rafmacalaba/datause-encoder-data huggingface dataset:
|**Table II: Difference of Means**|**for Census Variables**|||
|---|---|---|---|
|**Variable**|**Treatment Mean**|**Control Mean**|**Mean Difference**|
|_Proportion of blocks of houses with at least one house that has dirt floors_|0.573|0.612|-0.040|
||(0.030)|(0.042)|(0.051)|
|_Proportion of households with dirt floors_|0.173<br>|0.189<br>|-0.016<br>|
||(0.026)<br>|(0.026)<br>|(0.037)<br>|
|_Average number of children between 0 and 5 yrs of age_|0.710|0.721|-0.011|
||(0.014)|(0.014)|(0.020)|
|_Number of households_|511.292|488.989|22.303|
||(60.381)|(38.105)|(71.399)|
|_Number of people_|2241.586|2170.597|70.989|
||(280.400)|(172.460)|(329.191)|
|_Average number of rooms per household_|2.352|2.353|-0.002|
||(0.069)|(0.075)|(0.102)|
|_Average number of people per household_|4.326|4.448|-0.122|
||(0.033)|(0.109)|(0.114)|
|_Proportion of households with no water connection outside the house_|0.076|0.043|0.033|
||(0.018)|(0.014)|(0.023)|
|_Proportion of households with no water connection inside the house_|0.392|0.316|0.076|
||(0.038)|(0.036)|(0.052)|
|_Proportion of households with no water connection in the bathroom_|0.507|0.451|0.056|
||(0.040)|(0.032)|(0.052)|
|_Proportion of households with no gas heater_|0.028|0.029|-0.001|
||(0.004)|(0.004)|(0.006)|
|_Proportion of households with no refrigerator_|0.204|0.212|-0.007|
||(0.018)|(0.013)|(0.022)|
|_Proportion of households with no washing machine_|0.379|0.359|0.020|
||(0.018)|(0.018)|(0.026)|
|_Proportion of households with no telephone_|0.804|0.786|0.018|
||(0.014)|(0.020)|(0.025)|
|_Proportion of households with no vehicle_|0.735|0.734|0.000|
||(0.014)|(0.018)|(0.023)|
|_Average overcrowding index_|2.302|2.314|-0.012|
||(0.063)|(0.069)|(0.094)|
|_Proportion of households below the poverty line_|0.140|0.148|-0.009|
||(0.015)|(0.012)|(0.019)|
|_Proportion of households with illiterate members_|0.063|0.059|0.004|
||(0.003)|(0.004)|(0.005)|
|_Average years of schooling of head of household_|6.386|6.514|-0.128|
||(0.097)|(0.166)|(0.192)|
|_Average number of school dropouts among children aged 5 - 15 yrs_|0.180|0.172|0.007|
||(0.008)|(0.009)|(0.012)|
|_Average number of household members who work_|1.508|1.592|-0.084|
||(0.026)|(0.064)|(0.069)|
|_Average number of family members who earn an income_|1.422|1.495|-0.072|
||(0.025)|(0.060)|(0.065)|
|Number of observations|599|370||

Notes: Table computed at block level using 2000 census-block information. Standard errors clustered at census-block level shown in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% 29
developing countries), and financial education for entrepreneurs (mainly in developing countries).

In the next section, we begin by surveying the evidence on financial literacy levels around the world, as well as the results of empirical studies on the relationship between financial literacy and various outcomes. The following two sections discuss what we have learned about making financial education programs more effective, drawing lessons from recent impact evaluations of programs for both consumers and entrepreneurs. Finally, our review concludes with a summary of the literature’s lessons for policymakers, with the goal of improving the design and implementation of future financial education programs, and identifying gaps in our knowledge which still need to be addressed.

# **II. MEASURING FINANCIAL LITERACY AND ITS CORRELATES**

As is the case with most policy interventions, the first step to improving financial literacy is to be measure it. After all, why do we think financial education is necessary in the first place? Surveys around the world consistently indicate that financial literacy levels are low in high-income countries. There is less data available for middle- and low-income countries, but levels in these countries appear to be even lower. Furthermore, studies indicate that low levels of financial literacy are associated with and often cause adverse financial outcomes. This section compares the available survey results for both developed and developing countries, and reviews the empirical research with an emphasis on how financial literacy correlates with demographic characteristics various financial behaviors. Such research has improved our understanding of the determinants and consequences of financial literacy, and has the potential to improve the targeting of financial education programs as well.

