rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5:  Community radio access and household health and health policy knowledge**

|Dependent variables:|Knowledge of<br>gov‘t bed net<br>programs?|Know ORS treatment<br>for kids' diarrhea?|Conditional radio<br>effects on ORS<br>knowledge|Knowledge of<br>Benin‘s child<br>mortality rate|
|---|---|---|---|---|
|**Number of non-**<br>**commercial private**|**0.00654**<br>**(0.24)**|**0.00222**<br>**(0.87)**|**-0.046**<br>**(0.07)**|**0.0207**<br>**(0.01)**|
|**stations**|||||
|Gender of HH Respondent|-0.00822<br>(0.57)|0.0782<br>(0.01)|-0.0013<br>(0.98)|-0.0133<br>(0.56)|
|**Gender x Radio**|||**0.029**<br>**(0.07)**||
|Number of children|-0.0140|0.0222|0.010|0.00461|
||(0.00)|(0.00)|(0.24)|(0.26)|
|**Children x Radio**|||**0.005**<br>**(0.05)**||
|Number of children aged 0 –|0.0137|0.0246|0.024|0.00378|
|5|(0.01)|(0.02)|(0.02)|(0.65)|
|Elementary Education –|0.0391|0.135|0.134|-0.00364|
|Respondent|(0.00)|(0.00)|(0.00)|(0.81)|
|Secondary Education –|0.0324|0.244|0.243|0.0272|
|Respondent|(0.04)|(0.00)|(0.00)|(0.30)|
|Higher Education –|0.0744|0.342|0.338|0.0184|
|Respondent|(0.01)|(0.00)|(0.00)|(0.77)|
|Number of adults|-0.0109|0.0107|0.011|0.00289|
||(0.01)|(0.09)|(0.08)|(0.57)|
|Number aged over 60|-0.00607|-0.0929|-0.091|-0.0147|
||(0.67)|(0.00)|(0.00)|(0.42)|
|Owns a TV|-0.00889|0.0699|0.067|0.00198|
||(0.67)|(0.04)|(0.04)|(0.95)|
|Owns a mobile|-0.00455|0.0720|0.073|0.0631|
||(0.75)|(0.00)|(0.00)|(0.00)|
|Observations|3736|3736|3736|3693|
|Adj._R_<sup>_2_</sup>|0.04|0.18|0.18|0.05|

Note: Base specifications, p-values as in Table 4, plus control for health worker visit for malaria discussion. The third column also controls for the indicated interaction terms. Health worker visit and other coefficients not reported; among them, only the income and wealth variables are generally positively and significantly associated with knowledge of ORS.

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# **Figure 5: Use of formal account and loan services across firm size groups in international comparison**

<!-- Start of picture text -->
1.2<br>1<br>0.8<br>0.6 small<br>medium<br>0.4<br>large<br>0.2<br>0<br>SSA non-SSA SSA non-SSA<br>account loan<br><!-- End of picture text -->

Source: authors’ calculations based on Enterprise Surveys (www.enterprisesurveys.org) Enterprise survey data also allow an exploration of the reasons why enterprises do not have loans with formal financial institutions. Specifically, enterprises are asked for the reason that they did not apply for a loan with a formal financial institution over the past year. The share of enterprises that quote lack of demand is significantly lower in Africa (43%) than in other developing countries (62%), suggesting that lack of demand is a less important factor in Africa than elsewhere. High interest rates were also mentioned as a reason for not applying for loans (14% in Africa vs. 10% in other developing countries), which could indicate that the return on investment projects is too low. On the other hand, and as noted by many observers of African finance, the high cost of credit might impede the use of bank finance. As discussed above, interest rate spreads and thus lending rates are significantly higher in Africa than in non-African developing countries. Those high costs of credit can be explained not only by the lack of competition noted above, but also by monetary and socio-political instability resulting in high risk premia. The importance of monetary and socio-political stability can be appreciated when considering that the share of non-applicants due to high interest rates is especially high in DRC and Zimbabwe. Even more striking is the difference in the share of 19
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significantly across the six rounds."' Variables used in the multivariate analysis are obtained from child and household level information from the third round. However, there are exceptions. Per capita expenditures (used as proxy for permanent income) are based on an annualized flcw as obviously, a snapshot of the current expenditures in a particular round does not provide a complete picture of consumption pattarns. Total expenditures is chosen as proxy for incomes as it is well known that incomes fluctuate more than expenditures particularly in an agricultural environment. A measure of predicted income is also a possible proxy for observed income, but that measure is also difficult to obtain when shocks are correlated across observations as is common in most agricultural communities.

