rafmacalaba/datause-encoder-data huggingface dataset:
> FIGURE 3.10

<!-- Start of picture text --> Origination of new loans from family or friends, 2011 and 2014<br>Adults borrowing from family or friends in the past year (%)<br>2011 2014<br>East Asia & Pacific 28<br>Europe & Central Asia 24<br>High-income OECD economies 15<br>Latin America & Caribbean 13<br>Middle East 31 NOCHANGE<br>South Asia 31<br>Sub-Saharan Africa 42<br>Source: Global Findex database.<br><!-- End of picture text --> 31 percent (figure 3.10). As noted, the share who reported new formal loans simultaneously fell in South Asia, so the increase in borrowing from family and friends might reflect at least in part a substitution of informal for formal credit. Conversely, in Europe and Central Asia the share of adults who reported borrowing from family or friends fell between 2011 and 2014, perhaps reflecting a greater availability of credit from financial institutions. In all other regions the share of adults who reported borrowing from family or friends remained about the same over this period.

The origination of new loans from private informal lenders remained steady overall between 2011 and 2014, reported by fewer than 5 percent of adults around the world. But the Middle East and South Asia are exceptions to this general trend. The share of adults reporting a new loan from a private informal lender doubled in both regions between 2011 and 2014, reaching 8 percent in the Middle East and 11 percent in South Asia. The increase in South Asia might again reflect in part a substitution of informal for formal credit.

Overall, the gender gap in the origination of new formal loans changed little between 2011 and 2014. In high-income OECD economies in 2014, women were about 20 percent less likely than their male counterparts to report having borrowed from a financial institution in the past 12 months. This equates to a gender gap of 5 percentage points, much the same as in 2011—though an increase in the share of both women and men reporting new formal loans means a slight increase in the gender gap in relative terms. Developing regions show no significant gender gap in the origination of new formal loans for either 2011 or 2014. This may be due in part to the overall low level of formal credit in these economies.

# What are the main purposes for borrowing?

For what purposes are people most likely to borrow? One common purpose is to buy land or a home, the largest financial investment that many people make in their life. In 2014, 26 percent of adults in high-income OECD economies reported having outstanding formal housing financing from a bank or another type of financial institution. In contrast, the share was less than 10 percent in all developing regions. Even among high-income OECD economies there is much variation in the share of adults with formal housing financing from a financial institution. While half of adults in Norway reported having one, for example, less than 15 percent did in Italy, Greece, and Poland (map 3.3).

> THE GLOBAL FINDEX DATABASE 51
causing financial crises in the euro area in 2011-13. These events caused sharp asset price declines and severe credit crunches, a collapse in global trade, and highly synchronized recessions in a record number of countries around the world. However, as discussed below, with the exception of some of those in the Europe and Central Asia (ECA) region, EMDEs weathered the 2009 global recession relatively well.

# 3.3. Synchronization of National Recessions

Global recessions are highly synchronized events internationally. The fraction of countries in recession increased during the four global recessions (Figure 7). The GDP-weighted fraction of countries in recession was about 50 percent in the first three global recessions, but rose to slightly more than 80 percent in the latest episode. The unweighted fraction of countries in recession reached local peaks during the global recession years. For example, it was about 60 percent during the 2009 episode.<sup>23</sup> In all four global recessions, the fraction of countries in recession started picking up ahead of the recession year.

The number of countries in recession was often relatively low two to three years prior to each global recession. The 2006-07 period stands out for the historically low number of countries in recession. However, this was followed by a sharp reversal of fortune. In 2009, almost all advanced economies (35 of 36) and roughly half of EMDEs were in a recession. The degree of international synchronicity in the last global recession was the highest in the past 70 years, possibly reflecting the unusual depth of the global financial crisis and much stronger international trade and financial linkages than in earlier episodes.

# 4. Main Features of Global Recessions, Recoveries and Expansions

# 4.1. Global Recessions

The behavior of the main macroeconomic and financial variables displays a number of regularities during the four global recessions. The 2009 global recession, which saw by far the largest declines in many indicators of activity, otherwise followed a pattern broadly similar to the previous episodes. The impact of global recessions often varied across different groups of countries and regions.

