rafmacalaba/datause-encoder-data huggingface dataset:
As a fourth and last type, we identify _informational and behavioral nudges_ , such as information fairs at the workplace and informational brochures (seven percent of estimates). These studies typically evaluate behavioral change in response to these low-intensity treatments. There is one study in our sample that studies the effect of a behavioral nudge in the form of “financial edutainment” in massmedia (cf. Berg and Zia 2013). This is an intervention designed to impact financial behaviors through a non-cognitive channel (as opposed to increasing financial knowledge), and the included study evaluates the impact of financial messages inserted into episodes of a popular television series in South Africa.

# IV.  RESULTS FROM META-ANALYSIS

We report the mean effects for all studies (section 4.1) and then for subsamples: financial literacy and financial behavior (section 4.2), types of financial education programs (section 4.3), research designs (section 4.4), and different country groups (section 4.5).

# _Summary Effects of Financial Education_

Here we discuss the average effects of financial education on financial literacy and financial behavior. Based thereon, we study the relation between these two outcomes. As a starting point, we note that the summary effect of financial education on all kinds of reported outcomes is estimated to be _g_ = 0.148 ( _p_ = .000, _n_ = 539). However, heterogeneity in effect sizes is high, indicating that outcomes could be disaggregated for meaningful analyses.

_Financial behavior._ We find that the average impact of educational interventions on financial behaviors is statistically highly significant ( _g_ = 0.086) (see table S1.3 in the supplemental appendix S1). The main reason that we get a more favorable result than Fernandes et al. (2014) is that we profit from a moderate, positive time trend (more details in supplemental appendix S2). To compare the magnitude of this effect size to results from health promotion on behavioral change (e.g., weight loss and nutrition in obesity studies), Portnoy et al. <u>(2008) report in their meta-analysis of 75 RCTs an</u> average effect size of about 0.1.

_Financial literac_ y **.** The average impact of financial education on financial literacy is substantially higher ( _g_ = 0.263, _p_ = .000, _n_ = 190) than the one on financial behavior (see figure S1.2 and table S1.3 in the supplemental appendix S1). Moreover, financial education explains 1.7 percent of the variance in financial knowledge and, thus, appears to be only slightly less effective than educational interventions in other domains such as math and science instruction (cf. Fernandes et al. 2014: 1867). To put this effect size in perspective: the meta-analysis of 225 studies by Freeman et al. (2014) reports an average effect size of around 0.47 for studies evaluating student performance in response to 9
Latinoamericano de Evaluacion de la Calidad de la Educación (LLECE) and the Segundo Estudio Regional Comparativo y Explicativo (SERCE), which test students in third, fourth and sixth grades in a set of Latin American and Caribbean countries. Two tests with a focus on Africa include the South and Eastern African Consortium for Monitoring of Educational Quality (SACMEQ) and the Programme d’Analyse des Systemes Educatifs de la Confemen (PASEC). Specifically, SACMEQ conducted two surveys for South and Eastern African countries for third and fourth grade, and PASEC carried out two waves of testing for second and fifth graders in Francophone Africa.

We utilize all of these tests by making the achievement scores comparable. To this end, we link the results of regional tests – LLECE, SERCE, PASEC and SACMEQ – to international tests such as PISA, TIMMS and PIRLS. As mentioned earlier, in order to normalize achievement test results across tests and time we mainly build on the previous work of Altinok and Murseli (2007) and their attempt to build an international database on human capital quality. In particular we extend their results from 2003 until 2010. In addition we refer to Hanushek and Kimko (2000) who tried to construct a similarly comparable database across tests, countries and time.

Our approach first builds on Hanushek and Kimko (2000). We exploit the availability of a United States test score in all international achievement surveys conducted since the early 1960s. Therefore, we can express each country’s performance in relation to the US for a given test in a given year. Thus, US tests scores are a reference point, making country achievement comparable across tests. Furthermore, the national testing regime of the US allows for a comparison of test results over time: The almost biannually conducted National Assessment of Educational Progress (NAEP) yields comparable results of US-student achievement (in different subjects and grades) over time. Connecting these results to the most adjacent US score in the international achievement tests delivers comparable US results over time. This adjusted score can then be related to the results of all countries that participated in international achievement tests.

