rafmacalaba/datause-encoder-data huggingface dataset:
# **Sensitivity analysis**

As noted above, the methodology of propensity score matching does not fully correct for unobserved differences between the treated and control groups. While it is not possible to directly test for selection bias based on unobservables, it is possible to ascertain how sensitive the results reported above are to potential selection on unobservables by estimating Rosenbaum bounds (Rosenbaum 2002; Becker and Caliendo 2007; Caliendo and Kopeinig 2008). The underlying logic of this robustness check is that selection on unobservables implies that some individuals are more likely to receive remittances than others who have otherwise similar observable characteristics. Rosenbaum bounds report how strongly an unobservable characteristic must influence the selection into the treatment group such that the difference in outcome between the treated and control group is explained by nonrandom assignment.

Table 7 reports how much higher the odds of treatment must be among the treated to explain the statistical differences seen in the results above. The test was conducted for odds ratio equal to 1 (no selection bias) through 2 (double the odds of treatment) following the standards in the literature. Higher values mean that the difference between treated and control groups are more robust. Values closer to 1 are more sensitive to selection bias.

**Table 7.  Rosenbaum Bounds: Critical values of odds ratio of receiving treatment**

||<br>El Sal<br>|<br>vador<br>|<br>Guate<br>|<br>mala<br>|<br>Hon<br>|<br>duras<br>|
|---|---|---|---|---|---|---|
||2006|2014|2006|2014|2006|2014|
|Labor force participation|||||||
|Men|1.90|1.80|1.95|1.90|1.90|1.30|
|Women|1.75|1.60|1.00|1.10|1.00|1.15|
|Youth inactivity|||||||
|Men|1.05|1.25|1.40|1.45|1.15|1.05|
|Women|1.05|No Sig|No Sig|No Sig|1.10|No Sig|
|Informality|||||||
|Men|1.30|1.55|1.40|1.30|1.05|No Sig|
|Women|1.00|1.10|1.20|1.25|No Sig|No Sig|
|Self-employed|||||||
|All|1.20|1.35|1.40|1.50|No Sig|No Sig|
|Employer|||||||
|All|1.30|1.40|No Sig|No Sig|No Sig|1.05|

_Source:_ Authors’ tabulations using SEDLAC (CEDLAS and the World Bank). _Note:_ This table reports the results of Rosenbaum bounds test using the Mantel and Haenszel test statistic. The table reports the minimum value of gamma (odds ratio at which the treated group is more likely to be treated) for which the treatment effect could be explained by differences in selection rather than due to treatment. “No Sig” means the treatment effect is not statistically significant.

The values reported in Table 7 show that some of the results of the propensity score matching exercise are highly sensitive to selection bias. This includes the impact of remittances on female labor force participation in Guatemala and Honduras as well as female youth inactivity in all three countries. On the other hand, the results are robust for male participation (especially in El Salvador and Guatemala and Honduras 2006), male youth inactivity in Guatemala, and the negative impact on female labor force participation in El Salvador.

13
**Table 6:** Summary Statistics of Credit Exposures (EAD), Asset Class Concentration and Residual Maturity for 2010.

|Asset Class|Exp|osures at D|efault (E|AD)|Asset C|lass Conce|ntration|Res|idual Matu|rity|
|---|---|---|---|---|---|---|---|---|---|---|
||Min|Median|Max|AW|Min|Median|Max|Min|Median|Max|
|Corporates|32.5%|59.7%|88.3%|60.5%|11.8%|28.5%|55.9%|1.5|2.8|5.7|
|SMEs<sup>(1)</sup>|0.0%|0.2%|28.3%|1.8%|11.8%|28.5%|55.9%|0.0|2.8|4.1|
|Consumer Mortgage Loans|0.1%|7.7%|18.6%|7.5%|4.3%|37.8%|100.0%|4.2|14.9|17.6|
|Consumer Loans<sup>(2)</sup>|0.3%|12.8%|31.1%|11.5%|0.1%|2.5%|14.6%|0.0|1.0|4.6|
|Other Consumer Loans|4.5%|10.8%|37.5%|17.9%|4.9%|44.3%|95.5%|3.3|5.4|11.7|
|Sovereigns<sup>(3)</sup>|0.0%|0.1%|0.8%|0.3%|_NA_|_NA_|_NA_|0.0|0.0|0.0|
|Banks<sup>(4)</sup>|0.0%|0.1%|2.6%|0.6%|_NA_|_NA_|_NA_|0.0|0.1|2.3|