The measurement of financial literacy itself is a non-trivial issue. As Lusardi and Mitchell (2011a) note, ―While it is important to assess how financially literate people are, in practice it is difficult to explore how people process economic information and make informed decisions about household finances.‖ As mentioned in the introduction, financial literacy encompasses a number of concepts, including financial awareness and knowledge, financial skills and financial capability, and it is hard to capture all of this information in a survey of reasonable length. However, a set of three questions first developed by Lusardi and Mitchell (2011a) for the American Health and Retirement Study (HRS) in 2004 are commonly used.

3
# **Annex 2: Selection bias analysis**

As detailed in section 2, the average characteristics of firms included in the survey differ substantially from those of the sample frame, a fact which suggests the presence of non-random selection into the survey. In table A.2 we present the results of a regression estimated on the whole set of firms in our sample frame in which the dependent variable is a dummy that indicates whether the firm was successfully interviewed or not. The regressors correspondent to the firm’s age, its size in terms of employment, its export activities according to customs data and the federal district of location. The results show that older, bigger and exporting firms had a higher probability of being successfully interviewed.

<u>Table A.2 Selection bias in survey response</u>

|<br>Dep. var.: ==1 if successful interview|<br>(1)<br>OLS|<br>(2)<br>Probit|
|---|---|---|
|Log firm age|0.012<sup>**</sup>|0.088<sup>***</sup>|
||(0.005)|(0.032)|
|Log employment|0.043<sup>***</sup>|0.269<sup>***</sup>|
||(0.006)|(0.031)|
|Exporter in 2016, customs data|0.021<sup>**</sup>|0.125<sup>***</sup>|
||(0.008)|(0.041)|
|Central federal district|-0.004|-0.019|
||(0.016)|(0.096)|
|South federal district|-0.003|-0.019|
||(0.017)|(0.101)|
|Volga federal district|-0.021|-0.143|
||(0.017)|(0.106)|
|Ural federal district|-0.005|-0.031|
||(0.016)|(0.092)|
|Observations|12440|12440|
|_R_ <sup>2</sup>|0.017|0.029|

Clustered standard errors at the district-large sector level in parentheses. The reference category for federal districts is the Northwestern federal district. Pseudo-R2 value reported for column 2. Significance: * p<0.10, ** p<0.05, *** p<0.01.

Since the evidence shown on section 4 indicates that better management practices are found among bigger and exporting firms, the results of table A.2 suggest that, a priori, better managed firms were possibly more probable of being included in the survey sample. This may generate a selection bias in our analysis. In order to control for such bias, we use sample weights to compensate for it. In particular, we follow two approaches. The first one is applied by Lemos et al. (2016) on their analysis of management practices in Pakistan and consists in using as weights the inverse selection probability that result from column 2 in table A.2. The second approach follows a maximum entropy estimation (Wittenberg, 2010) and constructs weights such that average firm characteristics in the weighted survey match those of the sample frame. In particular, we choose as “target” characteristics to be matched the firm size in terms of employment (defined in 7 size groups), the exporting status (defined by 2 groups – exporting and nonexporting) and large industrial sector (8 groups). These three characteristics define 112 groups and the weights are estimated such that the percentage of firms in each of these groups in the survey corresponds to the same percentage in the sample frame.

29
(NDVI) and the normalized difference water index (NDWI) to improve prediction accuracy. Both of these metrics are derived from Landsat satellite imagery, with NDVI being related to the coverage of vegetation and NDWI related to the water content on Earth’s surface.

The summary statistics for our key indicators differ between the sample and the universe, because the former was designed so as to over-represent urban places (table 5). As expected, the mean values for population, population density, the built-up share and the lit-up share are substantially higher in the sample than in the universe. On the other hand, the mean values of NDVI and NDWI are not statistically different, suggesting that they may not help improve the prediction substantially.

**Table 5. Summary statistics of key indicators**

||Observations|Mean|Std. Dev.|
|---|---|---|---|
|**Selected sample**||||
|Population size (_thousands_)|1,277|46.79|392.48|
|Population density (_people per km _<sup>_2_ </sup>)|1,277|3085|6057|
|Built-up share (_percent_)|1,277|13.05|19.99|
|Lit-up share (_percent_)|1,277|52.97|45.45|
|NDVI (-1 to 1)|1,277|0.29|0.10|
|NDWI (-1 to 1)|1,277|0.09|0.09|
|**All**||||
|Population size (_thousands_)|564,052|2.10|33.53|
|Population density (_people per km _<sup>_2_ </sup>)|564,052|693|2700|
|Built-up share (_percent_)|564,052|0.81|4.43|
|Lit-up share (_percent_)|564,052|8.57|25.93|
|NDVI (-1 to 1)|564,052|0.31|0.11|
|NDWI (-1 to 1)|564,052|0.08|0.09|

