rafmacalaba/datause-encoder-data huggingface dataset:
and experience is necessary to better identify the determinants of successful financial education (e.g., Hastings et al. 2013).

Our study follows several earlier survey studies about financial education. Most of these studies have a narrative character, among them widely cited works such as Fox et al. (2005), Willis (2011), Hastings et al. (2013), and Lusardi and Mitchell (2014). This gives the authors some flexibility about selecting and interpreting the most relevant studies. A quantitative meta-analysis is more rigid in approach but has the advantages that transparent rules of procedure ensure replicable results and that quantitative relations can be derived. Overall, narrative surveys and meta-analyses complement each other.

We perform a meta-analysis because there are just two earlier systematic accounts of the financial education literature that leave much room for more research. The study by Miller et al. (2015) covers only 19 papers due to its extremely restrictive selection criteria, requiring interventions on _identical_ outcomes. This limits the sample sizes to about five studies and estimates per subsample, which does not allow investigating the sources of heterogeneity.

Thus, the most similar study to our work is Fernandes et al. (2014), which covers 90 effect sizes from financial education reported in 77 papers. Despite an overlap of 44 percent with their sample of studies, our research differs in four crucial ways, which explains our new results: (i) most important is that we analyze determinants of program effectiveness in a broader way by applying respective coding; (ii) we consider various outcomes per study (on average about four per study) and their respective effect sizes; moreover, (iii) we cover recent and mostly randomized experiments providing evidence of effective interventions; and (iv) we cover additional studies focusing exclusively on financial literacy as the outcome variable.

This paper is structured in seven further sections. Section 2 introduces our meta-analytic approach. Section 3 describes our data. Section 4 provides first results of the meta-analysis, while section 5 uses these results to explain heterogeneity of financial education treatment effects. Robustness tests are mentioned in section 6, and section 7 concludes with policy considerations and venues for future research.

# II.  META-ANALYTIC METHOD

Meta-analysis is a quantitative method to synthesize findings from multiple empirical studies on the same empirical research question. In a meta-analysis, the dependent variable is comprised of a summary statistics reported in the primary research reports, while the explanatory variables may include characteristics of the research design, the sample studied, or, in case of impact evaluations, the policy intervention itself (cf. Stanley 2001: 131). Meta-analyses can provide answers to two specific 4
above, and considered respondents living in the community for less than five years as migrants. After this selection process, their sample included 2,883 individuals aged 15 or above, only 6% of whom migrated from one Tanzanian district to another in the period between 2004 and 2008. Kubik and Maurel (2016), instead, in the absence of an explicit question on permanent migration in the data set, exploited the second wave of the survey collection (2010-2011) to identify migrants, by directly comparing the place of residence of all household members in the first and the second waves of the survey and identifying residential moves based on the GPS coordinates of the place of origin and destination. Following this strategy, they defined their outcome variable of interest as a migration dummy equal to one for households with at least one member who permanently moved out of the original village between 2008/09 and 2010/11, and found that 14 percent of households had at least one migrant between the two waves. Note also that Kubik and Maurel (2016), by design, observe internal migration only.

In the subsequent waves of the Tanzanian NPS collection, some changes were implemented. From Wave 2 (NPS 2010-2011) on, Module G on children living outside the household was dropped. In Wave 3, NPS 2012-2013, an amendment was added among the roster of possible replies to the following question in the _Shocks_ Section R: R.6: “ _What did your household do in response to this [SHOCK] to try to regain your former welfare level?_ ” Among the possible replies, there is the following choice: “ _Household member migrated_ ”. This option, however, was not present in the subsequent, and currently final, wave of the collection, NPS 2014-2015. The Tanzania example, chosen as its surveys were used by the studies reviewed above, is emblematic of the limitations and internal inconsistencies involving migration data in most surveys of the LSMS-ISA collection.

Prompted by the reply featuring migration as a coping strategy in the Tanzania surveys, we carried out a screening of all the questions and answers potentially related to the climate-migration nexus that are currently available in the entire LSMS-ISA collection. The detailed outcome of this screening is reported in Table A.1 in the Appendix. The migration-as-coping-strategy option is actually present in the shock questionnaires of many LSMS-ISA surveys in other countries. Interestingly, across the whole the LSMSISA project, the most explicit questionnaire reference to the climate-migration nexus can be found in the Uganda collection, in rounds 2008-2009, 2009-2010, 2010-2011, 2013-14, and 2015-2016 of the panel, where the following question appears: 29
post‐crisis period, possibly suggesting that young firms were forced to improve their learning capabilities in order to operate in a more competitive environment. Yet this improvement was not enough to compensate the worse sales growth performance of young enterprises after the crisis.

