rafmacalaba/datause-encoder-data huggingface dataset:
- Aimee Chin, N. Meltem Daysal, and Scott A. Imberman. Impact of bilingual education programs on limited English proficient students and their peers: Regression discontinuity evidence from Texas. _Journal of Public Economics_ , 107(C):63–78, 2013. doi:10.1016/j.jpubeco.2013.08. URL https://ideas.repec.org/a/eee/pubeco/v107y2013icp63-78.html.

- Kalena E. Cortes. The effects of age at arrival and enclave schools on the academic performance of immigrant children. _Economics of Education Review_ , 25(2):121–132, April 2006. URL https://ideas.repec.org/a/eee/ecoedu/v25y2006i2p121-132.html.

- Timothy M. Diette and Ruth Uwaifo Oyelere. Gender and racial differences in peer effects of limited English students: a story of language or ethnicity? _IZA Journal of Migration and Development_ , 6(1):1–18, December 2017. doi:10.1186/s40176-017-0111-5. URL https:// ideas.repec.org/a/spr/izamig/v6y2017i1d10.1186_s40176-016-0074-y.html.

- Simone Dobbelsteen, Jesse Levin, and Hessel Oosterbeek. The Causal Effect of Class Size on Scholastic Achievement: Distinguishing the Pure Class Size Effect from the Effect of Changes in Class Composition. _Oxford Bulletin of Economics and Statistics_ , 64(1):17–38, February 2002. URL https://ideas.repec.org/a/bla/obuest/v64y2002i1p17-38.html.

- Esther Duflo, Pascaline Dupas, and Michael Kremer. Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya. _American Economic Review_ , 101(5):1739–1774, August 2011. URL https://ideas.repec.org/a/aea/aecrev/ v101y2011i5p1739-74.html.

- D. Epple and R. Romano. Peer effects in education: Survey of the theory and evidence. In _Hand-_

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in driving restrictions on pollution has been studied by Viard and Fu (2015), their analysis explores a complex mixture of changes in a very short period, which makes it difficult to clearly separate stringency levels.<sup>3</sup> The most closely related studies evaluating driving restrictions are Gallego, Montero, and Salas (2013) and Davis (2008). Unlike their studies, which offer a broad analysis of a drastic driving restriction, the present paper focuses on the effects of two stringency levels. Due to a lack of a measurement of car driving, as in Gallego, Montero, and Salas (2013), the present study uses carbon monoxide (CO) levels as a proxy for car use and measure of air quality, exploiting the strong correlation with traffic. Using hourly CO in a regression discontinuity model, the effects of moderate and drastic restrictions are estimated at different times of the day and week for a two-year symmetrical time window centered on the start of each PYP phase. Within this interval, prepolicy observations are used as a counterfactual of postimplementation observations, as in Davis (2008), allowing for a polynomial time trend to control for unobserved time-varying factors.<sup>4</sup> To shed light on the households’ potential behavioral response to the policy, the effects of PYP on gasoline consumption and vehicle sales and registrations are examined and compared between phases. A set of robustness checks are also conducted to assess the sensitivity 3. The ban in Beijing was introduced on July 20, 2008, and lifted on September 20. It was adjusted and reintroduced on October 11 and again readjusted on April 11, 2009. During the development of the present research, a study by Lin, Zhang, and Umanskaya (2014) assessing driving restrictions in several cities, including Bogotá, became available. Their analysis focuses on the effectiveness of a mixture of driving restrictions (private and public transport) on several pollutants, but does not evaluate the effects of moderate and drastic bans on car sales and gasoline use. The authors argue that restrictions for other transport modes may have increased the cost of using them and did not result in a clearly improved air quality.

4. The difference-in-difference method would require the assumption of a similar CO trend in a comparable city that was not subject to the program before the policy was decreed. Because other Colombian cities differ in vehicle fleet composition, development, geography, and meteorological conditions and also have been affected by other driving restriction programs, that assumption is unlikely to be met by any other city. For instance, Colombia’s second largest city, Medellín, had extended restrictions to motorbikes prior to the drastic phase in Bogotá.

4
After restricting the sample to working age individuals (between 15 and 59 years old),<sup>4</sup> since our treatment is at the provincial level, we collapsed the data into province _×_ year cells to obtain yearly out- comes of the labor markets since our treatment is at the province level.