Sharp contraction in real activity. In the four global recessions, per capita global output (market exchange rate weighted) declined on average by 1.3 percent, which is 3.5 percentage points below the average annual growth rate of 2.2 percent during the 19502019 non-recession years (Table 3). With PPP weights, the decline in per capita output during global recessions was, on average, 0.8 percent, while growth during expansion years was 2.5 percent.

> 23 Imbs (2010), using monthly industrial production data, concludes that the degree of cross-country business cycle correlation during the latest recession was the highest over the past three decades. Other research also indicates that shocks originating in credit markets have been influential in driving global activity during global recessions, including the 2009 episode (Helbling et al. 2011; Eickmeir and Ng 2015; Bacchetta and van Wincoop 2016; Perri and Quadrini 2018).

12
16

# **TAB-E 3: CHARACTERISTICS OF LABOR FORCE WITHDRAWAL AND REENTRY POPULATION 12 AND OVER 1991**

|||MEN|||WONMEN||
|---|---|---|---|---|---|---|
||All<br>.____|Age<br><20|Age<br>20 >|All|Age<br><20|Age<br>20>|
|% withdrawing from<br>the labor force:|||_|_l|||
|from employment|_.05_|.18|.03|.19|.25|.17|
|from unemployment|.24|.37|.17|.53|_.55_|_.51_|
|**% withdrawals**<br>**reentering the labor**<br>force:|||||||
|within 3 mos.|_.55_|.48|.62|.41|.39|.42|
|within 6 mos.|_.55_|.48|.63|.4.3|.41|.43|

Source: Own results from 1988 National Employment Survey, INEGI
_page 21_

# Appendix **I:** Full Panel Regression Results.

Table 1. Panel regression: In yi, = a, + 1B In TOTi, + e, . Sample period: 1973, 1981, 1989.

|Dependent variable|Terms of trade<br>Coefficient|Terms of trade T-<br>ratio|_2_||_N_<br>N|
|---|---|---|---|---|---|
|Ln Real exchange rate|-0.5617|-6.8338|0.5716|0.3973|54|
|Ln Consumption|0.3594|4.4355|0.3598|0.3917|54|
|Ln Private Consumption|0.3413|4.2327|0.3386|0.3898|54|
|Ln Govt Consumption|0.4085|4.1049|0.3250|0.4810|54|
|Ln Savings|0.0312|0.2177|0.0014|0.6939|54|
|Ln Investment|0.4780|4.3860|0.3547|0.5267|54|
|Ln Private Investment|0.4895|3.5540|0.2652|0.6656|54|
|Ln Govt Investment|0.5731|4.5936|0.3761|0.6030|54|
|Trade Balance|-15.1420|-2.8571|0.1936|24.9415|53|
|Ln GDP|-0.0123|-0.1613|0.0007|0.3689|54|
|Ln Oil value added|-0.3862|-1.7479|0.2763|0.9133|23|
|Ln Non-oil value added|0.2211|4.4454|0.7118|0.2055|23|
|Ln Agriculture|0.0706|0.7940|0.0220|0.3787|46|
|Ln Manufacturing|0.1710|1.2890|0.0767|0.5432|36|
|Ln Construction|0.3314|2.7997|0.4655|0.4852|25|
|Ln Public Utilities|0.2553|1.5724|0.2610|0.6039|21|
|Ln Services|0.0951|1.0117|0.0353|0.4003|46|
|Ln Transportation|0.1958|1.7262|0.2986|0.4323|22|
|Ln Trade|0.1419|0.8500|0.0936|0.5207|21|
|Ln Other services|0.2704|3.7993|0.6734|0.2713|22|
**When Do Gender Wage Differences Emerge? A Study of Azerbaijan’s Labor Market**

Francesco Pastore<sup>¥</sup> , Sarosh Sattar<sup>§</sup> , Nistha Sinha<sup>§</sup> and Erwin R. Tiongson<sup>§</sup> JEL classification: I21, J13, J15, J16, J24, J31, J7, P30 Keywords: Gender Wage Gap and Dynamics; Early Labor Market Outcomes; SchoolTo-Work Transitions; Earnings Equations; Decomposition Analysis.

¥ Seconda Università di Napoli and IZA (Institute for the Study of Labor) of Bonn.