# **4 ­ Conclusions**

In this paper we examined the effects of the global downturn on innovative firms and on young firms in order to understand the growth prospects of Eastern European countries using a unique panel data for 1,686 firms in six countries (Bulgaria, Hungary, Latvia, Lithuania, Romania, and Turkey) covering manufacturing, retail and other service sectors. Two empirical methods were used. First, we applied a panel‐data analysis in order to assess the difference of sales growth performance between innovative and non‐ innovative firms, and between young and older firms, over the pre and post‐crisis periods, when controlling for certain firm characteristics. Second, we used the Juhn ‐Murphy‐Pierce technique to decompose the difference in sales growth performance between innovative (young) non‐innovative (older) companies, over time, into three effects: characteristics effect; returns effect and unexplained effect.

The results show that innovative firms have suffered more than non‐innovative companies, regardless the estimator applied (population‐averaged estimator or FGLS), and the criteria used to categorize innovation (introduction of a product or a process or development of R&D activities). We also showed that the decrease of sales growth rate of younger firms was more severe than of older companies.

When comparing innovative and non‐innovative companies, the decomposition technique showed three main outcomes. First, the positive difference in sales growth performance in favor of innovative companies has been reversed after the crisis. Second, while before the crisis that positive gap in favor of innovative companies was due to their better characteristics, especially regarding size, and, mainly, to higher returns obtained by innovative firms, after the crisis the gap reversion was due mostly to the deterioration of characteristics of innovative companies. Finally, the results showed that the residual (or unexplained) effect was always positive, before and after the crisis, suggesting that the 19
partner country, p; is the weighted average of its bilateral trade policy, Tn;c;p on all imported products, n; with their weights re‡ect the size of their imports, mn;c;p; as well as their respective bilateral import demand elasticity, "n;c;p : For the relevant bilateral import demand elasticity, "n;c;p; we will utilize the most recent estimates facing UK exports in these markets from Kee and Nicita (2017). These bilateral import demand elasticities are estimated from product level quantity-based gravity regressions, jointly with bilateral AVE of NTMs. The econometric model also takes into account the large presence of zero in the bilateral trade statistics, and at the same time uses instrumental variables to address the endogeneity issues of tari¤s and NTMs. Based on these estimates, we assess short-term trade impacts based on the potential changes in tari¤s and NTMs in these markets post-Brexit according to the ...rst di¤erence of Eq (2): where bilateral imports will be kept at the current, pre-Brexit level to isolate the trade policy impacts on trade ‡ows. The total impact of trade due to potential trade policy changes will be with mc;p and �"c;p measure the aggregate imports and import weighted trade elasticity of country c with respect to imports from partner p: We will use data on 2016 to calculate trade impact according to Eq (3) : 4
# **References**

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Babudieri, S., Dorrucci, M., Boschini, A., Carbonara, S., Longo, B., Monarca, R., Ortu, F., Congedo, P., Soddu, A., Maida, I.R., Caselli, F., Madeddu, G. & Rezza, G., 2011. Targeting Candidates for Directly Administered Highly Active Antiretroviral Therapy: Benefits Observed in HIV-infected Injecting Drug Users in Residential Drug-Rehabilitation Facilities. _AIDS Patient Care and STDs_ , 25(6): 359-364.

Berg, K.M., Litwin, A., Li, X., Heo, M. & Arnsten, J.H., 2011. Directly observed antiretroviral therapy improves adherence and viral load in drug users attending methadone maintenance clinics: A randomized controlled trial. _Drug and Alcohol Dependence_ , 113: 192-199.

- Bhargava A. (2005). AIDS epidemic and the psychological well-being and school participation of Ethiopian orphans. _Psychology, Health and Medicine_ , 10, 263-275.

Blacher, R.J., Muiruri, P., Njobvu, L., Mutsotso, W., Potter, D., Ong'ech, J., Mwai, P., Degroot, A., Zulu, I., Bolu, O., Stringer, J., Kiarie J. & Weidle, P.J., 2010. How late is too late? Timeliness to scheduled visits as an antiretroviral therapy adherence measure in Nairobi, Kenya and Lusaka, Zambia. _AIDS Care_ , 22:11: 1323-1331.

- Ford, N., Nachega, J.B., Engel, M.E. & Mills, E.J., 2009. Directly observed antiretroviral therapy: a systematic review and meta-analysis of randomised clinical trials. _Lancet_ , 374: 2064-2071.

Garland, W.H., Wohl, A.R., Valencia, R., Witt, M.D., Squires, K., Kovacs, A., Larsen, R., Potterat, N., Anthony, M.N., Hader, S. & Weidle, P.J., 2007. The acceptability of a directly-administered antiretroviral therapy (DAART) intervention among patients in public HIV clinics in Los Angeles, California. _Aids Care_ , 19(2): 159-167.

