rafmacalaba/datause-encoder-data huggingface dataset:
Table 13c: Difference-in-difference Estimates of the Impact of PROSPERA on ENLACE Achievement Tests: 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008 Propensity score matching with exact matching on age and state

|Variables|7th grade- 6th grade|9th grade -6th grade|Increase in category<br>of score from 6<sup>th</sup>grade|
|---|---|---|---|
||score<br>_Pane_|score<br>_l A: Spanish_<br>|<br>to 12<sup>th</sup>grade|
|Girls|3.77|10.98*|0.011|
||(5.87)|(6.69)|(0.042)|
|Boys|7.21|8.16|0.07|
||(5.82)|(7.08)|(0.05)|
|Observations|2,387|2,474|1,326|
||_Panel B_|_: Mathematics_||
|Girls|1.64|0.522|-0.002|
||(6.18)|(8.58)|(0.043)|
|Boys|7.26|13.25|0.015|
||(6.1)|(8.53)|(0.053)|
|Observations|2,297|2,365|1,183|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

In summary, the results for sixth grade children enrolled in schools in medium to very high marginalized areas show significant increases in enrollment, effects which persist through high school. With respect to secondary schools attended the results indicate that the program overall increases the probability of attending a tele-secondary school and reduces the probability of attending a technical school. At the high school level, the program increases the probability of attending a general high school and reduces the probability of attending a technical high school. There is no effect of the program on enrollment in private schools. With respect to effects on achievement, the results are generally small and variable according to the estimator used. On balance, the results are statistically significant but not large in magnitude, although, due to selection in enrollment, the estimates are susceptible to downward bias.

Results for indigenous children show similar trends, but somewhat larger. Enrollment effects are on the order of 10 to 12 percentage points increases in the probability of attending secondary school and on the order of 8 percentage points for high school. Effects on achievement were larger in Spanish at about 0.1 standard deviation but generally not significant in the area of mathematics. Effects of 0.1 standard deviation however compare favorably to a recent survey of impacts of educational interventions around the world (see <u>https://www.povertyactionlab.org/research-resources/cost-effectiveness).</u> 35
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changes in distribution, we should expect a change in poverty rates between 0.62*8.58 = 5.32 and 0.68*8.58 = 5.83 percentage points if one uses a deflator reflecting the food share of the bottom 30 percent of the population to define real expenditure changes.

Table 3: Sensitivity of Headcount Poverty to the Poverty Line

||Usin|g 1996 Suse|nas Data|Using|1999 Suse|nas Data|
|---|---|---|---|---|---|---|
|Percentage<br>increase in<br>poverty<br>line over<br>lowest<br>level|If poverty<br>line is<br>(Rp/person<br>/month)|Then<br>headcount<br>poverty is<br>(percent)|Elasticity<br>(percent point<br>change in<br>poverty/<br>percentage<br>change in<br>expenditures)|If poverty<br>line is<br>(Rp/person<br>/month)<br>|Then<br>headcount<br>poverty is<br>(percent)|Elasticity<br>(percent<br>point change<br>in poverty/<br>percentage<br>change in<br>expenditures)|
|0|28,516|9.75|-|62,877|9.75||
|_5_|29,942|12.01|0.45|66,021|12.10|0.47|
|10|31,368|14.39|0.50|69,165|14.55|0.51|
|15|32,793|16.93|0.56|72,309|17.40|0.63|
|20|34,219|19.50|0.59|75,452|20.18|0.64|
|25|35,645|22.06|0.62|78,596|23.03|0.68|

The combination of (a) the sensitivity of measured price inflation to the changes food share, (b) the change in real expenditures to changed inflation estimates, and (c) the sensitivity of poverty rates to expenditure changes can give us some rough rules of thumb as to what to expect from various food shares (o) in price deflation embedded in the poverty calculations. The formula is:

> **6** This 9.75 percent is produced by an iterative procedure for defining the poverty rate outlined in Pradhan _et al_ (2000) using Susenas 1996 data set.

10
group. Older children (1-3 years of age) account for 64% of the target population in the participating communities (table 2). During this last age window, children are vulnerable to infections. During these critical period the immune system is still not fully mature (Martorell 1999) and susceptible to illnesses. The program emphasizes the importance of hygiene practices as well as promotes child care practices in the event of illnesses.