Considering the non-panel structure of our survey data, this aggregation does not come at the cost of losing information. Therefore, in order to identify the impact of a drought shock on labor markets, we leveraged its within-province variation across years. Exploiting the details of employment status and the economic sector codes (NACE classification) for employed individuals, we focused on three labor market outcomes at the provincial level: the share of employment in the agriculture sector; the share of employment in the other economic sectors; and the unemployment rate. Other information at the provincial level includes the share of population across five age groups; the gender of the workforce; the share of educational levels across the population; the share of informal jobs; and the share of the workforce population living in urban areas.

We aligned ENE socioeconomic data with detailed weather information that we obtained from the Agri-4-Cast database. These data are provided by the Food Security Unit of the Joint Research Center (JRC.D.5) and were specifically employed for identifying the climate change impacts in the agriculture sector. The data consists of gridded meteorological observations from weather stations interpolated on a 25 _×_ 25 km grid. They are available on a daily basis from 1979 in the European Union and its neighboring countries, including Morocco. We selected variables for maximum, minimum, and mean air temperature (in Celsius degrees); mean daily wind speed at 10m ( _m/s_ ); and sum of precipitation (mm/day) to calculate the Standardized Precipitation Evapotranspiration Index (SPEI), our treatment measure. The SPEI represents a state-of-the-science indicator for measuring the impact of increased temperatures on water demand (Vicente-Serrano et al., 2010; Chiang et al., 2021). Since climate grids come at a finer spatial resolution than our administrative unit of analysis, we calculated

> 4 Retirement age in Morocco was 60 up to 2015.

8
generated by differences the intensity of use of assets, returns to these assets and transfers, and resilience to shocks.

Inter-generational mobility, on the other hand, is fundamentally about origin-independence and is measured by the extent to which the distribution of incomes of the children is independent of the distribution of incomes of parents. A society in which parents’ rank position has no bearing on the children’s position is a society with higher intergenerational mobility. Conceptually then, this is equivalent to establishing a link between the income generating capacity of the current generation to the endowment of assets for the next generation through inheritance and bequest, investment in education and health, access to networks etc.

# Economic Mobility

We begin the analysis by presenting some results on perceptions of mobility in the three countries in the sub-region. For this we utilize the data from latest round of the Life in Transitions Survey (LiTS). LiTS is a combined household and attitudinal survey collecting a rich array of information on the socio-economic status of the underlying population as well as information on perceptions about a variety of economic, social and political issues. The third round of LiTS was implemented between the end of 2015 and early 2016 in a total of 34 countries. This included 29 transition countries, the Czech Republic, Italy, Germany, Cyprus and Greece. All three countries, Armenia, Azerbaijan and Georgia are covered in the survey and a targeted 1,500 households were interviewed in each country.

For the purposes of the study of economic mobility, the LiTS surveys carry several questions that are relevant. First, each respondent is asked about their assessment of the statement “my household lives better than it did 4 years ago”. This question directly gets at the issue of perceived directional movement in living standards relative to where the household considered itself to be at an earlier period and the responses are interesting. (Figure 2) From the results, it appears that there is quite a bit of heterogeneity among the three countries. Roughly 14-16 percent of population in each of the countries have neutral views on the how they have fared in the last few years. But perceived downward mobility is particularly pronounced in Armenia where 66 percent of the population believes their household to have done worse than where they were 4 years ago. Georgia is not too far behind on this account: 57 percent of Georgians too perceive themselves to be doing poorer than they were doing four years ago. On the upside, one in four Georgians and roughly one in five Armenians believed their households were actually better off than four years ago. While the weaker post-crisis recovery of the economy in Armenia relative to Georgia could potentially explain some of this difference, it is not entirely consonant with the overall pace of poverty reduction seen in both countries in this period. Results appear slightly more positive in Azerbaijan with more than 2 in 5 households having a favorable assessment of their household’s circumstances today relative to four years ago. But the Azerbaijan 7
<mark>Table 8: Transition Matrices - Synthetic vs. Actual Panel Data Using Different Survey Designs</mark>