- Greenland, S., 2000. An introduction to instrumental variables for epidemiologists. _International Journal of Epidemiology_ , 29: 722-729.

13
Breaking the Waves? Education, Youth Bulges, and Armed Conflict youth. Hence, an important policy question is whether countries are best advised to expand educational opportunities gradually in order to avoid increased grievances and instability.

H4: The lower the educational progression ratios from primary to secondary, the greater the risk of armed conflict.

H5: Rapid expansions in educational attainment (secondary/tertiary) increase the risk of armed conflict.

# **3.0 Empirical model and data**

The analysis is based on a dataset combined from a number of sources and expanded through the calculation of additional derived variables. The dataset covers 120 countries for the 1970-2000 period, as well as four historical entities. The spatial-temporal domain is determined by the coverage of the education dataset used (Lutz et al., 2007; see Appendix A).

Data on the dependent variable, _domestic armed conflict onset,_ is drawn from the Uppsala/PRIO dataset (Gleditsch et al., 2002). The Uppsala/PRIO dataset sets a relatively low violence threshold for conflict, and distinguishes between minor armed conflict (a minimum of 25 battle-related deaths per year), and war (at least 1,000 battle-related deaths per year). Several conflicts in the dataset are inactive for periods before they restart, posing the issue of how to code recurring conflicts. The dependent variable used for this study requires at least two years of inactivity before a new onset is coded.<sup>2</sup> Similarly, conflicts beginning while another conflict in the country is already in progress are excluded. In other words, only transitions from a spell of peace to a state of conflict are analyzed. There are a total of 79 conflict onsets among the countries covered by the IIASA dataset during the 1970-2000 period.<sup>3</sup> We have used two measures for _young male bulges_ , the conventional measure of 15-24 year old males as share of all males aged 15 and above, and a measure that better captures the dynamics in the younger working-age segments measured as the ratio between 15-29 year-olds and 30-44 year-olds, the Relative Youth Cohort Size (RYCS). Both measures are constructed on the basis of the age structure data provided in the education dataset (Lutz et al., 2007), which is consistent with the UN Population Division’s _World Population Prospects: The 2004 Revision_ . As described above, the RYCS measure should be more sensitive to reduced employment opportunities due to increasing relative cohort size, while the conventional measure more generally captures the abundance of youth in the adult population. Both of these measures were centered on their means.

> 2 In a previous study (Urdal, 2006), alternative coding rules were applied by changing the requirement to one year and five year of inactivity, with only marginal impacts on the main results.

> 3 The corresponding number of conflict onsets for all countries for the same period is 125.

7
Notably, the intensification of mining activities has primarily occurred in the south of Ghana. Between 2003 and 2013, gold mines spread from a handful of districts in the Ashanti region to many districts in the Ashanti, Western, and Central regions in the south. Ghana’s oil and gas fields are also located off the south coast. Between 2003 and 2013, the increase in the number of mining firms and in mining employment in the south is larger than in the north by 14 and 62 times, respectively.

We test two predictions related to the positive mining shock in Ghana, by combining the literature on production networks (Acemoglu et al. 2012; Carvalho and Tahbaz-Salehi 2019) with that on mining-led local development (Feyrer et al. 2017; Toews and Vezina 2020). First, we examine whether the mining output shock that primarily occurred in the south of Ghana led to a growing regional (north-south) difference in intersectoral linkages between 2003 and 2013. Second, we examine whether regional differences in intersectoral linkages following the mining shock led to regional differences in the sectoral composition of regional output during the same period.

To estimate regional differences in intersectoral linkages and the composition of sectoral outputs, we use statistics at the sector level (output, productivity, and employment elasticities in other sectors with respect to mining) and at the firm level (rate of firm entry and average firm-level employment in heavy manufacturing industries). Region-level sectoral data come from aggregate and regional five-sector input-output (I-O) tables for 2004 and 2013 that we construct from supply and use tables for Ghana. The five sectors are agriculture, mining, “other industry” (that is, industrial subsectors other than mining), wholesale and retail trade (WRT) services, and “other services” (that is, services subsectors other than WRT services). For firm-level data, we use two rounds of censuses of firms for Ghana: the 2003 National Industrial Census (NIC) and the 2014 Integrated Business Establishment Survey (IBES).