While this is a valid methodology, such an approach has limitations. One particular limitation is that this approach ignores all surveys without United States test score availability, including those regional tests mentioned above. To deal with this, Altinok and Murseli (2007) use a new approach that exploits the appearance of a few countries in both international and regional 7"
Here **_Xivb_** is a vector of individual, household, and village-level control and stratification variables (municipalities) measured at baseline (denoted _b_ in the subscript). These controls, described further in Appendix 3, include the interaction of the treatment variable _Tv_ with the vector _(_ **_Xivb_** _- μ(_ **_Xvb_** _))_ to address concerns of regression adjustment laid out in Freedman (2008a & 2008b) and discussed in Lin (2013). The vector _μ(_ **_Xvb_** _)_ contains the averages of the control variables across both treatment and control groups. When a control variable is missing for individual _i_ , we set both the relevant element of the vector **_Xivb_** and the element of the vector _Tv *(_ **_Xivb_** _- μ(_ **_Xvb_** _))_ equal to -1. We let the elements of the vector **_Xivb Missing_** take the value 1 if the relevant control variable is missing and 0 otherwise. Our sensitivity tests also reconsider the following choices made above: (1) the use of OLS instead of binary models like Probit or Logit, (2) the inclusion of 43 children who were neither children nor grandchildren of the household head, which may lead to concerns around endogenous changes in household composition, and (3) using 12-month recall instead of 7-day recall for work outcomes.

Appendix Table 3 shows the effects on school enrollment and attendance, while Appendix Tables 4 and 5 present the effects on child work and the transition from idleness to joint schooling and work respectively. As the first row of each of these tables shows, point estimates do not change in magnitude or sign but more likely to be statistically significant when we include covariates. Impact on any work in the 12 months before the interview, for instance, is statistically significant when we include controls. Appendix Tables 3, 4 and 5 further illustrate that our results are robust to the use of binary response models instead of OLS, as well as child-level fixed effects using the 2007, 2008, and 2009 recall data as our baseline measurement. While some standard errors are marginally larger, all results are robust in magnitude and precision to the exclusion of children who are neither the biological child nor grandchild of the household head. Finally, Panel B of Appendix Table 4 confirms that the estimated increase in work is broadly robust to 7-day recall instead of a 12-month recall. For this alternative reference period, participation in work, unskilled work, and work for pay outside the 16
(Janzen and Michler, 2021). The use of a pre-analysis plan allowed us to pre-define the sources of data for inclusion in the study, what metrics would be tested using what functional forms, and how we would compare results across models in the absence of formal statistical tests. Second, we adopted a blinding strategy to help ensure objectivity in the implementation of the pre-analysis plan. As such, the authors were divided into two groups: the Data Generating Group and the Data Analysis Group. Authors Kilic and Murray were in the Data Generating Group and had full responsibility for extracting the EO data and matching it to the household records in the household survey data to create a number of different paired EO-survey datasets. In these datasets, the source of the EO data was anonymized prior to sharing with the Data Analysis Group. Authors Josephson and Michler made up the Data Analysis Group and had full responsibility for cleaning the agricultural productivity data, running the regressions, and conducting and writing the analysis. The pre-specified analysis was carried out on the blinded datasets and these results were posted to arXiv.org prior to unblinding (Michler et al., 2021a). The generation of datasets in this manner preserves the objectivity of any findings regarding differences in outcomes between different EO products.

The following analysis, and the associated results, were all pre-specified. If methods, approaches, or inference criteria differ from our plan, we highlight these differences. Results arising from these deviations in our plan should be interpreted as exploratory.