**Notes:**<sup>(1)</sup> Retail,<sup>(2)</sup> QRE retail,<sup>(3)</sup> loans to public institutions and state-owned enterprizes, and<sup>(4)</sup> loans to credit institutions. Residual maturity is the average residual time to maturity measured in years. The ”AW” column under the EAD heading measures the Exposures of the banking system computed as an asset weighted mean to account for the size (measured by assets) of the different banks in the construction of the aggregate average.

system shown under the AW column are fairly similar. Some differences for Other Consumer and SMEs Loans are visible though. Namely, the asset weighted mean is somewhat larger than the median in these two cases, suggesting that bigger banks allocate relatively more credit exposure to these asset classes than small banks. The overall exposure to the Consumer group is the second biggest, with a median (asset weighted mean) value of over 31% (36%) for the three consumer loan segments in total. This is due to the rapid increase in consumer lending during the most recent credit boom.

On average, lending to Corporates and SMEs is considered to be highly concentrated with the average share of the ten largest borrowers being around 30%. However, Other Consumer Loans and Consumer Mortgage Loans are the most concentrated asset classes. This could be due to these asset classes being small in absolute value and the youngest addition to the banking books of Eastern European banks.<sup>43</sup> A longer average residual time-to-maturity in each asset class is a proxy for the time 43Many banks in Eastern European countries have expanded their Consumer Mortgage Loans and Other Consumer Loans portfolios fairly rapidly and aggressively over the last decade.

55
Figure 4. The importances of services in developing East Asian countries’ trade and value added, according to the level of development a. Services exports <!-- Start of picture text --> 2005 2015<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>MNG KHM PHL<br>30 30<br>2010 LAOMMRKHMVNMIDNPHLCHNTHA MYS 2010 MMR IDNLAOVNMMNGTHACHN MYS<br>0 0<br>4 6 8 10 12 4 6 8 10 12<br>Log of GNI per capita Log of GNI per capita<br>b. Non‐travel services exports<br>2005 2015<br>40 40<br>35 35<br>30 30<br>PHL<br>25 25<br>20 20<br>15 MNG 15<br>105 KHM LAO IDNPHLCHNTHA MYS 105 KHMMMRIDN MNG MYSCHN<br>VNM LAO THA<br>MMR VNM<br>0 0<br>4 6 8 10 12 4 6 8 10 12<br>Log of GNI per capita Log of GNI per capita<br>c. Services value added<br>2005 2015<br>100 100<br>90<br>90<br>80<br>80<br>70<br>70<br>60<br>50 MNGPHL THA MYS 60 PHLTHA<br>40 MMR KHM CHN 50 LAO MNGCHN MYS<br>30 LAO KHM VNM IDN<br>40 MMR<br>20<br>30<br>10<br>0 20<br>4 6 8 10 12 4 6 8 10 12<br>Log of GNI per capita Log of GNI per capita<br>Share of services in total exports (percent) Share of services in total exports (percent)<br>exports (percent) (percent)<br>Share of services (excl. travel) in total Share of services (excl. travel) in total exports<br>(percent) (percent)<br>Share of services value added in GDP Share of services value added in GDP<br><!-- End of picture text --> Sources: WTO, World Bank World Development Indicators (WDI). Notes: GNI is in current US dollars calculated using the Atlas method. Vertical lines indicate thresholds for lower middle, upper middle and high income categories*. In panel b, countries with share of services in total exports greater than 40 percent are not shown: AFG, COM, CYP, DJI, GRC, LUX, KIR, CPV, CUB, MLT, IRL, LBN

*https://datahelpdesk.worldbank.org/knowledgebase/articles/906519‐world‐bank‐country‐and‐lending‐groups.