|<br> <br>Status in<br> <br>Esti<br>S<br>|<br>mates Without U<br>urveying-weight<br>Truth|<br>sing<br>s<br>|<br>Estimat<br>S<br>|<br>es Using Individ<br>urveying-weight<br>Truth|<br>ual-level<br>s<br>|
|---|---|---|---|---|---|
|Year[1],<br>Year[2]<br>Lower<br>Bound|Confidence<br>Interval|Upper<br>Bound|Lower<br>Bound|Confidence<br>Interval|Upper<br>Bound|
|[1]|[2]|[3]|[4]|[5]|[6]|
|_Panel A: Chile 1996 and 2_<br>|_006_<br>|<br>|<br>|<br>|<br>|
|P,P<br>15.80|(9.35,13.72)|8.15|6.59|(4.39,7.64)|3.04|
|P,NP<br>22.84|(24.05,30.13)|29.61|11.43|(19.38,25.07)|21.00|
|NP,P<br>0.73|(3.49,6.46)|8.38|2.84|(2.17,4.65)|6.39|
|NP,NP<br>60.63|(52.98,59.76)|53.86|79.14|(65.15,71.51)|69.57|
|<br> <br>_Panel B: Nicaragua 2001_|<br>_and 2005_|<br>|<br>|<br>|<br>|
|P,P<br>42.69|(37.53,44.91)|34.65|43.03|(38.41,45.82)|34.46|
|P,NP<br>2.49|(2.01,4.71)|10.68|2.44|(1.05,3.23)|8.20|
|NP,P<br>22.95|(21.19,27.63)|30.99|26.51|(24.07,30.76)|35.08|
|NP,NP<br>31.87|(27.52,34.46)|23.68|28.02|(24.93,31.69)|22.26|
|<br> <br>_Panel C: Peru 2008 and 2_|<br>_009_|<br>|<br>|<br>|<br>|
|P,P<br>31.12|(24.12,27.65)|18.81|34.46|(26.83,30.47)|20.56|
|P,NP<br>4.65|(8.87,11.30)|16.48|4.92|(8.47,10.85)|15.64|
|NP,P<br>5.49|(9.47,11.97)|17.80|6.14|(10.6,13.25)|20.03|
|NP,NP<br>58.74|(51.28,55.30)|46.91|54.48|(47.72,51.74)|43.76|
|Obs. in Panel A<br>823|823|823|823|823|823|
|Obs. in Panel B<br>684|684|684|684|684|684|
|Obs. in Panel C<br>2,368|2,368|2,368|2,368|2,368|2,368|
|Data source: Chile CASE<br>2008-2009.<br>Note: R-squared is calcul|N, 1996-2006; N<br>ated for opposite|icaragua E<br>halves of|MNV 200<br>the total Y|1-2005; and Peru<br>ear[1] sample. R|ENAHO<br>esults are|

<mark>Note: R-squared is calculated for opposite halves of the total Year[1] sample. Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results in columns [1], [2], and [3] are not weighted. Results in columns [4], [5], and [6] are weighted using individual-level survey-sampling weights. Controls are the same as in columns [3] and [7] of tables 2a, 2b, and 2c. Results in column [2] and [5] show actual panel mobility. 95 percent confidence interval between parentheses. P, P refers to Poor, Poor; P, NP refers to Poor, Non-Poor; NP, P refers to Non-poor, Poor; and NP, NP refers to Nonpoor, Non-poor</mark> 31
**Table 2. Revealed Comparative Advantage, Value and Shares for Russia, 2005 and 2013, (US$, millions)**

|||**2005**|||**2013**||**2005–13**<br>**Annual Average**|
|---|---|---|---|---|---|---|---|
||**Value**|**Share**|**RCA**|**Value**|**Share**|**RCA**|**Growth**|
|Transportation|9,100|32%|1.34|21,000|30%|1.30|11.9|
|Other business services|5,800|20%|0.98|18,000|27%|1.13|16.8|
|Travel|5,900|21%|0.7|12,000|17%|0.89|10.8|
|Construction|3,300|12%|2.98|5,700|8%|2.34|9.8|
|Telecom|1,000|4%|0.79|4,200|6%|0.78|21.7|
|Manufacturing services|1,400|5%|0.95|2,300|3%|0.93|8.8|
|Maintenance & repair|920|3%|2.57|1,800|3%|2.27|9.7|
|Finance|390|1%|0.25|1,700|2%|0.33|24.9|
|Personal, cultural, &<br>recreational|190|1%|0.69|770|1%|1.20|20.2|
|Intellectual property|260|1%|0.25|740|1%|0.14|14.9|
|Insurance|320|1%|0.88|580|1%|0.48|9.8|

Source: IMF, Balance of Payments Statistics.

