rafmacalaba/datause-encoder-data huggingface dataset:
refugees with mental health issues have negative externalities on their family members’ welfare outcomes.

# **4.2. Robustness tests**

Our results are robust to a number of sensitivity checks. We briefly summarize the results here and offer more detailed results and discussion in Appendix D and in Tables A3-A16 (Appendix A). First, we estimate the linear FE and IV-FE models for employment status and other binary outcomes variables instead of the logit FE and IV-logit-FE models. Second, we analyze the original non-standardized mental health scores. Third, for the labor income regressions in Table 1, refugees who do not work are assumed to have zero incomes. Alternatively, we also analyze a subsample of workers that have positive incomes. Fourth, to address panel attrition issues, we use a balanced sample as well as model with longitudinal weights provided by BNLA. Fifth, while language skills are commonly employed in studies relating to labor market outcomes of refugees (e.g., Dagnelie et al., 2019; LoPalo, 2019), these variables might be endogenous so we exclude these variables from the regressions. Our results remain robust.

Sixth, we examine other (overall) health and physical health issues and find negative impacts of overall health, but no impact of physical health, on labor outcomes. Seventh, we construct alternative disaggregate measures of mental illness instead of the sum of the K6 items. Figure 1 examines each item separately to determine which dimension drives the effects of mental health. Using our preferred model specifications, we find that most of these dimensions of mental illness have a negative impact on employment status and labor income. We further create a dummy indicator to measure serious mental illness, defined as mental health score being greater than 19 (Chen et al., 2017). The results using the new measure remain qualitatively similar.

16
- 32 -

and infrastructure accumulation (Figure 19).<sup>16</sup> A constant elasticity of paved road stocks with respect to GDP is estimated at 0.828. Using the average real growth rate in the past five-year, the annual growth rate in paved roads in each country is calculated. The elasticity of electricity capacity is almost unity. The elasticity of improved water and sanitation access with respect to per capita GDP varies depending on development level. Notably, additional populations with water supply and sewage access are calculated separately, though our bid function cover both simultaneously. But they are basically independent systems. This calculation _does_ ignore the causality problem for simplicity. But in our argument it does not matter which causes which. The important fact is that more infrastructure capacity is required to support economic growth and such positive correlation is very predictable even in a simple regression.

Figure 19. Correlation between Economic Development and Infrastructure Stocks <!-- Start of picture text --> 18 8<br>Ln(Installed Capacity) = 1.014 Ln(GDP) - 23.147<br>16 Ln(Paved Road) = 0.828 Ln(GDP) - 10.091 6 (0.033) (0.774)<br>14 (0.037) (0.856) R 2 = 0.85 Obs. = 168<br>R 2 = 0.76 Obs. = 167 4<br>12<br>2<br>10<br>0<br>8<br>-2<br>6<br>4 -4<br>2 -6<br>0 -8<br>15 17 19 21 23 25 27 29 31 15 17 19 21 23 25 27 29 31<br>Ln(GDP constant 2000 US$) Ln(GDP constant 2000 US$)<br>140 140<br>Water Access = 31.14 Ln(GDP per capita) - 1.47 Ln(GDP per capita) 2 - 65.27 Sanitation Facility Access = 38.92 Ln(GDP per capita) - 1.60 Ln(GDP per capita) 2 - 130.2<br>120 R 2 = 0.56 Obs. = 159(6.04) (0.37) (24.06) 120 (8.71) (0.57) (32.29)R 2 = 0.63 Obs. = 148<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>2 4 6 8 10 12 2 4 6 8 10 12<br>Ln(GDP per capita constant 2000 US$) Ln(GDP per capita constant 2000 US$)<br>Ln(Paved road length km)<br>Ln(Electricityinstalled capacity GW)<br>Improve water access (%)<br>Improve sanitation facility access (%)<br><!-- End of picture text --> Source: Author’s calculation from _World Development Indicators_ and Energy Information Administration.

16 This follows, for instance, World Bank (1994). For more sophisticated empirics, see Canning and Pedroni (1999), Fay and Yepes (2003), and Calderón and Servén (2004). While Calderón and Servén estimate the impact of infrastructure quantity and quality on economic growth, Fay and Yepes estimate the effect of economic growth and other conditions on infrastructure accumulation.
<!-- Start of picture text --> Figure 5: Health expenditures Transition<br> as % of sub-national expenditures Developing<br>25<br>20<br>15<br>10<br>5<br>0<br>19801981198219831984198519861987198819891990199119921993199419951996199719981999<br><!-- End of picture text --> Source: IMF Government Finance Statistics (various years) and World Bank Fiscal Indicators. (http://www1.worldbank.org/publicsector/decentralization/fiscalindicators.htm) In transition economies, sub-national educational expenditures as a percentage of public sector education expenditures have declined from 71% in 1981 to 55% in 2000. In developing countries, on the other hand, such expenditures have risen from 21% of total public sector education expenditures in 1980 to 40% of the same in 2000. In health, the role of sub-national governments in total public sector health expenditures in transition economies declined from about 92% in 1977 to 39.2% in 2000. In developing countries, the same role expanded from about 22% of total education expenditures to 57% of the same in 1999. These trends, however, hide the wide variations in such ratios across countries as shown in Table 2.