We examine the spatial and temporal pattern of the mining shock by exploiting the variation in the intensity of mining activities between the north and the south and between 2003 and 2013. Differences in regional intersectoral linkages are measured using the I-O tables for the north and the south in 2004 and 2013. Between 2004 and 2013, mining’s output as a share of gross domestic product (GDP) or the Domar weight increased by 16 percentage points, and the increase in the Domar weight for mining was 1.7 times larger in the south than in the north. The sectoral total factor productivity (TFP) ratio between mining and “other industry” in the south (0.532) is more than twice the size of that in the north (0.204). We also find larger growth in the share of intermediate use in mining and the downstreamness index 3
_Figure 2: Change in total exports by country_ <!-- Start of picture text --> CHN<br>SAU<br>THA<br>QATKWT IRQ OMN IRN<br>TKM<br>MMR<br>UZB<br>YEM<br>KEN TZA<br>KGZ<br>MDV<br>0 5 10 15<br>Percentage change in Trade<br>East Asia & Pacific Europe & Central Asia Miidel East & North Africa<br>South Asia Sub-Saharan Africa<br>20<br>18<br>16<br>14<br>Log(Pre-BRI exports)<br>12<br>10<br><!-- End of picture text --> Note: Change in trade is calculated as the difference between pre‐BRI and post‐BRI trade for each country _Figure 3: Change in total exports by sector_ <!-- Start of picture text --> Mineral Products<br>Machinery / Electrical<br>Metals<br>Chemicals & Allied Industries<br>Plastics / Rubbers<br>Textiles<br>Transportation<br>Miscellaneous Stone / Glass Vegetable Products<br>Foodstuffs Wood & Wood Products<br>Animal & Animal Products<br>Footwear / Headgear<br>Raw Hides, Skins, Leather, & Furs<br>0 20 40 60 80<br>Percentage change in Trade<br>21<br>20<br>19<br>18<br>Log(Pre-BRI exports)<br>17<br>16<br><!-- End of picture text --> Note: Change in trade is calculated as the difference between pre‐BRI and post‐BRI trade for each sector 24
-93 .

macroeconomic linkages Is needed to ensure that all supply constraints are adequately considered In sector development. These factors Include not only forelgn and local funding availability but also management and professional staff capability for all aspects of system operatlon and development.

The Bank has Increasingly supported Investment planning studies. However, these have tended to focus on long term generatlon planning or the preparation of the next project. There Is a need to broaden the scope of the studies to Include subtransmission and distribution In detail as requirements for ongoing operatlons and maintenance, and manpower Jevelopment. If the sector plan reveals that there Is a constraint In a particular area which cannot be resolved from within the power sector, then the planning forecast should be adjusted to stay within the bounds of the constraint In m3ny countries, the primary constraint will be the percentage of total GDP that should be allocated to the sector.

Assuming a greater emphasis on Improving the efficiency of the utilitles, there Is a need to determine why some countries such as Thailand, Korea, Tunisia, and Malawi have successfully Increased their efficiency, while repeated projects In other countries have met with little success. It does appear that macroeconomic conditions have a strong bearing on the likelihood of success. If the government Is following uneconomic policies and the national administratlon Is generally weak and Inetficient, then It Is unlikely that the power utility will succeed In making significant Improvements In performance.

Given the lncreasing climate of uncertalnty which affects all aspects of planning and performance, It Is essential to go beyond the simple deterministic approach based on expected values, by evaluating different scenarios and paying greater attentlon to risk analysis. At the level of project preparation, this Includes risk analysis with regard to the effects of uncontrollable or uncertain fantors on:
preted as a more positive attitude. Both the game and video treatments have a positive and significant impact of 8.9 pp and 10.5 pp, respectively, on the assessment that circumstances beyond a Venezuelan migrant’s control relate most to poverty (vs. lack of effort), which amount to an increase of 11.2 and 13.2 percent, respectively. Although the point estimates are positive, the treatments did not significantly influence attitudes about equal job opportunities for Venezuelan migrants and natives or attitudes about the right to segregate migrants from neighborhoods. Finally, regarding policy attitudes about government support for and increased spending on Venezuelan migrants, both treatments had positive and significant effects. Thus, both interventions were effective in shifting attitudes towards increased public support for migrants. Moreover, both treatments improved the attitudes index by 0.33 sd.

Panel B of Table 4 presents the effects of each intervention duration in minutes. The estimates affirm the programs’ effects in enhancing attitudes across the same variables. Additionally, they indicate that the video was at least four times more effective in improving attitudes toward migrants per minute spent compared to the game. Furthermore, the effects between treatments per minute of duration are statistically distinguishable.

# **7 Treatment Effects on Beliefs about Forced Migrant Impacts**

We also conducted an exploratory analysis (not prespecified) on respondents’ beliefs about the impacts of migration on the economy and on society. It is important to note that the treatments do not provide explicit information on the effects of migration on crime or on the labor markets. Yet, to the extent that they reflect the realities of migrants who are hard-working, honest individuals, it is possible that treated participants may generalize this information to other dimensions of the migrant experience and thus change their beliefs. Nonetheless, as shown in Panel A of Table 5,<sup>21</sup> the interventions had no statistically significant impact on commonly held beliefs that Venezuelan migrants compete with natives for jobs or that they increase crime rates.

> 21 The raw data is illustrated in Figure D.3.

21