10
**Table 3.2. Average Annual Growth of Real Exports (Goods and Non-Factor Services, Percentages)**

||**1992-1993**|1994-1997|**1998-2001**|**1992-2001**|
|---|---|---|---|---|
|Argentina|1.8|14.4|3.6|7.6|
|7majorLACcountriesw/oArgentina(*)|<br>7.7|11.1|7.8|9.1|
|Uppermiddle-incomeLDC's**(**)**|7.8|11.9|8.9|9.8|
|**World**|3.8|8.4|6.6|6.8|
|_MemoItem:_|||||
|Argentina'sREERgrowth<br>(appreciation+)|10.4|-4.1|5.8|2.8|

# **Notes:**

**(*)** Bolivia, Brazil, Chile, Colombia, Mexico, Peru, and Venezuela.

**(**}** World Bank classification: American Samoa, Antigua and Barbuda, Argentina, Barbados, Botswana, Brazil, Chile, Costa Rica, Croatia, Czech Republic, Dominica, Estonia, Gabon, Grenada, Hungary, Isle of Man, Latvia, Lebanon, Libya, Lithuania, Malaysia, Malta, Mauritius, Mayotte, Mexico, Oman, Palau, Panama, Poland, Puerto RioD, Saudi Arabia, Seychelles, Slovak Republic, St. **Kitts** and Nevis, St. Lucia, Trinidad and Tobago, Uruguay, and Venezuela.

**Source:** World Development Indicators - World Bank.

Such views were most often based on simple comparisons of the prevailing real exchange rate with its historical value, under the view that the equilibrium REER is constant - the so-called _purchasing power parity_ (PPP) view. Figure 3.1 illustrates the use of this approach to assess the misalignment of the Argentine REER over the 1990s, taking as equilibrium value the REER average over the last four decades (1960-2001). The latter is depicted by the horizontal line in the figure. Comparison with the actual REER suggests that the peso was initially undervalued in 1990, but became increasingly overvalued after the introduction of the Convertibility Law in 1991. The overvaluation peaked initially in 1993, (leclined later through 1996, and rose again to nearly 50 percent in 2001.

However, this approach neglects two important factors that may cause the equilibrium REER to change over time The first one is the relative level of productivity across countries. Other things equal, an increase in productivity in traded goods sectors relative to nontraded goods sectors in a given country above that experienced by its trading partners should lead to a REER appreciation -- precisely the argument advanced by some observers to justify the rapid real appreciation of the Argentine peso in the early 1990s.<sup>14</sup> The second ingredient is the adequacy of tlh current account to sustain equilibrium capital flows. The real exchange rate must be consistent with a balance of payments position that does not lead to explosive accumulation of external assets or

> **14** This is the so-called Balassa-Samuelson effect.

19
Policy ReseaRch WoRking PaPeR 4637

# **Abstract**

Large numbers of agricultural labor moved from the countryside to cities after the economic reforms in China. Migration and remittances play an important role in transforming the structure of rural household income. This paper examines the impact of rural-to-urban migration on rural poverty and inequality in the case of Hubei province using the data of a 2002 household survey. Since remittances are a potential substitute for farm income, the paper presents counterfactual scenarios of what rural income, poverty, and inequality would have been in the absence of migration. The results show that, by providing alternatives to households with lower marginal labor productivity in agriculture, migration leads to an increase in rural income. In contrast to many studies that suggest the increasing share of non-farm income in total income widens inequality, this paper offers support for the hypothesis that migration tends to have egalitarian effects on rural income for three reasons: (i) migration is rational self-selection – farmers with higher agricultural productivities choose to remain in local agricultural production while those with higher expected return in urban non-farm sectors migrate; (ii) poorer households facing binding constraints of land shortage are more likely to migrate; and (iii) the poorest poor benefit disproportionately from remittances This paper is a product of the Poverty Reduction and Economic Management Department, East Asia and Pacific Region. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at xluo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
economies and weak benefits for TFP from the diversity of local economic activity. Henderson‟s specification relates plant-level time-varying output controlling for inputs (i.e., TFP levels) to time-varying agglomeration indices at the industry-location level hence is at a more disaggregated level than those in earlier papers. Cingano and Schivardi (2004) estimate production functions for Italian industries using firm-level survey data and following the Olley and Pakes (1996) methodology. Based on growth in industryregion averages of their firm-level TFP estimates, they find evidence of MAR externalities but no effects of sector competition or diversity.<sup>6</sup> Our paper‟s contribution is threefold. First, we examine the importance of the local economic structure - concentration, specialization, and competition - for long-run TFP growth at the sector-location level using manufacturing census data for an emerging economy. We differ from Henderson (2003) and Lopez and Südekum (2009) since we focus on _long-run,_ rather than on yearly effects of the local economic structure. Furthermore, we focus on TFP _growth_ , rather than TFP _levels_ , as our outcome variable. By considering long-run effects, our paper mitigates the potential concern of a spurious relationship between agglomeration and TFP growth that could arise from the correlation between unobserved determinants of TFP growth across sectors and regions and the agglomeration measures (Martin et al., 2009). Our inclusion of sectoral and regional fixed effects in some specifications explicitly addresses this concern. Second, our estimates of TFP are unbiased, obtained as the residuals from flexible translog production