On the high side, in China (56%), India (46%) and Argentina (40%) are noteworthy for the relative importance of sub-national expenditures in total public expenditures. Moldova (18%) and South Africa (18%) have the highest percentage of these expenditures with respect to their GDP. Slovak Republic (8%), Dominican Republic (2.6%) are noted for the lowest share of sub-national expenditures in total public sector expenditures in transition and developing countries respectively. As a percentage of GDP, Croatia (0.01%), Dominican Republic (0.4%), Costa Rica and Bahrain (less than 1% of GDP) are noteworthy.

14
Further evidence of the heterogeneity across banks in the crowding-out channel comes from analyzing changes in bank lending more granularly. Doing so allows us to relate lending adjustments within a state by banks with varying ex ante exposures to the state’s public indebtedness. Our exercise is based on regressions summarized in equation 5, with observations at the bank-state-month and firm-bank-month levels. Table 6 summarizes our results for the statebank-month (Panel A) and firm-bank-month (Panel B) data. In Panel A, the outcome of interest corresponds to the total volume lent to state-government _s_ by bank _b_ at month _m_ . Our estimates from columns 1 to 3 indicate that the contraction in lending to ex ante more indebted local governments after the Law is mainly driven by banks with higher ex ante exposures to local public debt. That is, in a state with an average ex ante public indebtedness ratio of around 0.86, a onestandard-deviation increase in bank exposure leads to a 23 percent decline in lending to the government. This effect remains significant and larger in magnitude when bank-month and statemonth fixed effects are further introduced in the regression (columns 2 and 3).

In Panel B, the outcome of interest corresponds to the volume of loans extended by bank _b_ to firm _f_ in month _m_ . Our results show that after the Law, banks ex ante more exposed to the public debt of a state increase their lending to private firms headquartered in the state (Columns 4 to 6). More concretely, a one-standard-deviation increase in bank exposure is associated with a 2.3 percent increase in lending to firms (Column 4).<sup>38</sup> Estimates from Column 5 further indicate that this adjustment in credit supply remains (and becomes economically larger) once we saturate our specification with bank-month and state-month fixed effects. In Column 6, we further include firmmonth fixed effects to exhaustively control for unobserved time-varying firm fundamentals (e.g., firm risk, investment opportunities, and balance sheet movements). One drawback of the inclusion of firm-month fixed effects is that we restrict the sample to firms that at a given month have loans with more than one bank. Thus, this exercise could bias our results downwards since firms with multiple lenders tend to have lengthier credit histories and greater access to financing. While we drop more than half of the observations, the results overall corroborate that after the FD Law, firms in more indebted states experience an expansion in the supply of credit by banks previously more exposed to local public debt. In a state with an average public indebtedness ratio, a one-standard-

> 38 While the magnitude of the impact on bank lending to states (around 30 percent) is higher than the impact to private firms (2 percent) this regression is only analyzing the impact at the intensive margin, not the bank lending to new firm-bank relations. As noted in Table 5, where we analyze results at the aggregate bank-month level, the magnitudes of the impact of the FD Law on bank lending to states and firms are relatively similar (albeit with opposing signs).

30
Table 6 Descriptive statistics of the CCR efficiency index (Normal and Bias-Corrected) by Industry and country <u>(year 2005).</u>