# **4.4. The Outcome of Interest**

Our outcome of interest is the average malnutrition rate, which is measured by the nutritional status of children under three, of the communities participating to the program. We use weight-for-age<sup>21</sup> as an indicator for the nutritional status of children. If the weight (for a given age) of a particular child falls below two (three) standard deviations from the international standard (z-scores), then the child is considered as moderately (severely) malnourished.

Figure 5a presents the non-parametric density estimates of malnutrition rates over time, separated by the three age groups.. Three points are worth noting in this figure: (1) the proportion of underweight children increases with age, (2) There is a downward trend in the outcome of interest in the participating communities, as malnutrition rates decrease over time for all age groups, (3) There is seasonal variation in malnutrition rates, which slightly rebound around the lean season (in the first and last quarters of each year), especially for the 7-12 and 13-36 months old children.

In addition, we document in figure 5b the trends in malnutrition rates over time, separated by age groups, for participating communities that have different lengths of exposure. As we stress earlier in this paper, the vertical distance between these trends cannot be entirely attributed to the difference in the duration of program exposure. It is because the vertical distance captures the difference in the average malnutrition rates between sites that entered the program in two different periods without accounting for initial differences in the selective selection into different program durations.

> 21 The weight of each child attending the weighing sessions is obtained from the registry information.

15
- 6 -

Securities Market (USM), and after the enactment of several incentives which can be grouped under three schemes: th,.- venture capital scheme, the business expansion scheme and thei share incentives scheme. The U.K. industry consists of four types of specialized venture capital funds: i) independent companies which raise money from external sources; ii) bank subsidiaries and financial institutions (including investors in Industry--3i); iii) venture capital funds established under the government's Busiress Start-Up Schers (BES), later renamed Business Expansion Scheme (BES); and iv) government/local authority backed venture capital firms in the U.K. rose from 19 in 1979 to 57 in 1982 and to around 120 in 1987. The capital pool increased from nearly US$2.0 billion in 1982 to over US$4.5 billion in 1987. Captive investors--bank subsidiaries and other financial institutions-account for the "lion's share" of the total amount invested in the U.K. This can be explained by the emphasis which capital investors place on the latter stages of financing, such as expansion financing and management buy-outs. Among them, 3i still provides over one-quarter of the industry's total funds.

Investors in Industry (3i), formerly Industrial and Commercial Fi.nance Corporation (ICFC), which was established in 1945 by the London and Scottish clearing banks in association with the Bank of England, provides a good example of the government-supported, sometimes privately-runned, venture capital institutions which have been more typical of the European scenario for the industry. Originally intended for term lending and with a central objective "to play a significant part in the evolution of the industrial and commercial sectors of the British economy", it has, in fact, been a private sector institution with a longer term view. 3i has provided the venture capital community with leadership, in the sense the SBICs did in the American market, but in a different way: it started the business in the post-war period when there were no private players ready for it, and it has supplied private firms with much needed scarce expertise. A survey by U.K. Venture Capital Journal, in early 1986, showed that 35 out of a total of 340 managers in 59 venture capital firms had worked at 3i. About two-thirds of 3i's disbursements go to small enterprises and most of them in the form of a package of loans and equity investments. As early as 1975, ICFC had 2,330 investments in 18 different branches of industry, into which it had invested over L.225 million annually. By 1987, the total assets of 3i were about L.2.2 billion in over 5,000 companies. Although 3i appears prepared to back any business, 44 percent of its investments are in the manufacturing sector. In some respects, 3i differs from a "pure" venture capital firm. It does not require its portfolio companies to commit themselves to a public flotation and is prepared to hold their shares for a longer time provided they pay dividends. Nevertheless, over the last ten years 3i has achieved a compound rate of return in excess of 20 percent per annum which reflects
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- _55_ -

# iJ)wiCogesion Metering in Cambridge, England

72. Cambridge, England is currently in the design stage of a road pricing scheme based on smart card technology. A prototype on-board unit (transponder plus smart card) - and its miniaturized version -- is currently being developed as part of the Pricing And Monitoring ELectronicaly of Automobiles (PAMELA) project, which is one of the projects under the European Community's Dedicated Road Inftastructure for VEhicle Safety (DRIVE) research 5 programme l. Full scale field trials are expected to begin in 1993-94, with public consultation to be sought in 1995-96 for a decision of whether to go ahead with road pricing or not.