62
**Table 1. Summary statistics, 2021**

|**Variable**|**Number of**<br>**Observations**|**Weighted**<br>**mean**|**Standard**<br>**Deviation**|**Min**|**Max**|
|---|---|---|---|---|---|
|Women|81,106|50%|0.5|0|1|
|Poorest 40% of within-country household<br>income distribution|81,106|40%|0.5|0|1|
|Age (years)|81,106|37.25|16.2|15|99|
|Primary education only|81,106|51%|0.5|0|1|
|Household size|81,106|4.61|2.8|1|26|
|Unemployed|81,106|9%|0.3|0|1|
|Out-of-workforce|81,106|39%|0.5|0|1|
|Self-employed|81,106|25%|0.5|0|1|
|Employed for wage|81,106|26%|0.5|0|1|

Source: Global Findex database 2021.

Note: Summary statistics are shown for 79 low- and middle-income countries. The full list of countries is shown in Appendix table A.1. The summary statistics exclude China due to missing data on employment status.

# **4. Who are the unbanked?**

Our measure of financial inclusion is ownership of an account—that can be used to store money and send or receive payments—at a regulated financial institution, including mobile money service providers. Looking at all countries covered in the database, the Global Findex 2021 data finds that globally, 76 percent of adults surveyed had a financial account in 2021—a 50 percent increase from 2011, when only 51 percent of the global adult population had an account.<sup>3</sup> Yet, about 1.4 billion adults—or 24 percent of adults—worldwide remain without a financial account (Figure 1) or are _unbanked_ . The share of unbanked as a percent of the population varies significantly across countries (Table A.1). While there are, on average, virtually no unbanked in high income countries (4 percent on average), the share increases to 29 percent in developing countries, ranging from 2 percent in Mongolia to a staggering 94 percent in South Sudan.

> 3 The Global Findex defines a “financial account” as any account with a regulated financial institution, such as a bank, credit union, microfinance institution, or mobile money provider.

8
# **2. Fiscal deficits and public debt in the medium term**

# **2.1 Fiscal deficits**

The fiscal position of the general government and medium term fiscal projections are shown in table 1. Romania ran a pro-cyclical fiscal policy during the economic boom of the mid 2000s, with a structural fiscal deficit of 8.5 percent of GDP in 2008, at the height of the boom. The severe recession which struck the economy in 2009 – real output contracted by 7.1 percent – exposed the fragility of the fiscal stance and forced the government to begin to implement a radical fiscal consolidation. The government has made tangible progress in reducing the structural fiscal deficit, to 5.1 percent of GDP in 2010 and to a projected 2.8 percent of GDP in 2011; if the latter target is achieved there will have been a fiscal consolidation amounting to 5.7 percent of GDP in the space of 3 years, one of the largest in the European Union (figure 1).

**Table 1: Key macroeconomic and fiscal data and projections (percent of GDP): 2008-2016**

||2008|2009|2010|2011|2012|2013|2014|2015|2016|
|---|---|---|---|---|---|---|---|---|---|
|Real<br>GDP<br>growth|7.3|-7.1|-1.3|2.0|1.8|3.5|4.0|4.0|4.0|
|Revenue and<br>grants|32.2|31.4|32.8|33.0|33.6|34.1|34.3|34.4|34.6|
|Expenditure|37.0|38.7|39.4|37.3|35.5|35.5|35.6|35.3|34.9|
|Fiscal<br>balance|-4.8|-7.3|-6.5|-4.3|-1.9|-1.4|-1.3|-0.9|-0.3|
|Structural<br>balance|-8.5|-7.0|-5.1|-2.8|-0.1|0.2|0.0|0.1|0.6|
|Gross public<br>debt|13.6|23.9|31.7|34.5|35.8|34.5|32.8|30.9|28.5|