> 6 Lopez and Südekum (2009) examine a related question using Chilean plant-level data following Henderson (2003)‟s approach. Their study differs from ours in several important respects. First, their empirical specification relates plant-level time-varying TFP levels to time-varying indices of concentration and diversity at the regional and sector level. Second, their indices of concentration and diversity are based on a simple count of the numbers of plants in particular sectors in the region. Third, their study covers an earlier sample period.

5
|Table S.31: Checking on|spillover ef|ects - GPA|z-scores:|Case B (|OLS)||
|---|---|---|---|---|---|---|
|Dependent variables:|||GPA|z-scores|||
||Bengali|English|Math|General<br>science|Islam<br>studies|Bangladesh<br>general<br>studies|
||(1a)|(1b)|(1c)|(1d)|(1e)|(1f)|
|**December 2012**|||||||
|Share of treated students and their|1.067|2.626|2.822|0.408|-0.467|-0.826|
|siblings in each school-grade|(1.851)|(2.218)|(2.505)|(2.753)|(2.036)|(1.362)|
|Share of students in each school-grade that|-0.082|-0.498|-0.627|0.248|-0.300|0.411|
|had no access to electricity at baseline|(1.208)|(1.539)|(1.423)|(1.486)|(1.278)|(0.836)|
|All products|-0.027|-0.042|-0.006|-0.038|-0.102|-0.081|
||(0.080)|(0.077)|(0.069)|(0.073)|(0.072)|(0.089)|
|One product|-0.035|-0.004|0.006|-0.017|-0.121|-0.093|
||(0.095)|(0.080)|(0.095)|(0.104)|(0.085)|(0.094)|
|R-squared|0.222|0.219|0.212|0.211|0.223|0.223|
|No. of obs.|849|849|849|849|849|849|
||(2a)|(2b)|(2c)|(2d)|(2e)|(2f)|
|**December 2013**|||||||
|Share of treated students and their|1.319|-0.586|3.618*|3.584*|1.880|-1.158|
|siblings in each school-grade|(1.988)|(3.003)|(1.904)|(1.927)|(2.022)|(1.869)|
|Share of students in each school-grade that|-1.113|-1.072|-1.416|-1.915|0.443|0.445|
|had no access to electricity at baseline|(1.482)|(1.878)|(1.237)|(1.482)|(1.323)|(1.501)|
|All products|-0.048|-0.056|-0.044|0.049|0.054|0.066|
||(0.087)|(0.064)|(0.058)|(0.084)|(0.083)|(0.070)|
|One product|0.024|-0.133*|-0.081|0.039|0.022|0.128|
||(0.086)|(0.071)|(0.072)|(0.078)|(0.079)|(0.089)|
|R-squared|0.259|0.257|0.398|0.308|0.262|0.272|
|No. of obs.|849|849|849|849|849|849|
||(3a)|(3b)|(3c)|(3d)|(3e)|(3f)|
|**December 2014**|||||||
|Share of treated students and their|-2.826***|0.394|0.660|-1.869|-2.793*|-4.240***|
|siblings in each school-grade|(0.884)|(0.817)|(1.256)|(1.182)|(1.402)|(1.292)|
|Share of students in each school-grade that|1.429***|0.435|0.585|0.787|1.422***|1.711***|
|had no access to electricity at baseline|(0.414)|(0.437)|(0.637)|(0.507)|(0.516)|(0.547)|
|All products|-0.082|-0.035|-0.101|-0.100|-0.056|-0.122|
||(0.082)|(0.085)|(0.088)|(0.081)|(0.088)|(0.091)|
|One product|0.052|0.043|0.038|-0.068|0.033|-0.030|
||(0.092)|(0.086)|(0.088)|(0.092)|(0.099)|(0.093)|
|R-squared|0.120|0.100|0.163|0.152|0.113|0.139|
|No. of obs.|845|845|845|845|845|845|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