# _Classical data modeling_

Subjective assessments of the places in the sample can be used to infer the status of all other places in the universe. In line with the classical statistical tradition, this can be done using an econometric model, in which case an explicit probabilistic distribution is assumed for the data. The most common functional choices in the classical statistical tradition are the Logit and the Probit models, which respectively assume a logistic and a normal distribution for the data ( <mark>Cameron and Trivedi 2005).</mark> Following the classical statistical tradition, we specified a probabilistic model on whether a place is urban or rural in practice. We used the standard _Logit_ model linking the urban status of a place with the values of its key indicators. The specification took the following form: 18
Contractual Savings In Countries With A Small Financial Sector

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- 28 -
Second, asset price bubbles are not easy to spot in real time – at least at their early stages, when policy action might be most useful to stop them. Identifying a misalignment of asset prices requires a reliable assessment of their fundamentals. But available models of asset price fundamentals are inherently imperfect, and in addition some fundamentals – e.g., investors’ perceptions of risk and future asset productivity --- are not readily observable. A simpler alternative would be to use of rules of thumb, such as a threshold above which asset price increases would be deemed to involve bubbles. However, aside from their arbitrary nature,<sup>29</sup> simple rules of thumb would likely lead to frequent identification errors, especially in narrow and illiquid asset markets (such as those of most emerging countries) characterized by high asset price volatility. And reacting to a misidentified bubble may be very costly – e.g., tightening in response to an asset price rise that in reality is driven by improving fundamentals will both hamper growth and interfere with the role of asset prices in allocating resources. Of course, one possible solution to this problem is to react only to bubbles once they become self-evident, but by then it may be too late for monetary policy to mitigate their effects. Indeed, if the authorities tighten when the bubble is already nearing collapse on its own, the contractionary effects of the tightening could compound, rather than mitigate, those of the bubble collapse.

Third, what asset prices should the monetary authority watch? Not all asset prices are synchronized, and at any one time bubbles may be present in just a few of the many

> 29 How fast should asset prices rise to safely conclude that a bubble is at work? Too low a threshold would lead to the (incorrect) detection of too many bubbles.  Even modest changes in key fundamentals – such as investors discount rates or their anticipations of the rate of growth of future dividends – can lead to large changes in the prices of long-lived assets over short periods. In turn, too high a threshold (say 20% or 40% per year) could fail to detect many bubbles altogether. For example, during the recent U.S. housing bubble real estate price increases remained consistenly below 15% per annum, according to the Case-Shiller U.S. national home price index.

29
the most recent nationally representative LSMS-ISA survey in Ethiopia (2013-14), nearly 21 percent of _parcels_ (not fields, as measured in LASER _)_ were reported as having poor soil quality (CSA, 2013).

Subjective assessments of soil fertility also suffer from a lack of intra-household variation. Of households with more than one cultivated field in the sample, 63 percent reported the same soil quality on all plots. Similarly, 71 percent reported the same soil type, 73 percent reported the same soil color, and 68 percent the same soil texture. This is striking, especially given the high number of fields cultivated per household. Figure 2 (A–D) illustrates the percentage of households reporting no variation in the abovementioned indicators, by number of plots cultivated.

Descriptive analysis suggests that farmers use soil color and texture as indicators of soil quality. As observed in Figure 3, self-reported dark and fine textured soils were categorized as _good_ soils while red and course textured soils were more frequently categorized as _poor_ soils. While the more specific subjective questions, such as texture and color, appear to be correlated with the overall quality assessments, the value of these questions in terms of correlation with objectively measured soil properties, believed to be the truest measure, remains to be analyzed. Section 3 will explore these correlations.

8
where Outcome� is the outcome for student i, j indexes the three assignments to the eReader treatment and X� is a vector of student-level controls. Given the nested nature of the treatment groups (see below), the _Treatj_ variables are an overlapping set of indicators. This implies that:

- Treat1 variable takes the value 1 for all students who received an eReader with the noncurriculum digital library and zero otherwise;

- Treat2 takes on the value of 1 for students who received an eReader with the curriculum textbooks and zero otherwise;

- Treat3 takes on the value 1 if a student received an eReader with the supplementary curriculum materials and zero otherwise.