§ World Bank (Poverty Global Practice, Europe and Central Asia Region).

§ World Bank (Poverty Global Practice, Europe and Central Asia Region).

§ Georgetown University and IZA (Institute for the Study of Labor) of Bonn.
Figure A2: FDI Concentration Ratio (Threshold Robustness) <!-- Start of picture text --> Minimum 1 investors Minimum 10 investors<br>60 55<br>50<br>50<br>45<br>40<br>40<br>35<br>30 30<br>2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br>Minimum 25 investors Minimum 50 investors<br>55 55<br>50 50<br>45 45<br>40 40<br>35 35<br>30 30<br>2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br>High Income Developing<br>Concentration Ratio Concentration Ratio<br>(5 largest investing MNEs) (5 largest investing MNEs)<br>Concentration Ratio Concentration Ratio<br>(5 largest investing MNEs) (5 largest investing MNEs)<br><!-- End of picture text --> Source: Authors’ analysis on fDi Markets. Note: The analysis relies on greenfield FDI investment project capital expenditure. The concentration ratio of country groups is calculated as the weighted average of each country’s concentration ratio , weighted by the country’s total investment. Countries are considered developing if classified as low income, lower-middle income or upper-middle income in 2003 based on the World Bank’s income classification.

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c) <!-- Start of picture text --> Timber (billion tons) Difference between Stochastic and Deterministic Solutions<br>6.2<br>6 0<br>5.8<br>−10<br>5.6<br>5.4 −20<br>5.2<br>−30<br>5<br>4.8 −40 Range of all sample paths10% quantile<br>25% quantile<br>4.6 50% quantile<br>−50<br>4.4<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2010 2020 2030 2040 2050 2060 2070 2080 2090<br>Year Year<br>d)<br>Output of Ecosystem Services (billion of 2004 US dollars) Difference between Stochastic and Deterministic Solutions<br>0<br>2.25<br>−1<br>2.2<br>−2<br>2.15<br>−3<br>2.1<br>2.05 −4<br>Range of all sample paths<br>2 −5 10% quantile<br>25% quantile<br>50% quantile<br>1.95 −6<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2010 2020 2030 2040 2050 2060 2070 2080 2090<br>Year Year<br>million tons<br>million of 2004 US dollars<br><!-- End of picture text --> Figure 4: Optimal Path of Land-Based Goods and Services under Uncertain Crop Productivity 26
**Chart 24**

<!-- Start of picture text --> Borowing Requirements (% of GDP)<br>16.0<br>LAC Europe Asia<br>14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>2007 2008 2009<br>Source: JP Morgan EM Debt and Fiscal Indicators (2009)<br>(% of GDP)<br><!-- End of picture text -->

**Borrowing requirements in the surveyed EMs therefore expanded, but different to other crises, this time around EMs were better prepared to handle it.** In Romania and Turkey, where the crisis impact was stronger, borrowing requirements trebled in 2009<sup>21</sup> . This contrasts with most Latin American and Asian countries where debt managers had to fund no more than 1.5 times the sums raised in 2008. The task was nonetheless challenging given that during Q3 and Q4 of 2008 the international capital markets were effectively closed and domestic market conditions in most EM countries had deteriorated. The apparent success with which debt managers were able to meet the additional borrowing demands could reflect that debt portfolios were less dependent on foreign borrowing, while the increase in primary deficit was limited. In addition the external markets, closed for nearly five months, reopened with foreign interest rates at historical lows as monetary policy in the G7 was aggressively expansionary.

# **This situation contrasts sharply with that of industrialized countries that plunged into deep deficits**

**and saw their debt to GDP ratios rise rapidly.** The implementation of large bailout packages<sup>22</sup> to keep the financial system afloat and the recession that followed the virtual paralysis of domestic credit and the burst of the real estate bubble, brought about a dramatic increase in the fiscal deficits and the borrowing requirements of these governments. Chart 25 presents the evolution in the primary and overall fiscal deficits as well as the debt/GDP ratio in industrialized countries in the period 2007-2009.

> 21 In Hungary the agreements with the Fund and the EU forced a fiscal adjustment which kept the government deficit within relatively narrow bands

> 22 Financial rescue programs in industrialized countries are estimated at 13.2% of GDP. See “The Future of Public Debt: Prospects and Implications” by Stephen G. Cecchetti, MS Mohanty and Fabrizio Zampolli, BIS

19
between migrant and non-migrant households which also affect schooling. We turn to this issue next.