# **4.1 Estimation**

We follow the basic model of Deschˆene and Greenstone (2007): where _Yht_ is our outcome variables from the LSMS-ISA-supported household surveys, described above, for household _h_ in year _t_ , log transformed using the inverse hyperbolic sine. We control for year fixed-effects ( _γt_ ) and include household fixed-effects ( _αh_ ) in some specifications. The function _fj_ ( _Wjht_ ) represents our weather variables of interest where _j_ represents a particular measurement of weather. Finally, _uht_ is an idiosyncratic error term clustered at the household-level.

15
would match that of new household formations<sup>32</sup> . In addition, lower and less volatile inflation and interest rates have permitted the development of longer-term, fixed-rate products, which has improved mortgage affordability.

Supply-side factors, supported by financial system reforms, have also contributed to increased mortgage financing over the last few years. Improvements in Infonavit’s management, operations and corporate governance have significantly reduced NPLs and improved loan collection, thereby allowing it to fund more mortgages. The existence of SHF funding (which will be phased out by 2009) and the ‘opening up’ of domestic capital markets have also allowed Sofoles to increase mortgage lending. In addition, reforms in the credit infrastructure – particularly in the operation of the credit bureau and in the secured lending framework (2003 Security Interest Package reform) – have allowed banks to better measure and manage their risks, thereby permitting them to resume mortgage lending. Increased competition has also led to lower rates and product innovation, such as the appearance of mortgages with progressively lower interest rates for borrowers who continue to repay their obligations in a prompt manner (see next section for more details on the improved accessibility and affordability of mortgage financing). However, as mentioned previously, inefficient and costly state-level enforcement procedures and public registries of property raise both the overall level and variability by state in cost and risk of mortgage lending (see section IV) – in fact, the absence of a secondary mortgage market can be mainly attributed to the costs of switching between mortgage providers<sup>33</sup> .

# **_Consumer Financing_**

Consumer lending has grown four-fold as a proportion of GDP since 2000, albeit from a very small base. Commercial banks have increasingly dominated this market, accounting for almost 80 percent of outstanding loans as of end-2005. Bank-originated consumer credit is mostly provided via credit cards, which comprised 56 percent of their consumer loan portfolios as of September 2005. By contrast, Sofoles mostly finance the purchase of consumer durables, especially automobiles. The contribution of SAPS in overall consumer credit volume remains relatively small, but they are important in terms of access to lower income households.

> 32 See BBVA Bancomer (July 2005) for a recent analysis of the housing market.

> 33 Depending on the nature of the security interest to be created, the relevant agreement may require registration either with the public registry of commerce of the corporate domicile of the pledgor (i.e. for pledges without transfer of possession and for mercantile pledges) or with the public registry of property where the real estate is located (i.e. for mortgages).

25
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Blattman, C., Fiala, N., & Martinez, S. (2020). The Long-Term Impacts of Grants on Poverty: Nine-Year Evidence from Uganda's Youth Opportunities Program. American Economic Review: Insights, 2(3), 287304.

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<mark>Bossuroy, T., Goldstein, M., Karimou, B., Karlan, D., Kazianga, H., Parienté, W., ... & Wright, K. A. (2022). Tackling psychosocial and capital constraints to alleviate poverty.</mark> _<mark>Nature</mark>_ <mark>,</mark> _<mark>605</mark>_ <mark>(7909), 291-297.</mark> Buehren, N., Chakravarty, S., Goldstein, M., Slavchevska, V., & Sulaiman, M. (2017). Adolescent Girls’ Empowerment in Conflict-Affected Settings: Experimental Evidence from South Sudan. _Working Paper_ .

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at least one child aged 6 to 15 at the time of the baseline survey. A baseline survey was conducted between June and August 2003 and a follow-up survey was carried out between January and March 2005.

The sample for the experiment consists of households with a poverty index between the 13th percentile and the 28th percentile. One-half of the households in this sample were randomly assigned to the treatment group that was eligible for the cash transfer, and the other half was assigned to the control group that was not eligible for the transfers during the period of the evaluation. These two groups are the lottery winners and the lottery losers respectively.