76
GDP will grow as slow as 5 percent per annum, and as fast as 9 percent, in the next ten years. Similarly, with identical probability, the exchange parity can lie in the range of 225-375 MRO per unit of US dollar by 2023.

**Table AI.1. VAR model: estimated coefficients, t-statistics in [ ].**

|Growth rate of real<br>GDP (%)|Relative change of<br>real exchange rate (%)|Domestic real interest<br>rate (%)|
|---|---|---|
|Growth rate of real GDP (%) - lagged<br>0.306<br>[ 0.64211]|-2.876<br>[-1.81657]|-0.236<br>[-0.15491]|
|Relative change of RER (%) - lagged<br>0.003|-0.329|-0.198|
|[ 0.02408]|[-0.86196]|[-0.53842]|
|Domestic RIR (%) - lagged<br>0.172|-0.758|-0.351|
|[ 1.21590]|[-1.60890]|[-0.77467]|
|Intercept<br>-0.759|-5.747 (*)|8.267|
|[-0.20346]|[-0.46428]|[ 0.69346]|
|Public Investment (% of GDP)<br>0.325|2.020 (**)|0.117|
|[ 0.98032]|[ 1.83960]|[ 0.11042]|
|R-squared<br>0.316|0.411|0.227|
|Adj. R-squared<br>0.042|0.176|-0.082|
|Sum sq. resids<br>82.715|911.335|845.353|
|S.E. equation<br>2.876|9.546|9.194|
|F-statistic<br>1.155|1.748|0.733|
|Log likelihood<br>-34.089|-52.086|-51.522|
|Akaike AIC<br>5.212|7.611|7.536|
|Schwarz SC<br>5.448|7.847|7.772|
|Mean of dependent variable<br>5.441|-3.570|6.612|
|S.D. of dependent variable<br>2.939|10.517|8.837|
|Determinant resid covariance (dof adj.)|12713.8||
|Determinant resid covariance|3767.0||
|Log likelihood|-125.6||
|Akaike information criterion|18.7||
|Schwarz criterion|19.5||

Notes: Adjusted values are (*) = 8.5 and (**) = 1.

Source: World Bank calculations

# **Table AI.2. VAR model: estimated matrix of covariances of residuals.**

||Growth rate of real<br>GDP (%)|Relative change of<br>real exchange rate (%)|Domestic real interest<br>rate (%)|
|---|---|---|---|
|Growth rate of real GDP (%)|8.3|-16.4|-21.6|
|Relative change of RER (%)|-16.4|91.1|53.9|
|Domestic RIR (%)|-21.6|53.9|84.5|

Notes: Adjusted values are one-fifth of all covariances.