73. The scheme works as follows. A cordon is placed around the City of Cambridge and beacons are strategically installed at 17 radial roads which fully control the gateway to the city. Whenever an automobile equipped with an on-board unit containig a valid smart card passes a roadside beacon when 'congestion' occurs, a 'congestion unit' of 20 pence [US36 cents], say, is debited electronically on the smart card (Oldridge, 1990). Simultaneously, trffic information by lane is also collected by a real-time data-concentrator, which aids in monitoring the congestion situation. However, the key difference between the Cambridge road pricing concept and all other road pricing systems is that the former is based on _cc post_ pricing and the latter on _er ante_ pricing. Congestion charging, in this case, is applied literally - as and when - congestion occurs, without any prior notification regardless of whether avenues of escape are available at a congested botdeneck. When an equipped automobile enters the city during the peak hours, the in-vehicle unit is tiggered on and is turned off only upon exit or whenever the engine is shut off. Thus the Cambridge system is based on continwus pricing rather than on point pricing, which was the basis of the Dutch smart card system and most other AVI systems.'<sup>2</sup> A 'congestion unit' is debited on a smart card when one of the following

> Si A microwave c caions link using a 2.45 GHz bs e 1qutly was already tosted at Nowcasde in 1990 (Hills and Blytho, 1990; Blyth and Hlls, 1991; horpe and Hills, 1991).

> 52 A similar device caled Timezone, which chages for congestion an a continuous pricing bais, is designed by GEC-Mco. TI tial for tesing dlo dec equpment a to begin in the Landon Burfough of Richmond in 1991.
Table 9a: Impacts of PROSPERA on ENLACE tests All 6<sup>th</sup> Graders in Medium, High and Very Highly Marginalized Communities 2008 State Fixed Effects

|Variables|7th-6th score|9th-6th score|12th-6th level|
|---|---|---|---|
||_Panel A: Sp_|_anish_||
|PROSPERA beneficiary 2008|4.806***|8.181***|-0.012|
||(1.46)|(1.72)|(0.011)|
|Female beneficiary|2.641|-0.578|0.018|
||(1.881)|(2.214)|(0.014)|
|Female|8.090***|0.313|-0.036***|
||(1.583)|(1.867)|(0.011)|
|Constant|-16.145*|43.677***|0.761***|
||(9.133)|(10.797)|(0.071)|
|Observations|40,382|39,540|19,914|
|R-squared|0.035|0.081|0.022|
||_Panel B: Mat_|_hematics_||
|PROSPERA beneficiary 2008|4.944***|11.139***|-0.030**|
||(1.614)|(2.195)|(0.012)|
|Female beneficiary|-0.918|-0.411|0.012|
||(2.08)|(2.825)|(0.015)|
|Female|-2.187|2.071|-0.090***|
||(1.75)|(2.383)|(0.012)|
|Constant|6.103|115.010***|0.936***|
||(10.099)|(13.778)|(0.076)|
|Observations|40,382|39,540|19,440|
|R-squared|0.048|0.084|0.027|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

The results in these tables vary to some extent with the estimator used, but overall suggest some small positive effects on achievement with impacts on achievement in seventh and ninth grade averaging between 0.03 and 0.11 standard deviation, both on Spanish and on mathematics and both for girls and boys. With respect to the impacts in twelfth grade, recall that we use an alternative indicator that measures whether individuals increase at least one category in the ENLACE grading between sixth and twelfth grade. This indicator overall does not show significant effects, perhaps reflecting that there is less variation in the measure, although there is a slight unexpected and significant reduction in the probability of advancing categories in mathematics for 2 of the three models.

25
# Housing, Imputed Rent, and Households’ Welfare<sup>1</sup>

Lidia Ceriani<sup>2</sup> Sergio Olivieri<sup>3</sup> Marco Ranzani<sup>4</sup>

**JEL:** C46, D31, I32

**Keywords** : Rent Imputation; Inequality; Poverty Measurement; Shared Prosperity.

> 1 The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. The authors wish to thank Dean Jolliffe, Peter Lanjouw, Kristen Himelein, and Carlos Rodriguez Castelan for their comments on previous versions of the paper, as well as Carlos Felipe Balcázar and Jikun Wang for their research assistance. 2 Georgetown University.