To exploit the discontinuity in eligibility around the program’s eligibility threshold of the second quintile of the poverty index, families with a poverty index between the 33rd percentile and the 47th percentile were sampled. In that design families with their value of the poverty index between the 33rd percentile and the threshold (40<sup>th</sup> percentile) are just eligible for receipt of the cash transfer. Families with a value on the poverty index between the threshold and the 47th percentile are just ineligible for receipt of the transfer.

The survey includes one record for each household member including their gender, age and relation with the head of the household. The survey also contains information on parents’ level of schooling, marital status, and language spoken by all household members. For children aged between 5 to 17 years, the survey includes information on enrollment during the current school year (level and grade). Finally, the survey includes a complete module of household expenditures, which replicates the structure of the 1999 Ecuador LSMS.

Attrition is low; 96% of the households interviewed at the baseline were interviewed again in the follow-up survey. No significant differences are found between households who were and were not interviewed. Attrition can introduce biases when correlated with treatment status (Angrist, 1997). A regression of an attrition indicator on treatment status has a coefficient of 0.0012 (s.e. 0.11), suggesting that attrition will not bias our results.

The sample is restricted to children aged 5 to 17 years when they live in households that responded to the follow-up survey. This results in a sample of 3,004 children in 1,309 families in the experiment, and of 2,384 children in 1,221 households in the RDD study.<sup>9</sup> Table 1 presents descriptive statistics for eligible and ineligible children/households in both groups. Columns 1-3 pertain to the RDD sample of children/households who have a poverty index just below or just above the program’s threshold. Columns 4-6 are for the experimental sample around the first quintile of the poverty index.

> 9 Data on all key variables are available for all households in the sample, with the exception of parental education, which is missing in some cases.

6
# **Table 16a.  Impact of 20 Percent CO2 Reduction through Emissions Intensity Standards on Output by Sector and Regional Market. (Results are Percentage Change from Base Year)**

