezramos@worldbank.org. The views expressed here are the authors’ and do not reflect those of the World Bank, its Executive Directors, or the countries they represent. I am grateful to Aart Kraay and Espen Beer Prydz for sharing preliminary data and Stata codes and for answering numerous questions, and to Norman V. Loayza for guidance, multiple discussions and feedback. I am also grateful to the Global Monitoring Report 2015/2016 team for useful inputs at the beginning of this research, with especial thanks to Philip Schellekens, Ayhan Kose, Jos Verbeek, Eugenia Suarez Moran, Vandana Chandra, Hiroko Maeda and Christian Eigen-Zucchi. I am also thankful to Ha Minh Nguyen, Dorina Georgieva, Cesar Cancho, Emmanuel Chavez, Elke D. Groh, Ramon Padilla Perez, Jesus Antonio Lopez Cabrera, participants of the Seminar in the Economic Commission for Latin America and the Caribbean in Mexico City, and particiants of the World Bank DECRG Seminar Series in Kuala Lumpur, Malaysia, for helpful comments and suggestions at different stages of the paper.

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in the informal sector.<sup>2</sup> Unemployment rates are particularly high among youth (aged 15-29) holding a tertiary degree (the majority of whom are female<sup>3</sup> ), reaching 62 percent in 2012

> 1 Sections 2 and 3 build on Premand et al. (2016) and are provided to ensure the paper is self-standing.

> 2 Angel-Urdinola et al. (2015) report that 64 percent of employed individuals in Tunisia are either informal wage earners or self-employed. Data from OECD (2015) show that a large proportion of youth is employed in informal jobs: share of youth who work: i) without a contract (24%); ii) in an unregistered firm (40%); iii) in a micro-business (five or fewer employees) (56%); and iv) without contributing to social security (53%). 3 Out of the pool of those unemployed, aged 15-29 and with a tertiary degree, 68 percent are female and 32 percent are male.

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5

# **3 Poverty and Growth Diagnostics**

# **3.1 Decomposition Analysis of Growth and Inequality**

A useful way to take a first look at the impact of growth on poverty is by decomposing the change in the headcount rate. In general, a change in poverty can be attributed to the interaction of two processes -- growth in mean consumption and a change in consumption inequality. The formal decomposition proposed by Datt and Ravallion (1992) attributes changes in poverty to a growth effect, an inequality effect, and an interaction effect (the residual).

Results from this decomposition applied to the Tanzania data are shown in Table 3. The decomposition shows that overall growth reduced poverty in the country as whole by 4.6 percentage points. The slight increase in inequality, however, increased poverty by 1.1 percentage points. The same general pattern holds for rural areas and urban areas other than Dar es Salaam.

The results are more notable for Dar, where the substantial decline in poverty was attributable to the city’s economic growth. Holding inequality constant, growth reduced poverty by 16.3 percentage points. The growth-induced decline in poverty was partially countered by increasing inequality, which drove up poverty by 9.8 percentage points.

**Table 3: Growth and Inequality Poverty Decomposition**

||**Mainland**<br>**Tanzania**|**Dar es**<br>**Salaam**|**Other**<br>**urban**<br>**areas**|**Rural**<br>**areas**|
|---|---|---|---|---|
|**Poverty Rate in 1991/92**|38.6|28.1|28.7|40.8|
|**Poverty Rate in 2000/01**|35.
**Figure 1B: Quantile Regression Estimates of the Differences between Schools More or Less Affected by the Pandemic according to their Principals, All ECA+ Countries**

<!-- Start of picture text --> 0<br>-5<br>-10<br>-15 0-2 weeks<br>2-8 weeks<br>more than 8 weeks<br>-20<br>-25<br>-30<br>1 2 3 4 5 6 7 8 9<br>Reading achievement decile<br>Departure from achievement trend<br><!-- End of picture text --> Figures 2A and 2B provide comparisons depending on the number of weeks of full and partial school closures according to UNESCO country-level data. In EU countries with around 10 weeks of full or partial closures, the best students have the achievement as expected – similar to the predictions before the pandemic. The weakest students in EU countries with around 10 weeks of closures lost around 10 points, which is considerable but also not a large loss. However, in EU countries with 50 weeks of full or partial closures, the best students lost around 20 points, while those at the 1<sup>st</sup> decile lost nearly 30 points. Overall, according to the estimates based on UNESCO data, the lowest-achieving students in EU countries lost more, but the differences to the best students are similar for countries with shorter and longer school closures.

Figure 2B show similar comparisons for all ECA+ countries. Overall, taking students in all ECA+ countries, the losses are larger. However, similarly to EU countries, the lowest achieving students lost more during the pandemic. The main difference is that in countries with the longest school closures, also students at the highest achievement levels tend to show large learning losses similar to those with lower achievement levels.

11
and trade flows. Our approach here allows us to isolate Cambodia’s agreements specifically and estimate, through the “specific” component, the relationship between Cambodia’s trade agreements and trade flows, and then compare them to the “average” effect across all other agreements.