> 3 World Bank Group, Poverty and Equity Global Practice.

> 4 World Bank Group, Poverty and Equity Global Practice.
Those organizations are armed with details about potential victims and “scripts” instructing fraudsters on how to persuade potential victims to buy their stories and make money transfers (Levi, 2008). However, it remains unclear why fraud victims are victimized. In this paper, we investigate fraud victimization from the perspective of household financial conditions. Specifically, how and why do household credit constraints affect fraud victimization when facing fraud schemes?

Though our data have both urban and rural households, our investigation of the relationship between credit access and fraud victimization relies on the urban sample for several reasons. First, we have a credible identification strategy for the effects of credit constraints only for urban households, with details to be explained later. Second, previous research has shown that rural and urban access to credit differ greatly. Rural residents have much less access to bank finance and depend to a much greater extent on informal finance (Cull et al., 2019). Finally, rural and urban residents also differ greatly in their fraud victimization behavior, with rural residents living in more sparsely-populated areas, facing less social interaction, and using the internet less (i.e., a major source of fraud). We thus focus only on urban residents in this paper.<sup>6</sup> Using the urban sample of a novel nationally-representative data set on fraud victimization and household finance, we find that households facing credit constraints is a key determinant of fraud victimization. The baseline regression results show that being credit constrained is associated with 2.3 percentage points increase in the probability of becoming a victim, and 20.4 percent increase in the total amount of subsequent economic losses for those being approached, after controlling for other household characteristics and regional fixed effects. To deal with potential omitted variable bias and reverse causality issues, we employ the instrumental variables (IV) approach where the exposure to a nationwide property privatization reform, which happened only in the urban areas, and the local

> telemarketing and other forms (for more detail, see https://www.fbi.gov/scams-and-safety/common-fraud- <u>schemes). Those schemes differ from each other in many ways, from targeting subjects to operating tools.</u> For example, gambling scams take advantage of people’s willingness to win gambles. Fraudsters send email to potential victims and tell them that they need to pay a small amount of fee in advance to guarantee winning an imaginary big prize/lottery/rewards. Investment frauds encourage people to participate in fake and rare investment opportunities with absurd returns. Bogus products and services provide potential victims useless health care products, psychic services, fake career opportunities, and so on (Button et al., 2009). 6 At this stage, we have much less understanding of fraud victimization in rural areas, as will be implied in the literature survey. This topic is thus left for future research.

3
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_E i_ is an entity fixed effect at the country level and<sup>_T_</sup> _t_<sup>is a time fixed effect at the year level. This</sup> approach allows us to eliminate further bias by controlling for _both_ differences across countries as well as changing determinants within a country over time. Thus, our results can more likely be interpreted as causal. Still, we could be confronted with unobserved heterogeneity as soon as systemic changes coincide with other changes that drive test scores. This issue is exaggerated due to temporal gaps in our data. For this reason, we also control for a host of macroeconomic explanatory variables that vary over time.

# _B) Data_

Next we present a systematic overview of all the explanatory variables that enter in our analysis. We also discuss the difficulties that arise due to missing data in some of our core indicators of educational systems.

In particular, we complement the adjusted test score database with explanatory factors such as the overall governance of countries. A recent study by King et al. (2010) suggests that several governance indicators have a particularly significant impact on the rate of return to education. This finding is based on T.W. Schultz’ hypothesis (1975) that economic returns to schooling vary with the capacity to manage unforeseeable price, productivity or technology shocks (see King et al. 2010, p. 3). Thus, more freedom and rights allow individuals to reallocate their time and resources when unforeseeable shocks occur. In turn, investing in human capital becomes critical to ensure that individual reallocation is allowed and is efficient.

While this positive association between better governance indicators and higher returns to education is robust to the inclusion of several macroeconomic indicators, better governance indicators might also coincide with positive institutional changes that affect returns to education. To this end, we break down governance into more specific indicators. Specifically, we include a measure for Economic Freedom from the Heritage Foundation’s Index of Economic Freedom. This data exists since 1994 and provides an index consisting of several indicators such as the ease to open a business, openness to trade, taxes relative to income etc. We further include Globalization, which comes from an Index by Dreher (2006) for the years 1970-2006. We also add a measure for Civil Rights from the Empowerment Rights Index (which is available since 27"