|Good|**`Central`**|**`Siberia`**|**`South`**|**`North`**|**`Urals`**|**`Far East`**|**`Moscow`**|**`St. Pete.`**|**`Tumen`**|**`Northwest`**|
|---|---|---|---|---|---|---|---|---|---|---|
|**ELE**|6.3|4.8|5.5|4.9|5.3|4.9|5.3|5.6|-0.1|7.2|
|**`NFM`**|0.9|0.6|-1.7|0.7|-0.7|2.6|4.2|4.4||11.0|
|**`SCI`**|0.1|0.0|-0.1|0.1|-0.2|0.3|0.3|0.2|-1.7|0.4|
|**`FIN`**|-0.1|-0.1|-0.2|-0.2|-0.1|-0.2|-0.2|-0.2|-0.7|-0.1|
|**HEA**|-0.3|-0.4|-0.5|-0.4|-0.4|-0.5|-0.5|-0.4|-0.8|-0.4|
|**`CON`**|-0.5|-0.1|-0.8|-0.1|-0.7|0.5|-0.3|-0.3|-3.2|-0.3|
|**`CRU`**||-0.5|-0.8|-0.6|-0.7|-0.4|||-1.2|-1.0|
|**`CNM`**|-0.4|-0.4|-0.8|-0.4|-0.8|0.1|-0.2|0.0|-5.5|-0.2|
|**`TMS`**|-1.1|-0.9|-1.1|-0.9|-1.0|-0.9|-1.1|-1.0|-1.4|-1.2|
|**`CHM`**|0.3|0.7|-1.0|-0.6|-1.1|0.9|0.7|1.3|-15.7|2.5|
|**HOU**|-0.7|-0.9|-1.0|-1.2|-0.9|-1.0|-0.9|-0.8|-3.7|-0.9|
|**`MWO`**|-1.0|-2.1|-2.1|-0.8|-1.9|0.0|-0.7|-0.7|-5.6|-0.4|
|**`AGR`**|-0.7|-1.3|-1.7|-1.2|-1.5|-0.7|-0.5|-0.6|-7.3|-0.2|
|**`OTI`**|-0.9|-1.1|-1.4|-1.1|-1.3|-0.3|-0.8|-0.8|-7.7|-1.1|
|**PST**|-1.8|-1.6|-1.6|-1.6|-1.6|-1.5|-1.5|-1.7|-2.0|-2.0|
|**`TRO`**|-1.6|-1.5|-1.7|-1.7|-1.5|-1.7|-1.7|-1.6|-2.2|-1.7|
|**`RLW`**|-1.9|-1.4|-2.0|-1.8|-1.9|-1.4|-1.5|-1.5|-2.2|-2.2|
|**`FOO`**|-1.0|-1.4|-1.9|-1.7|-1.4|-1.1|-0.9|-1.0|-7.7|-1.1|
|**`OIL`**|-1.1|-0.5|-3.0|-1.3|-0.7|-0.2|-2.9|-1.7|-7.2||
|**`OTH`**|-2.4|-1.1|-2.7|-0.6|-2.4|-0.5|-2.3|-2.1|-5.8|-1.9|
|**TRD**|-3.5|-2.2|-2.0|-1.8|-2.1|-2.3|-2.7|-2.8|0.5|-3.7|
|**CLI**|-1.9|-1.9|-2.3|-2.2|-2.1|-2.0|-1.7|-2.0|-6.8|-1.9|
|**`TPP`**|-1.5|-3.8|-2.4|-2.7|-2.0|-6.9|-1.4|-2.1|-5.2|-2.0|
|**`TRK`**|-3.5|-3.0|-2.9|-3.0|-3.0|-3.3|-3.5|-3.4|-1.2|-3.8|
|**`FME`**|-4.3|-3.5|-6.6|-3.6|-4.9|-1.1|-4.1|-10.4||-3.5|
|**`MAR`**|-5.8|-4.4|-6.7|-6.5|-5.0|-5.6|-5.5|-6.4|-8.2|-7.1|
|**`COL`**|-7.0|-6.3|-7.2|-6.5|-7.3|-6.2|||||
|**`PIP`**|-12.7|-9.3|-8.2|-7.8|-7.3|-7.7|-19.8|-9.5|-3.2|-26.2|
|**`GAS`**||-11.1|-11.7|-11.0|-11.5|-10.0|||-21.0|-10.3|
|**`AIR`**|-17.9|-16.4|-17.5|-20.7|-15.3|-20.7|-15.2|-21.0|-20.8|-20.8|

See table 1 for sector codes. **Source: Authors’ estimates.** 56
of excess saving in China, in fact, has been the local corporate sector, in particular large corporations. Gross corporate savings in China have increased to more than 26% of GDP in 2007 from 15% a decade earlier. Lin (2009) suggests that such a high saving rate of local large corporations is due to inefficiencies in the Chinese financial sector, mostly formed by large banks. This sector is not tailored to finance investment by small or medium-sized enterprises, and ought therefore to be reformed before long.

High gross corporate savings are however seldom originated by inefficiencies in the financial sector. As corporate savings are formed out of non-invested profits, a key issue is to identify the drivers of the accumulation of profits. As Anderson (2009) points out, gross corporate savings are formed by profits earned on markets abroad, hence part of Chinese saving is not originating in the domestic economy but in foreign trade. While gross profits as a share of total revenues have been stable in China, their ratio as a share of GDP has increased very sharply since 2002, against the backdrop of doubling industrial sales revenues with respect to GDP over the 2002–2009 period, driven by steel, basic materials, and machineries. Industrial-sector data indicates that corporate profits have boomed, rising steadily from 2% of GDP to above 10% in 2007, driven by expanding market shares. This is a pattern similar to increased saving observed in oil-exporting countries over the same period of time.

As a result, China’s internal imbalance is directly caused by the external flow of trade on product markets. An adjustment of the yuan exchange rate would reduce gross profit earnings and, indeed, saving in China. However, the magnitude of the adjustment required in that respect would amount to an important decrease in the volume of foreign trade, at a time when the global economy is already suffering from excess capacity.

7