More than half of the under six children in the sample areas are stunted (or chronically malnourished) as defined by the WHO reference standards for height-for-age. This is given in Table 1 with detailed prevalence rates for each district. Stunting is an indication of longer term problems related to cumulative undernourishment affecting the child's structural development. The sample average is higher than the stunting rate of 41.6 percent obtained by the Demographic and Health Survey of 1990 (DHS-IRD and Government of Pakistan). That survey, however, reports levels of nutrition for the entire country while the sample for the present study is based on rural districts with limited _1_ Moreover, as expected, Z-scores are highly correlated between - rounds. Unless one is modelling growth a very different exercise - than modelling status little is gained by using all rounds of anthropometric data.
2018). In particular, we use the LASSO procedure (Tbishirani, 1995) to choose, among all District characteristics and interviewees’ selection traits, which variables are most predictive of DLT scores. LASSO is considered to be particularly suited, among other machine learning techniques, when estimates for several of the potential explanatory variables are believed be approximately zero (Abadie and Kasy, 2017). In practice, the LASSO minimizes an objective function that includes a goodness-of-fit term (sum of squared errors) and a penalization for the sum of covariates’ coefficients. The weight of the penalization, the lambda parameter, is chosen by cross-validation, i.e. by estimating the model in test subsamples and evaluating its performance in the remainder of the sample (Athey, 2018).

Following Best, Hjort and Szakonyi (2017), we proceed in two steps. We first estimate a LASSO model<sup>30</sup> with District-level health score as the dependent variable and a set of 40 covariates. We then select the variables with non-zero coefficient in the LASSO model and report the results of both bivariate and multivariate regressions.

The 40 variables used in the LASSO model include the average for 12 selection traits already explored, separately for bureaucrats and politicians: PSM, Integrity, Big Five, NonCognitive, Altruism, Indicator for mistake in risk game, Cognitive, Risk Aversion, Assets and indicators for 3 education levels. We also include regional dummies and the following socio-economic and political District level characteristics: vote share of the presidential candidate of the NRM, Number of radio stations, Poverty rates, log Population, Share of urban population, Herfindahl index for Councillors vote share and the UCE score as a measure of education in the district population (lower values are asdociated with better education outcomes).

The LASSO model selects eight variables with non-zero coefficients: four socio-economicpolitical characteristics of Districts (Vote share of the presidential candidate of the NRM, Poverty Rate, Number of Radios and UCE score), three bureaucrats’ traits (Altruism, Riskaversion and indicator of less than college education) and politicians’ Integrity. Figure 9a reports the point estimates and 95 percent confidence intervals for bivariate regressions

> 30Specifically, we use Friedman, Hastie, and Tibshirani (2010) algorithm implemented in the R-package -glmnet-. It runs a LASSO regression with a lambda parameter that minimizes the 10-fold mean crossvalidation error.