This overlapping definition implies that β� captures the impact of receiving an eReader with no curriculum content and β� captures the marginal impact of additional content received (for j=2, 3). In addition to estimating specification (1) we estimate heterogeneous treatment effects by fully interacting assignment indicators with baseline student characteristics.

Outcome� �σ� � Σα�Treat� � Σγ�Z� ∗Treat� �δZ� �λX� � ε� (2) Z _i_ represents key interactions that capture a range of sources of heterogeneity motivated by theory and recent empirical research. In particular, we examine the following interactions separately: (i) student scoring above median reading at baseline (Glewwe, Kremer and Moulin, 2009), (ii) student owned a paper textbook at baseline (Das et al. 2013), and (iii) student had aspirations for high level of education at baseline.

We estimate the impact of information treatment using a reduced version of equation (1), where Treat� is 1 if student i did receives the information treatment and 0 otherwise.

To explore the potential influence of attrition (missing students) on our findings, we calculate Lee (2009) bounds on the potential treatment effect given extreme assumptions on attrition bias. Lee bounds assume monotonicity, i.e. that the likelihood of non-response is monotonically related to receiving the treatment. This assumption rules out the possibility that the treatment may affect sub-groups differently and implies that non-respondents in groups with high response rates would not have responded if their treatment status were changed. To do this, we follow Tauchmann (2014) and use the **leebounds** command in Stata12.

# **4. Device Usage**

Descriptive statistics on self-reported eReader usage are presented in Table 4. Information on eReader usage was collected through a self-administered survey as well as direct extraction from the devices. Self-reported data show high usage. Around 73 percent of the treatment sample claimed to have used the device at least once a week or almost every day. The likelihood of using the device at least once a week or more is slightly higher among students in the T1 group. About 45 percent of the sample gave a reason for limited usage; and the most frequently cited reason was lack of electricity to charge the device, followed by broken eReader. About 262 students (87 percent of the interviewed treatment sample) were able to name at least one book they had read on 8
**Table 2 Subgroup Decomposition of the Global Income Inequality**

|Year|Global I|nequality|Subgro|ups||
|---|---|---|---|---|---|
||Gini|Theil|Countries|China-India ver|susRoW|
|2000|0.68|0.93|Between<br>0.69|Between|0.17|
||||_(0.75)_||_(0.18)_|
||||Within<br>0.23|Within|0.76|
||||_(0.25)_||_(0.82)_|
|2030|0.63|0.77|Between<br>0.54|Between|0.03|
||||_(0.70)_||_(0.04)_|
||||Within<br>0.23|Within|0.74|
||||_(0.30)_||_(0.96)_|

Notes: (1) Decomposition results are based on the Theil index (Generalized Entropy index with aversion parameter equal to 1) decomposition (2) Proportion of total income inequality between brackets Source: Authors’ calculations As a matter of fact, the decomposition results for China and India versus the rest of the world shows that 14 out of a total of 16 points reduction in the Theil index between 2000 and 2030 are explained by a reduction in inequality in average incomes between the China and India group versus the rest of the world (compare the result of 0.17 Theil points explained by China and India in 2000 with the 0.03 points for 2030). In other words, average income in China and India are closer to the world’s average in 2030 than what they were in 2000.

# 4.2 The Emergence of the Global Middle Class

According to our baseline, in 2030, 16.1 percent of the world population will belong to what can be called a “global middle class,” up from 7.6 percent in 2000. That is, in 2030 more than a billion people in developing countries will buy cars, engage in international tourism, demand world-class products, and require international standards for higher education. Compare that with only 250 million people in developing countries who had access to these kinds of living standards in 2000. This large middle class will create rapidly growing markets for international products and services—and become a new force in domestic politics.

The _global middle class_ is defined here as in Milanovic and Yitzhaki (2002). The authors proposed disaggregating the world population into three categories—the poor, the middle class, and the rich—where the middle class is defined by two absolute thresholds equal to the per capita incomes of Brazil and Italy.<sup>16</sup> By assigning an individual to the global middle class according to his or her income, Table 3 shows the evolution of this income group and contrasts it with the groups of the poor and the rich.<sup>17</sup> This table also shows that the great majority of the global middle class entrants are citizens of developing

> 16 Italy’s per capita income was used as the upper threshold because it was the country with the lowest income among the G7; Brazil’s per capita income corresponded to the official poverty line used in rich countries like the US and Germany (about $PPP 10 per capita per day).

> 17 Notice that the definition of poor used here is far from being comparable to the standard 1 dollar-a-day definition.

19