_Source: IMF, World Economic Outlook, September 2011_ 5
There is little doubt that the global financial crisis had long lasting effects on the global economy. Hence, an interesting question to ask is whether such a crisis did affect the patterns of financial globalization. In order to check whether this is the case, we allow the trend coefficients to be different before and after the global financial crisis (2008) in table 2. More precisely, we estimate the following spline model: where �� is a dummy variable, the subscript �= ��, �, �� denotes developed, emerging and frontier markets, and �� (After Crisis) is an indicator function that takes value of 0 until 2008 and 1 after. Our results are reported in table 2, which shows that once we introduce a spline and allow the trend to be different before and after the global financial crisis, on average, we find no significant differences (column 1). However, we find quite different patterns for the different country groups (column 3). In the pre-crisis period, developed markets did not exhibit a significant trend, emerging and frontier did. As a consequence of the crisis, developed market experienced a very important setback in terms of globalization, larger than that of emerging or of frontier markets.<sup>12</sup> After the crisis, developed countries experienced a fast recovery in the financial globalization process, which is reflected in the higher trend. Emerging markets also exhibit an increase in the financial globalization trend, albeit statistically insignificant. Frontier markets, instead, are experiencing a significant decrease in their trend.

Table 2 highlights the effect of controlling for heteroscedasticity (columns 1 and 3) or not (columns 2 and 4). Had we used the UFGI, we would have concluded that during the pre-crisis period developed markets integrated at a faster pace than emerging and frontier, and that after the crisis all country groups entered in what looks like a deglobalization phase. Instead, using our FGI, the picture we get is quite a different one. Before the global financial crisis, countries with less developed financial market were converging towards developed markets’ levels of financial globalization. The global financial crisis further narrowed the difference in financial globalization between country groups, but this was because of the sharper decline in developed markets. However, in the aftermath of the crisis developed markets recovered fast towards pre-crisis financial globalization levels, while frontiers entered in what looks like a deglobalization phase. This very comparison between FGI and UFGI trends is indicative of the importance of controlling for the heteroscedasticity of global volatility especially around episodes of severe financial distress.

> 12 28 percentage points _vis-à-vis_ 23 and 2, see table DA9.

6
The final issues in the use of PPPs are how to extend them over time. We compare prices across countries only once using the most recent ICP round, relying on domestic consumer price inflation for the within-country comparisons. In other words, our approach only requires one ICP round: domestic local currency units in any year are converted into domestic 2005 prices using a domestic CPI deflator, and to these (constant price) local currency units we then apply the 2005 PPP exchange rate obtained from direct price comparisons under ICP.

Conceptually, our approach is simple because it keeps the comparisons over space and over time separate. All our within-country comparisons are independent of international prices and only depend on domestic prices, which is attractive not least because domestic prices are appropriate for assessing the trade-offs at the country-level (Nuxoll, 1994).

# **1.3** **<u>National accounts and household surveys</u>**

Typically, per capita household consumption in national accounts exceeds the average consumption or income recorded in the survey (Deaton 2005).<sup>17</sup> Moreover, as Deaton shows, the discrepancy appears to have increased over time not only in India (which is somewhat of a _cause célèbre_ in that respect) but also in rich countries like the United States and Great Britain. Studies of global inequality differ in their view of how to account for this discrepancy. In our main specification, we follow the approach suggested by Anand and Segal (2008), and simply use the average income recorded in the survey.

As mentioned before, other papers (except Milanovic, 2002, 2012) have anchored the income level to the national accounts (usually, GDPs per capita), combined it with distributional information from household surveys and typically assumed lognormality. Anand and Segal (2008) argue that GDP per capita “is not a suitable measure of household income” (p. 67) and should not be used to anchor household survey means, since it includes items such as

> different consumption baskets of the poor and non-poor has very little effect on the estimated PPP exchange rates, which is also reassuring for our estimation strategy.

> 17 In some - mostly African - countries, per capita national accounts consumption is actually lower than that found in the household survey. Deaton (2005) argues that this might be explained by under-estimation in the national accounts rather than by problems in the household surveys. For an early statement of the issue, see Milanovic (2002, pp. 656).

11