21
_Table 12: Balance table for socioemotional skills treatment_

|Variable|(1)<br>Contro|(2)<br>Treatmen|(3)<br>Differenc|
|---|---|---|---|
||l|t|e|
|Ankara|0.179|0.179|0.000|
||(0.384)<br>|(0.384)<br>|(0.007)<br>|
|Istanbul Asia|0.311|0.311|0.000|
||(0.463)|(0.463)|(0.009)|
|Istanbul EU|0.509|0.509|0.000|
||(0.500)|(0.500)|(0.010)|
|Experience (months)|49.234|49.109|-0.125|
||(5.563)|(5.673)|(0.108)|
|Age|26.411|26.399|-0.012|
||(0.643)|(0.650)|(0.012)|
|Accounting|0.292|0.292|-0.000|
||(0.455)|(0.455)|(0.009)|
|Marketing|0.276|0.276|0.000|
||(0.447)|(0.447)|(0.009)|
|Sales|0.304|0.304|-0.000|
||(0.460)|(0.460)|(0.009)|
|IT|0.128|0.128|-0.000|
||(0.334)|(0.334)|(0.006)|
|Worked in one firm only|0.253|0.264|0.011|
||(0.435)|(0.441)|(0.008)|
|Objective beauty|0.943|0.945|0.002|
||(0.231)|(0.227)|(0.004)|
|Subjective beauty|0.506|0.493|-0.012|
||(0.500)|(0.500)|(0.010)|
|Reading|4.582|4.573|-0.009|
||(0.493)|(0.495)|(0.010)|
|Speaking|4.517|4.526|0.009|
||(0.500)|(0.499)|(0.010)|
|Writing|4.514|4.512|-0.001|
||(0.500)|(0.500)|(0.010)|
|Total application size (100)|5.491|5.491|0.000|
||(9.110)|(9.110)|(0.176)|
|Observations|5,374|5,374|10,748|

**Notes** : Standard errors are given in parentheses. Symbols ***, **, and * indicate significance at the 1%, 5% and 10% level, respectively.
technologies in expanding productivity. Van Ark, O’Mahony, and Timmer (2008) show that the productivity slowdown in Europe during the 1990s and 2000s is attributable to the lower contribution of ICT to growth, the smaller share of technology-producing industries, and slower advances in technology and innovation as compared to the U.S. Not only the development of new technologies but also the adoption of existing ones play a substantial role in enhancing productivity and income growth. Comin and Hobijn (2010) and Comin and Mestieri (2018), using data on the diffusion of more than 15 technologies across a large number of countries over the last two centuries, show that varying patterns of the adoption and diffusion of technologies since 1820 account for at least 25 percent of the income divergence across countries and 75 percent of the income difference between rich and poor countries.

# **Education**

Education, as the knowledge and skills of the population, is essential to generate new technologies, as well as to disseminate, adapt, and implement them throughout the economy. Education allows workers not only to produce more and better, but also to expand and disseminate the technological frontier. For education to contribute to productivity, it must consist of strong basic foundations and sufficient specialization, rich in both quantity and quality, and spread throughout the population (Barro 2001; Hanushek and Woessmann 2015).

Studies suggest that indicators such as the number of schooling years and the completion rate of secondary and tertiary education of the population are associated with output growth through both TFP improvements and the direct contribution of human capital (Benhabib and Spiegel 1994; Griffith, Redding, and Reenen 2004; Bronzini and Piselli 2009; Erosa, Koreshkova, and Restuccia 2010). Having a sufficiently high level of education increases productivity growth in developing countries by enabling them to adopt new technologies from frontier countries. Benhabib and Spiegel (2005), for example, show that a country’s average years of schooling (as a proxy for education) has a positive impact on TFP growth through technology catch-up. Miller and Upadhyay (2000) show that education (also using the years of schooling as a proxy) can affect how developing countries adopt new technologies through trade, with a positive impact on TFP. Barro (2001) shows in a study of around 100 countries that the quantity and quality of education, using the years of schooling and student test scores as respective 7
Table 5A: Robustness checks (Coefficient on IBRD country)