|**Country/industry**|**Be**|**verages**||**Food**|**Ga**|**rments**|**Metals**|**and machinery**|
|---|---|---|---|---|---|---|---|---|
|**CCR efficiency**<br>**index**<br>|**Normal**|**Bias Corrected**<br>|**Normal**|**Bias Corrected**<br>|**Normal**|**Bias Corrected**<br>|**Normal**|**Bias Corrected**|
|Bulgaria|0.781|0.658|0.977|0.866|0.811|0.682|0.780|0.718|
|Czech Republic|0.611|0.554|0.536|0.504|0.554|0.518|0.687|0.656|
|Estonia|0.547|0.492|0.503|0.430|0.609|0.566|0.777|0.722|
|Hungary|0.752|0.653|0.763|0.681|0.719|0.628|0.700|0.656|
|Latvia|0.784|0.627|||0.790|0.627|0.769|0.693|
|Lithuania|0.751|0.645|||0.606|0.564|0.870|0.801|
|Moldova|0.665|0.598|0.525|0.485|0.560|0.521|0.664|0.637|
|Poland|0.624|0.571|0.678|0.617|0.663|0.620|0.726|0.685|
|Romania|0.708|0.636|0.759|0.682|0.706|0.629|0.733|0.682|
|Slovak Republic|||0.647|0.608|0.552|0.519|0.768|0.726|
|Slovenia|0.702|0.616|0.673|0.621|0.927|0.781|0.912|0.814|
|Mean|0.693|0.605|0.673|0.611|0.682|0.605|0.762|0.708|

Source: DDP Quick Query database of WDI & GDF, World Bank.

# _5.2.1 Estimation of the propensity score_

The propensity score calculated to reflect the probability of a firm receiving overdraft facility, predicts that firms that are younger are more likely to receive benefits. The score, presented in Table 7, is predicted based upon a logit model controlling for factors such as age, age squared, and dummy variables (size firm, country income level, and domestic owners). Each additional year of existence (age) lowers the likelihood of receiving overdraft facilities. Country income level and owners characteristics do not seem to influence the likelihood of receiving overdraft facilities.

19
of private manufacturing firms to these deficiencies; and (iii) related costs of various private provisions to substitute or complement inadequate public sector supplies. The empirical findings generated policy implications in: (a) deregulation to create contestable markets for the supply of various infrastructure services; (b) potential efficiency gains from economies of scale and scope through private sector participation in such emerging markets; and (c) socially efficient pricing policies which take into account the presence of 'endogenous' congestion, subsequent system failures, and private provisions by firm size and location. Among a number of publications from the Nigeria study, the key findings can be found in the following: Lee and Anas (1992a); Lee and Anas (1992b); Baumol and Lee (1991); Lee and Anas (1989).

During the past several years or so, there have been shifts in the conceptual framework and policy thinking in the infrastructure sector within and outside the Bank, e.g., 1994 World Development Rert on infrastructure. The study on Nigeria contributed to such shifts which include: (a) Infrastructure services are not only final consumptions to meet the 'basic needs' of households but also should be recognized as important 'intermediate inputs' for productive activities of manufacturing and commercial enterprises.