# **3. Empirical Methodology and Identification Strategy**

# **3.1. Identification**

The first challenge in estimating the causal impact of migration on education outcomes is the possibility of unobserved characteristics of households which influence their decision to migrate also playing a role in their schooling decisions. For example, parents who care more strongly about the education of their children may migrate in order to earn income that can be used to pay for schooling expenses, and will also devote more attention and non-income resources to improving schooling outcomes of their children. A simple comparison of migrants and non-migrants would in this case overstate the education gains from migration. Alternatively, Hanson and Woodruff (2003) note that negative labor market shocks experienced by parents may both induce migration and require children to work instead of spending time in school, leading to a spurious negative relationship between migration and years of schooling. As such the direction of any selectivity bias is theoretically uncertain.

We therefore follow Woodruff and Zenteno (2001) and a number of subsequent studies<sup>9</sup> in using historic state-level migration rates as an instrument for current migration stocks. In particular, we use the U.S. migration rate from 1924 for the state in which the household is located, taken from Foerster (1925)<sup>10</sup> . Since this instrument only varies at the state level, we cluster our standard errors at the state

> 9 Hanson and Woodruff (2003); McKenzie and Rapoport (2004); López-Córdoba (2004); and Hildebrandt and McKenzie (2004) all employ historic migration rates as instruments for current migration.

> 10 Thanks to Chris Woodruff for supplying these historic rates.

10
factors, the precision of the method is still poor as reflected in the relatively high measures of error in Table 2, suggesting that national accounts growth is a poor predictor of survey growth and thus adding uncertainty about poverty projections.

The discussion above assumes a distribution-neutral adjustment to account for the HHSNAS gap. However, ‘top income’ adjustments are more appropriate if the source of the gap is originating from top incomes being mismeasured in surveys due to biased response rates or underreporting by the richest households. Since this adjustment mainly effects the very top of the distribution, that in all countries is above the $1.90 threshold, it has little effect on poverty measures. However, since we add a population segment to the survey distribution, poverty measures fall proportionally to the number of observations added to the top of the distribution. Figure A2.1 (in Appendix 2) shows the global poverty trends for the adjusted and unadjusted HHS data, with very similar estimates. The adjusted distributions give slightly lower estimates due to the added number of people in the denominator in poverty estimates.<sup>32</sup>

# **_Inequality and inclusive growth_**

While the implications of the top income adjustment have little effect on poverty measures, the implications for levels and trends in inequality are significant. Figure 6 compares the Gini coefficients from the unadjusted distributions with inequality from the distributions with topadjustments, using the method proposed by Chandy and Siedel (2017b).<sup>33</sup> At the national level (shown in Panel A of Figure 6), the observed Gini coefficients are on average 20% higher when using the top-income adjusted distributions. Other measures of levels of inequality, such as the 90/10 ratio and the Palma ratio, would also be drastically affected by such adjustments.

Measuring inequality globally using the top-adjusted distributions also results in a much higher level of global inequality, with the top income adjustment increasing the global 32 For example, in a country where poverty is estimated to be 30 percent in an unadjusted survey, and we expand the distribution by adding 10 percent of the population to adjust for missing people the top, the poverty rate would fall to 27.2 percent. Since the method only adds a rather small share of people to the distribution, the effect on aggregate poverty measures is small.

33 We apply the Chandy and Seidel (2017b) method by adding a top segment to the Lorenz curve from the survey distribution and thank the authors for providing the replication code to implement this.

25
# **References:**

Acemoglu, Daron, James A. Robinson, Simon Johnson (2001). "The Colonial Origins of Comparative Development: An Empirical Investigation American Economic Review, 91, pp. 1369-1401.

Arrow, Kenneth J. Partha Dasgupta, Lawrence H. Goulder, Kevin Mumford and Kirsten Oleson (2010) "China, the U.S., and Sustainability: Perspectives Based on Comprehensive Wealth" in _Is Economic Growth Sustainable?_ , G. Heal, ed. International Economics Association conference Volume 148, London: Palgrave Macmillan, pp. 92-144.