|A<br>Base|(1)<br><br>proje|(2)<br><br>ct(y/dx)|(3)<br><br>AnyHDpro|(4)<br><br>ect(dy/dx)_|
|---|---|---|---|---|
|specification|-0.187**|<br>-0.196***|<br>-0.249%*«|<br>= -0.244***|
|(allcountries,1988-2016)|(0.076)|(0.073)|(0.047)|(0.053)|
|<br>Longer time frame|-0.160%*|= -0.124***«|-0.140"|= -0.150***|
|(all countries, 1961-2016)|(0.037)|(0.040)|(0.028)|(0.035)|
|Exclude reversegraduates<br>|-0.174**<br>|-0.181**<br>|-0.245«<br>|——-0.235***<br>|
|(1988-2016, 5 excluded)|(0.085)|(0.082)|(0.045)|(0.050)|
|Exclude small and large countries|-0.202**|-0.210**|-0.236***|-0.220***|
|(1988-2016, 11 excluded)|(0.094)|(0.092)|(0.051)|(0.057)|
|Additional control variables|-0.137|-0.144*|-0.263***|-0.272***|
|(all countries, 1988-2016)|(0.088)|(0.085)|(0.064)|(0.067)|
|Controls|no|yes|no|yes|
|Country fixed effects|yes|yes|yes|yes|
|Fiscalyearfixedeffects|yes|ves|yes|yes|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Control vaniables in the even numbered columns are (a) log of GNI per capita, (b) log of population, and (c) Net aid flow. The rows with additional countrol variables include (a) Polity Index (-10 to 10), (6) Number of years left in current term of the executive, (3) If executive has finite terms in office, and (4) Indicators for left and nght leaning executive. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, **TM* Significant at 1%.
# **Section 4: Activities**

Section 4 of the 2011–12 survey, which includes six main questions, gathers information on the regulations regarding the activities in which banks can engage, such as securities, insurance, and real estate, as well as on the extent to which banks can own voting shares in non- financial firms and the type of financial conglomerates allowed. Table A4 summarizes selected responses.

Overall, but especially among advanced economies, the share of countries allowing for a full range of securities and insurance activities (either conducted directly or through subsidiaries, in the case of EMDEs) increased between 2005 and 2010. While the share of countries allowing banks to engage in real estate activities directly or through subsidiaries increased from 12 to 16 percent (especially, among EMDEs, from 6 to 13 percent) a much higher percentage of countries restricted such activities all together. In particular, while 40 percent of jurisdictions reported that their banks cannot engage in real estate related activities, only 17 percent and 7 percent expressed such restrictions for the case of insurance and securities activities, respectively.

The percentage of jurisdictions allowing for banks to own 100 percent of equity in any nonfinancial firm went up from 7 percent in 2005 to 24 percent in 2010, highlighting the growing influence of banks in various non-financial sectors of the economy. The increase was particularly notable among EMDEs.

Finally, regarding the type of financial conglomerates that are allowed, we find that the most common form for EMDEs involves those cases where the bank is the parent (64 percent). On the other hand, among advanced economies, 59 percent allow conglomerates whose parent was a non-financial institution.<sup>9</sup>

> 9 If we limit our analysis to only those countries that have responded to both surveys (i.e., the “matched” sample), the resulting trends highlighted in this section are similar with only slight changes in the magnitudes of these trends.

30
no impacts on boys’ schooling. Yet, another hypothesis to help explain this result can be put forward if we are to make additional assumptions. Since boys were found to have enrolled in school at a much higher rate than girls before 1945 (Section II.1), the ME policy may have had weaker impacts on boys. Furthermore, analysis of a smaller survey that covers three provinces, Ha Nam, Nam Dinh, and Ninh Binh, in North Vietnam suggests that a far larger share of men than women were enrolled in the army during the Second Indochina war (or the Vietnam war), when those born during 1924-1955 reached adulthood. The proportion of men who ever served in the army was estimated to range approximately between 35 and 75 percent, depending on the age cohorts, while the corresponding figure for women was just 4 percent (Teerawichitchainan, 2009).<sup>35</sup> Notably, the same study also finds that enlisted men were predominantly _more_ educated: young men with up to five years of schooling were respectively 44 percent and 19 percent less likely to be inducted than those who had between six and nine years of education and 10 years of education or more. As such, the (much) larger war mortality for (educated) men (Hirsch et al., 1995) may have further reduced any potentially positive impacts of the ME policies. Indeed, analyzing the same data, another study offers further corroborative evidence that during the same war, the death rate for sons of fathers with six or more years of education was 68 percent higher than that for sons of fathers with no education or a primary school education (Merli, 2000).<sup>36</sup> Interestingly enough, while the specific context of the mass education program implemented in Vietnam half a century ago is unique, similar movements—albeit at a different level—also

> 35 In general, women’s participation in armed activities are much more likely to be informal than that of men (see, e.g., Turner-Gottschang and Phan, 1998; Taylor, 1999). The larger male war mortality rate also concurs with evidence for other countries (Obermeyer _et al._ , 2008).