(b) The criteria for evaluating the productivity of infrastructure should not only include the efficiency of infrastructure facilities on the supply side, but also the productivity of end-users, i.e., firms, and households on the demand side.
|**[7]**<br>**[8]**<br>**hange)**<br>**)**<br>**HP**|1.078***<br>(0.269)<br>0.814<br>(0.803)<br>-30.016***<br>-30.543***<br>(2.444)<br>(2.419)<br>0.033***<br>0.031***<br>(0.006)<br>(0.006)<br>0.030<br>0.030<br>(0.132)<br>(0.135)<br>0.043**<br>0.063***<br>(0.019)<br>(0.020)<br>0.150**<br>0.141*<br>(0.074)<br>(0.074)<br>1498 / 82<br>1577 / 82<br>0.301<br>0.286<br>0.323<br>0.316<br>3516<br>3547|.<br>.|
|---|---|---|
|**[5]**<br>**[6]**<br>**cycle (% c**<br>**(t+1**<br>**BK**|0.599***<br>(0.222)<br>-0.451<br>(0.712)<br>-64.450***<br>-64.654***<br>(3.555)<br>(3.455)<br>0.017***<br>0.016***<br>(0.005)<br>(0.005)<br>-0.124<br>-0.139<br>(0.148)<br>(0.144)<br>0.032**<br>0.038**<br>(0.015)<br>(0.015)<br>0.008<br>-0.015<br>(0.059)<br>(0.059)<br>1327 / 82<br>1390 / 82<br>0.415<br>0.401<br>0.149<br>0.175<br>3108<br>3114|.<br>.<br>%; *** significant at 1%|
|**[4]**<br>**P**|0.969***<br>(0.33)<br>0.818***<br>(0.31)<br>0.861***<br>(0.303)<br>0.788**<br>(0.344)<br>-1.946***<br>(0.314)<br>0.011***<br>(0.003)<br>0.262***<br>(0.083)<br>0.039***<br>(0.008)<br>0.06<br>(0.034)<br>1548 / 82<br>0.607<br>3.246<br>1994|.<br>t 10%; ** significant at 5|
|**Table 5**<br>_Sample of 1974-2004_<br>_Dependent Variable: Percentage of Change of the Real Gross Domestic Product_<br>**Variables**<br>**[1]**<br>**[2]**<br>**[3]**<br>**trend (% change)**<br>**(t+1)**<br>**BK**<br>**H**|int1. Index (t)<br>0.562***<br>0.270***<br>(0.164)<br>(0.104)<br>int1. Index (t-1)<br>0.598***<br>0.345***<br>(0.165)<br>(0.106)<br>int1. Index (t-2)<br>0.354**<br>0.238**<br>(0.169)<br>(0.104)<br>int1. Index (t-3)<br>0.117<br>0.196*<br>(0.168)<br>(0.105)<br>int2. Index (t)<br>1.555***<br>(0.54)<br>int2. Index (t-1)<br>1.810***<br>(0.52)<br>int2. Index (t-2)<br>1.892***<br>(0.504)<br>int2. Index (t-3)<br>1.177*<br>(0.659)<br>_Control Variables_<br>LGDP_BK_trend (t)<br>-3.157***<br>-2.894***<br>(0.478)<br>(0.492)<br>LGDP_HP_trend (t)<br>-2.163***<br>(0.313)<br>LGDP_BK_cycle (t)<br>LGDP_HP_cycle (t)<br>∆Log(ToT)<br>0.024***<br>0.024***<br>0.011***<br>(0.004)<br>(0.004)<br>(0.003)<br>Population growth<br>0.361***<br>0.368***<br>0.249***<br>(0.128)<br>(0.130)<br>(0.082)<br>Financial account to GDP<br>0.038***<br>0.048***<br>0.034***<br>(0.013)<br>(0.013)<br>(0.009)<br>Trading partners growth<br>0.121**<br>0.129**<br>0.05<br>(0.052)<br>(0.054)<br>(0.032)<br>Observations / Countries<br>1298 / 82<br>1352 / 81<br>1477 / 82<br>R-squared<br>0.474<br>0.481<br>0.602<br>Mean Dep. var.<br>3.329<br>3.28<br>3.298<br>St Dev De var<br>2591<br>2608<br>2001|p. .<br>.<br>.<br>.<br>Note:_y_(t, t+s) corresponds to the average over the period t to t+s of the variable_y_.<br>Except otherwise indicated, all controls are averages for the period over which the dependent variable is measured.<br>All regressions included country fixed effects and Time dummies / Robust standard errors in parentheses / * significant a|
Fisheries Department (2001a). _Report on Handling, processing and Marketing of Fish in Kenya: progress Towards Compliance with National and International Fish Quality and Safety Standards February 2001._ Fisheries Department, Nairobi.

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Francis, E. and Hoddinott, J. (1993). Migration and Differentiation in Western Kenya: A tale of Two Sub-Locations. _Journal of Development Studies_ , 31, 1, 29-42.

Geheb, K. (1997). _Regulators and Regulated: Fisheries Management Options and Dynamics in Kenya’s Lake Victoria Fishery._ PhD Thesis, University of Sussex, Geheb, K. and Binns, T. (1997). ‘Fishing Farmers’ or ‘Farming Fishermen’? The Quest for Household Income and Nutritional Security on the Kenyan Shores of Lake Victoria. _African Affairs,_ <u>96, 73-93.</u> Gibbon, P. (1997). _Of Saviours and Punks: The Political Economy of the Nile Perch Marketing Chain in Tanzania._ Centre for Development Research, Copenhagen.

Gitonga, N.K., Okal, L. And Mutegi, E. (2000). _Effect of EU Bans on Kenya Fisheries_ . Fisheries Department, Nairobi.

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80
Educational Achievement (IEA) in 35 developed and emerging countries.<sup>5</sup> The aim was to obtain an internationally comparable database for the reading literacy of students in what is the fourth grade of primary school in most education systems. The assessment was designed to capture the skills and features that are necessary “to understand and use those written language forms required by society and/or valued by the individual” (Campbell et al. 2001, p. 3). This definition leads to a real-life focus of the study. Because most national curricula require that students are able to use reading to learn, the definition also ensures that the international comparability of the measured reading performance should not be severely affected by differing national curricula (cf. Gonzalez and Kennedy 2003).

The Latin American countries that participated in PIRLS are Argentina and Colombia. To be able to compare and benchmark the Latin American results, we also analyze an additional six countries. These are Turkey and Macedonia, which show average performance levels similar to Argentina and Colombia. Together, these four countries form our group of “low performing countries” (LPC). We also look at benchmark countries from the industrialized world. Germany and England have been chosen as two large developed countries representing a Continental-European and Anglo-Saxon background, respectively.<sup>6</sup> In addition, two further Western European countries that might constitute interesting benchmarks for the Latin American countries are Greece and Italy. The two countries are presumably the European countries participating in PIRLS that are most comparable to Argentina and Colombia. Many Argentines are of Italian origin, while Greece is perhaps closest among the European countries to the Latin American countries in terms of its level of economic development. Germany, Greece, Italy and England together form our group of “high performing countries” (HPC).