We estimate each gravity equation using the Poisson Pseudo Maximum Likelihood with High-Dimensional Fixed Effects (PPML HDFE) methodology (Correa, Guimarães, and Zylkin, 2020) that produces results robust to statistical separation and convergence issues and are corrected for potential biases arising from country-specific time trends and other parameters included in the model. More specifically, we measure trade at the country-pair level, so that the overall effect of GDP growth on trade is absorbed by the country-specific time trend variables included in the estimation. We also include country-pair fixed effects to—at least partially, if not mostly—control for cultural ties and political relationships.

# **_Data_**

Since the country-pair fixed effects and both importer and exporter time trends absorb the variation of the “usual” gravity variables (distance, gross domestic product, and other country-pair characteristics like common language, common border, and so on), our data consist of bilateral trade flows and trade agreement indicators from the Centre for Prospective Studies and International Information (CEPII) database (Abreha and Robertson 2023). The dataset includes over 1 million observations that cover 232 exporters, 179 importers, 262 Regional Trade Agreements (RTAs) over the 1990–2016 period.

# **_Results_**

Table 3.2 contains the gravity results. Again, the data cover all pair-wise trade flows for each year from 1996 to 2015. The impact compares the change in trade following the implementation of the agreement relative to countries without agreements (assuming that the trade agreements constitute an absorbing state in the sense that do not end before end of the sample period). Each row represents a specific trade agreement. The β<sup>k</sup> coefficients represent the estimated relationship between the agreement listed in
to simplify the comparison with services. For instance, the manufacturing sample survey in the year 2005-06 is referred to as the 2006 survey. Likewise, we use 2010 in the tables and text to refer to the years 2009-10 for ASI data and years 2010-11 for NSSO data. For services, we always refer the surveys by their start year.

# **_Sample Preparation_**

Our descriptive work primarily compares manufacturing and services during the overlapping period of 2000-2010. The raw data have 155,513 ASI observations and 408,002 NSSO observations. For the ASI, we drop establishments that are not “open,” excluding plants that were closed or non-operational when surveyed, were deleted due to deregistration, and were out of coverage (that is, belonging to defense, oil storage, etc.). Next, we drop ASI establishments that do not belong to the manufacturing sector. Such industries may relate to mining and quarrying, fishing and aquaculture, or the services sector. Finally, we flag observations with null, missing, negative, or extremely large values of output, output per worker, and employment. At this stage, those states that are not surveyed by ASI are also flagged from the NSSO surveys. These include the states of Arunachal Pradesh, Mizoram, Sikkim, and the Union Territory of Lakshadweep. We also flag observations that have blank state codes. In the end, we drop observations that have any one of the flags described above. These drops result in a final sample of about 93,000 for the organized sector and 400,000 for the unorganized sector for the three survey years under consideration.

The NSSO surveys many more establishments in the services sector, about 800,000 during our period of study. We drop some very small industries: NIC 61 (Inland, sea and coastal water transport), 73 (Research on natural sciences, engineering, social sciences and humanities), and 90 (Sanitation services). We also drop NIC industries 37 (Recycling) and 50–
is acquired from the ERA-5 reanalysis dataset (Hersbach 2018). We derive estimates for the maximum and minimum temperatures at the daily level by taking the maximum and minimum of the hourly values for each day.

- **Future temperature projections from climate model simulation data:** Maximum and minimum hourly temperatures over the previous 24 hours are acquired from Earth system model simulations from the Coupled Model Intercomparison Project, phase 6 (CMIP6). We consider three shared socioeconomic pathways, namely SSP1 (Sustainability, taking the green road), SSP2 (middle of the road), and SSP5 (fossil-fueled development). SSP2 currently appears to be the most likely scenario from an emissions perspective (Pielke Jr et al. 2022). And while SSP1 and SSP5 currently appear to be unlikely, understanding climate projections under these relatively extreme scenarios provides us with a view of the full range of possibilities. Moving forward, we refer to these scenarios as “low emissions” (SSP1), “middle of the road emissions” (SSP2), and “high emissions” (SSP5). We consider projections from the following global climate models: AWI-CM-1-1-MR, EC-Earth3-VegLR, FGOALS-g3, GFDL-ESM4, INM-CM4-8, INM-CM5-0, IPSL-CM6A-LR, KACE-10-G, MIROC6, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, and NESM3 since each of these models has less than 2% missing daily values, over the full simulation period of 1979 to 2100, for each of the three socioeconomic scenarios considered here.

- **Economic data:** Gross domestic product (GDP) estimates, based on purchasing power parity, at a resolution of 30 seconds in both latitude and longitude (i.e., a spatial resolution of just under 1 km in the region of Southeast Europe) from Kummu et al. (2018). We use estimates from the year 2015 since these are the most recent available estimates.

# **Quantile Delta Mapping Post-Processing for CMIP6 Simulations**

We post-process the Earth system model simulations using a quantile mapping method, which corrects systematic distributional