- Barro, R. J.  and Lee J. W. (2001) "International Data on Educational Attainment: Updates and Implications" _Oxford Economic Papers_ , 53 (3): 541-63.

- Caselli, F. (2005) “Accounting for cross-country income differences” In Aghion, P. and Durlauf, S. (Eds.), Handbook of Economic Growth.

- Chong, Alberto and Calderon, Cesar. (2000). “Causality and Feedback between Institutional Measures and Economic Growth.” _Economics and Politics_ , 2000, 12(1), pp. 69-81.

- Cohen, Daniel and Marcelo Soto (2007) "Growth and human capital: good data, good results" _Journal of Economic Growth_ ", 12:51–76

- Ferreira, S. and J. R. Vincent (2005) “Genuine savings: Leading Indicator of Sustainable Development?” _Economic Development and Cultural Change_ 53 (3): 737-54.

Fisher, I. (1906). _The Nature of Capital and Income._ London: Macmillan.

- Gundlach, E., Rudman, D. and Woessmann, L. (2002) "Second Thoughts on Development Accounting" _Applied Economics_ , 34, 11, 1359–1369.

- Hall, R.E., and C.I. Jones, (1999), “Why do some countries produce so much more output per worker than others?”, _The Quarterly Journal of Economics_ 114(1):83-116.

- Hamilton, K. and J. Hartwick (2005). Investing exhaustible resource rents and the path of consumption. _Canadian Journal of Economics_ 38:2, 615-621.

- Hanushek E.A., and D.D. Kimko (2000), “Schooling, Labor-Force Quality, and the Growth of Nations”, _The American Economic Review_ 90(5):1184-1208.

- Helpman (2004). _The Mystery of Economic Growth_ . Belknap Press of Harvard University Press: Cambridge, Massachusetts.

19
same cities from the Ghana Living Standard Survey (GLSS) 2012/13, a nationally representative household survey, and shows that average socioeconomic characteristics of experiment participants and their households are not statistically different from GLSS participants.

However, at baseline, the different groups exhibited significant differences in some of the variables not targeted in the randomization, notably in the propensity to be engaged in manufacturing and in the public sector. To account for these differences, in appendix tables D1 and D2 we present regressions in which we include controls for baseline sector of employment, days and hours worked per week at baseline, as well as baseline pay per week, in addition to the variables used in the randomization. Our main findings, which are presented below, are robust to including these controls.

Balance tables for the self-employed and wage employed are presented in appendix tables B6 and B7, respectively. The results are similar to those for the main sample; we cannot reject the null that there are no differences across treatment groups in terms of the variables targeted in the randomization, but do detect some differences across groups for variables that were not directly targeted – notably sector of employment, and days worked per week for the self-employed, and days worked per week for the wage employed. These differences attest to the need to include controls for these baseline differences when analyzing the reporting behavior of the different groups.

# **4.2 Aggregating Triweekly interviews**

Most of the triweekly interviews were conducted on the same days of the week (Tuesday, Thursday, and Saturday) as is shown in appendix table C1. Respondents were asked to recall their labor market outcomes over the previous two days. As a result, Saturday was consistently missed in the high-frequency reporting. The follow-up survey aimed to fill this gap by inquiring about respondents’ current and past labor market behavior on Saturdays, as well as all other days of the week. To obtain estimates of how many hours the triweekly group worked each week they reported on, we simply add up the number of days and hours they reported working over 3 consecutive twoday interviews, and we impute the number of hours they worked each Saturday using information they provided at endline using the following rules; (i) if respondents reported never working on a Saturday (approximately 52% of respondents) no adjustment was made (i.e. no hours or days were added); (ii) if a respondent reported working all Saturdays, we added one day of work and the 10
FIGURE 15. NEONATAL MORTALITY TRENDS BY MOTHER' S EDUCATION IN COTE D'IVOIRE, 1970-86 (LSMS 1985/1986) <!-- Start of picture text --> No schooling<br>so<br>' ' _ ~~~Primary education<br>N~~~~~~and above<br>Ci|<br>S970 SU7 *Lo 8935<br>Year (1)<br><!-- End of picture text -->

NOTE: (1) IMR5 -5 Year Moving Average.