The specific target population of the PIRLS study was defined as the upper of the two grades with the highest share of nine-year-olds of a country at the time of testing. This led to a student population which was in the fourth grade in the countries considered in this paper, with the exception of England, where it was fifth grade. The mean age of the students in the

> 5 For detailed information on the study and the database, cf. Mullis et al. (2003), Martin et al. (2003), Gonzalez and Kennedy (2003), Campbell et al. (2001) and the PIRLS homepage at http://timss.bc.edu/pirls2001.html.

> 6 The United States could not be used for the analysis because two crucial student background variables, the number of books at home and parental education, were not administered in the US. This would result in a lack of comparability to the other countries, as well as to considerable bias in the estimation of the remaining student background and school effects in the US. Instead of the US, England is included in the analysis as a representative of the Anglo-American country group.

5
24 Winikoff, B. "The Effects of Blrth Spacing<sup>on Child and Maternal Health."</sup> Studies in Fmuil! Planning, 14 (10):<sup>231-245, 1983.</sup> World Bank. WldDevlopmen Revert 1984, Washington, D.C., 1984.

World Health OrganLzation. Prevention of<sup>Maternal Mortality. Report of an</sup> Interregional Meeting, Geneva, November 11-15, 1985.

Wray, _J.D._ "Population Pressure on Families:<sup>Family Size and Child Spacing."</sup> Renorts on Population/Familv Planning, No. 9, The Population Council,<sup>New</sup> York, 1971.
related analyses in the range of 0.8 and 0.9 (Abadzi, 2011; Piper, 2010). Abadzi (2011) compares various measures on EGRA and refers to the tight correlation between reading fluency and comprehension, consistent with the literature (Piper, 2010; Vagh, 2009; Gove, 2009; Kim et al., 2010). Our own analysis corroborates this. Across 81 country-year observations, we find a correlation of 0.86 (see Figure 1.0).

Figure 1.0: Correlation between Reading Comprehension and Oral Reading Fluency <!-- Start of picture text --> Pearson Correlation Coefficient: .8547<br>NIC<br>MKD<br>TJK IDNIDN<br>AFG KGZKGZ HND<br>IDNKGZKGZ PHL<br>LBR<br>MKD<br>JOR LAOTZARWA TJK PNG<br>GMB SDNRWATUVRWAMMRAFGJOR PNG<br>GMB VUTMMRBDIIRQBGDHTILBREGYEGYETH BDI BGDTONTLSAFGPNGLBRTONKENGUYKIR KEN<br>ZMBGHAMLIGHAMWINGASLEHTINGAGMBMLICODYEMLBRHTILBRCODMARNGASENBGD RWA<br>MWI UGA TLS<br>0 25 50 75<br>ORF score<br>100<br>75<br>50<br>Reading %<br>25<br>0<br><!-- End of picture text --> Despite the viability of both measures, the oral reading fluency sub-task does not have as sound a conceptual link to RSATs and ISATs as comprehension. Moreover, fluency and words per minute used in ORF scores are likely to be more susceptible to differences in implementation and language. Reading comprehension, on the other hand, is not as tightly timed and is less sensitive to language and implementation details. Moreover, it has the most plausibly direct and substantive proficiency link to RSATs and ISATs. For example, reading comprehension on EGRA maps directly onto one of the levels in Levels 1-8 on SACMEQ, the RSAT for East and Southern Africa. Analysis and work done by ACER for the UIS Reporting Scale – a global effort to compare underlying proficiencies across assessments – focuses on reading comprehension partially for this reason (Turner et al., 2018). Abadzi (2011) also cites reading comprehension as a likely proficiency link between EGRA and ISATs and RSATs. This is born out in the data. For example, when we use reading comprehension scores, Tanzania outperforms Kenya, consistent with SACMEQ. When using ORF scores, Kenya does not outperform Tanzania, which is no longer consistent with SACMEQ. Moreover, reading comprehension was the most available subtask across all EGRA datasets we collected.

To this end, we primarily analyze reading comprehension, which is available in nearly all EGRA datasets, is less sensitive to differences in context and implementation, and has a strong conceptual link to RSATs and ISATs. To ensure robustness to language effects, we only include data when students took the test in their language of instruction.

5