> 36 Another related hypothesis is that since more than two-thirds of men were estimated to be enlisted when younger than 20 years old during the Vietnam war (Teerawichitchainan, 2009), this may also have disrupted men’s education and thus reduced their education attainment. However, we have no data on veterans who were encouraged to go back to school after the Vietnam war. Studies for the US have suggested that preferential government policies on this population group can significantly increase their college education (see, e.g., Bound and Turner, 2002; Stanley, 2003).

35
# **Table A9:  European Bank for Reconstruction and Development Transition Report (EBR)**

<!-- Start of picture text --> Data Provider European Bank for Reconstruction and Development<br>Description Multilateral development bank based in London, United Kingdom<br>Website www.ebrd.org<br>Data Source Transition Report<br>Type Expert Assessment<br>Respondents EBRD staff<br>Frequency Annual since 1996<br>Coverage Transition economies<br>Public Access Yes<br>Description The Transition Report includes scores on a 5-point scale for eight Transition Indicators<br>measuring progress towards market economy status. Scores are based on a checklist of<br>underlying criteria and reflect the views of EBRD staff.<br>2006 2005 2004 2003 2002 2000 1998 1996<br>Voice and Accountability<br>NA .. .. .. .. .. .. .. ..<br>Political Stability<br>NA .. .. .. .. .. .. .. ..<br>Government Effectiveness<br>NA .. .. .. .. .. .. .. ..<br>Regulatory Quality<br>Price liberalisation X X X X X X X X<br>Trade & foreign exchange system X X X X X X X X<br>Competition policy X X X X X X X X<br>Rule of Law<br>NA .. .. .. .. .. .. .. ..<br>Control of Corruption<br>NA .. .. .. .. .. .. .. ..<br>Country coverage 29 27 27 27 27 26 26 27<br>Year of Publication 2006 2005 2004 2003 2002 2000 1998 1996<br><!-- End of picture text --> 47
though somewhat lower than that of the Turkish at 30 - 50 percent. Child labor is also quite prevalent, though there have been extensive efforts made to ensure that refugee children attend school.<sup>19</sup> Publicly available information on refugees comes from an AFAD survey of 2,700 households in June and July 2013. Figure 2, using data from AFAD (2013), provides an overview of the Syrian governorates from which the refugees to Turkey originated. The refugees primarily come from northwest Syria. The largest source regions are Aleppo (36 percent), Idleb (21 percent) al-Raqqah (11 percent), Lattakia (9 percent), and Hamah (8 percent). Consistent with travel distance being a good predictor of refugee flows to Turkey, 80 percent of respondents report that they chose to flee to Turkey, instead of another country, due to the ease of transportation. The refugees in Turkey, unlike the later 2015 refugee flows to Western Europe, are nearly 50 percent female. Slightly over 50 percent are minors (under the age of 18). These facts reflect that to large extent Syrian families fled to Turkey together. Educational attainment is lower than for the Turkish (though not compared to Turkish border regions), with around 15 percent illiterate and about 20 percent having completed high school or obtained a higher degree. Reported mean and median monthly household earnings were, respectively, 447 and 300 Turkish Lira.

# **3. EMPIRICAL STRATEGY**

# **3.1 Estimating Equations**

To estimate the impact of Syrian refugees in Turkey on outcome _Y_ for individual _i_ in year _t_ and subregion _r_ we consider the following estimating equation: where the main variable of interest is the number of Syrian refugees _R,_ normalized by the working-age population, of a subregion. Further, we include subregion �� and year �� fixed effects, flexible individual level controls _X_ , and a time-varying control for the distance, D, of each subregion to the Syrian-Turkish border. The inclusion of subregion fixed effects means

> 19 According to AFAD (2013) while about 83 percent of the children 6-11 years old in the camps attended a school, only about 14 percent of the children 6-11 years old out of the camps attended a school. Since then, by all accounts, school attendance rates outside have camps have improved substantially.

12