Source: World Bank calculations 31
A second model VECM was estimated taking logs of real Ml, real GDP and the inflation rate as endogenous variables. As before, intercepts (but no trends) appear in the cointegrating relation as well as in the short-run model. We also find that the signs of the estimated long-run parameters are consistent with economic theory.

|**Venezuela:VECMforLogs**<br>**RealGDPan**<br>**_LagStruct_**|**ofRealMoney**<br>**dInflation**<br>**_ure:1,4_**|**Balances,**||
|---|---|---|---|
|**Statistic**|**LogReal**<br>**Ml**|**Log Real**<br>**GDP**|**Log**<br>**Inflation.**|
|Coefficientincointegrating equation|1|0.24|-0.48|
|GoodnessofFit(ordinaryR<sup>2</sup>)|0.74|0.59|0.33|
|Akaike InformationCriterion|-1.99|-3.76|-3.96|
|Schwarz Criterion|-1.62|-3.38|-3.59|

Finally, we estimated a VECM model taking as endogenous variables the logs of real Ml, real GDP and the nominal rate of currency depreciation. Intercepts but no trends appear in both the cointegrating relations and the short-run model. As before, the signs of the estimated long-run parameters are consistent with economic theory.

|**Venezuela:VECMforLogs**<br>**RealGDPandDe**<br>**_Lag Struct_**|**ofRealMoney**<br>**preciationRat**<br>**_ure:1,2_**|**Balances,**<br>**e**||
|---|---|---|---|
|**Statistic**|**LogReal**<br>**Ml**|**LogReal**<br>**GDP**|**Logof**<br>**Depr.**|
|Coefficientincointegrating equation|**1**|0.26|-0.68|
|GoodnessofFit(ordinaryR<sup>2</sup>)|0.68|0.63|0.19|
|Akaike InformationCriterion|-1.88|-3.80|-3.48|
|SchwarzCriterion|-1.52|-3.43|-3.11|

# **Estimation results following the combined approach**

A model taking as endogenous variables the first differences of log real Ml, log real GDP and the inflation rate, was estimated. Very strong convergence was achieved in 86 iterations. The value of the log-likelihood is 276.18. Estimated high and low Money Demand in Venezuela, Mario A. Cuevas 19
Inequality, Economic Growth and Nonrenewable Resources Table 4. Indicators of Heterogeneity across Countries in the Dataset, for Control Variables

||Panel A:<br>Education<br>ratio<sup>14</sup>|Variable inclu<br>CPI|ded in the GINI equation<br>Services value<br>added (% of<br>GDP at market<br>prices)|<br>Manufacturing<br>value added<br>(% GDP at m.<br>prices)|Agriculture<br>value added<br>(% of GDP at<br>m. prices)|
|---|---|---|---|---|---|
|High income|4.6|80.9|66.3|17.6|3.9|
|Upper middle income|0.9|65.3|52.3|17.1|7.9|
|Lower middle income|0.7|75.7|48.5|18.6|18.1|
|Low income|0.4|63.7|45.4|14.6|29.4|
|Average|1.9|73.2|54.8|17.4|11.5|
||Panel B:<br>(log)Labor<br>Force|Variables inclu<br>(log)Gross<br>fixed<br>capital<br>formation|ded in the GDP equatio<br>(log)Export<br>(log)RE|n<br>ER<br>World oil<br>price index|<br>World<br>metal price<br>index|
|High income|9.5|11.7|11.8<br>4.5|<br>73.0|91.9|
|Upper middle income|9.0|9.7|9.9<br>4.6|<br>73.0|91.9|
|Lower middle income|9.8|9.4|9.4<br>4.7|<br>73.0|91.9|
|Low income|9.0|6.7|7.4<br>4.8|<br>73.0|91.9|
|Average|9.4|9.9|10.1<br>4.6|73.0|91.9|

Finally, to introduce the empirical analysis Table 5 summarizes the pairwise correlations between the most relevant variables. As shown in the table, resource dependence is positively associated with the Gini index and negatively associated with GDP per capita at a significance level greater than 1 percent. In other words, the simple correlations show that the more dependent a country is on nonrenewable resources, the higher its Gini coefficient and the lower its GDP per capita.

Table 5: Correlation Matrix for the Main Variables

||Resource<br>Dependence|Gini index|Log GDP|
|---|---|---|---|
|Resource Dependence|1|||
|Gini index|0.242***|1||
|GDPpercapita|-0.142***|-0.350***|1|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

14 Education ratio is the ratio between the share of the population with secondary and tertiary education and the share of population with primary or no education.

9