rafmacalaba/datause-encoder-data huggingface dataset:
coefficients and their statistical significance decreasing in the medium term. Nevertheless, some impacts persist. In particular, the humorous fake reality TV drama is found to make viewers less likely by 13 p.p. (which is equivalent to 33% of the mean in the control) to believe women should be banned from the kitchen or household shrine during menstruation and 7.3 p.p. less likely (24% decrease) to think that it is more important that a boy goes to school than a girl. Treatment 2 also impacted the latter outcome by 8.8 p.p. (30% decrease) and increased the awareness of VAW being an issue in India by 7.8 p.p. (10% increase). For the latter, the coefficient is similar in magnitude to the short-term estimate.

# **4.4.2 Information-seeking and posting behaviors**

The short-term effects on information-seeking behaviors disappeared in the medium term (Annex Table A14).<sup>35</sup> On the other hand, treated participants were more willing than control participants to publicly display their disapproval of VAW in the medium term.<sup>36</sup> Columns (1)-(3) of Table 6 show that treatment 2 made participants more likely to intend to add the frame against VAW in their Facebook profile picture. While ITT estimates indicate an impact of 7.9 p.p. (32% increase), ToT estimates are twice as large (16 p.p.). Results are much smaller in magnitude and not statistically significant for treatment 1.

Most importantly, treatment 2 was effective in making individuals actually update their profile picture. ITT estimates indicate that participants in treatment 2 were 7.5 p.p. more likely than the control to add the VAW frame “End violence against women” to their picture, an increase of 91% (p<0.05). ToT estimates were twice as large: individuals who watched at least half of the documentaries were 15.3 p.p. more likely to add the banner, an increase of almost 190% (p<0.05). For treatment 1, the observed increases are not statistically significant.

Although these impacts were recorded only four months after program exposure, we were able to observe their potential cascade effects within the social network in the long run. We found that about one and a half years after the frame was first presented to the respondents, it was used by more than 34,000 people all over the world.<sup>37</sup> This is 55 times larger than the sample receiving the frame at first, and almost 500 times larger than the number of respondents who used it initially. Back-of-the-envelope calculations suggest that treatment 2 alone was responsible for about 6,300 of such uses.

> 35We discard the possibility that recall bias from the short-term survey drives this result. In fact, we find no medium-term information-seeking effects even when focusing on the subsample of individuals who were not interviewed at the short-term followup (58%).

> 36This outcome was only measured in the four-month data collection.

> 37The figure was objectively measured by the Facebook Frame Manager.

22
between these two samples; the column between the own account and wage workers shows the significance of differences in means between these two samples.

Table 1 displays differences in several measures of ability. In addition to years of schooling, we tested respondents in three ways. First, we conducted a forward digitspan recall test. Respondents were shown a three digit number. The card showing the number was then taken away. Ten seconds later, respondents were asked to repeat the number as written on the card. Those responding correctly were shown a four digit number, and so forth up to 11 digits. The table shows that SME owners were able to recall just over 7 digits on average, while own account workers recalled just under six on average.

The second ability measure comes from a Raven progressive non-verbal reasoning test. We provided 12 printed pages, each of which contained one 3 by 3 pattern with one cell missing. Below the pattern were eight figures, one of which fit the pattern, and the other seven of which did not. The patterns become progressively more difficult from the first to the 12<sup>th</sup> page. Respondents were given five minutes to complete as many of the patterns as possible. They were instructed to skip as desired, but told that the patterns became progressively more difficult. The average respondent completed three of the patterns correctly.<sup>7</sup> Finally, we administered a localized version of Frederick’s three question cognitive reflection test (Frederick 2005). The test asks, for example: “A bat and a ball cost Rs. 1100 in total. The bat costs Rs. 1000 more than the ball. How much does the ball cost?.” Fewer than one in three of the respondents answered any of the three questions correctly. We also use years of schooling and the response to three questions measuring financial literacy as measures of ability.<sup>8</sup> The pattern of ability is consistent by almost all measures: The ability of own account workers is lower than both the SME owners and wage workers. The gap with respect to SME owners is significant at the .01 level for all but financial literacy, where the significance level is .05. SME owners have just two years of additional schooling, answered correctly 0.18 more of the cognitive reflection questions correctly (both two-

> 7 We used the same version of the Raven used in the Mexican Family Life Survey (Rubalcava and Teruel, 2004)

> 8 The measure of financial literacy comes from Lusardi and Mitchell, 2006.
# 5.2.2  Women’s livelihood project in India

A poverty alleviation project implemented by the government of the Indian state of Bihar and funded by the World Bank led to the enrollment of economically and socially disadvantaged women in savings groups of 10-12 persons, and to a series of social changes that brought about greater gender equality in a very poor and patriarchal region of India. A qualitative study (Sanyal, Rao, and Majumdar 2015) tracked two control and two treatment villages intensively over a three-year period. The study systematically compared women’s empowerment outcomes in villages where women were actively participating, and in one where the project was never introduced. The study found that the eight-year project had given women in the treatment villages exclusive access to certain physical and financial resources (such as cheap credit), and also new symbolic resources and a new institutional environment. The women were organized into a well-defined network called Self-Help Groups (or SHGs) within and across villages. Once a women’s group had established a record of regular weekly savings, the group obtained access to loans at rates much lower than those available from village moneylenders. Each group met once a week, and the three elected officers of each of the groups met bi-monthly at the village and district levels. A majority of group members were entering domains and spheres of activity outside the home for the first time in their lives. The groups gave women an identity that cut across castes. The financial resources gave women greater bargaining power in the households, while the solidarity gave them greater political power. Their participation in civic, political, and financial institutions further broke down longstanding gender barriers, thereby “changing both men’s and women’s idea of what it means to be a woman.” Women who went outside their homes and courtyards were no longer perceived as immoral for moving freely. They also were much more likely to run for elections as the ward and village level. As one participant said, “You know, when a woman sees other women go out, she is willing to try it out herself...Now, I too have gone to other villages...” (p. 38).

# 5.2.3  Soap operas

Our third and last example of a variable that can change perception, cognition, and preferences is the media. When people who have never been exposed to societies with low fertility were exposed to soap operas in which the women had few children, fertility rates declined (Jensen and Oster 2009; La Ferrara, Chong, and Duryea 2012). In Brazil, there was a sizeable impact on fertility of exposure to long-running serial dramas produced by a company that deliberately crafted soap operas with characters who had few or no children in order to try to bring about social change. In general, the 35
Policy Research Working Paper 5290

# **Abstract**

This paper offers new measures of aid quality covering 38 bilateral and multilateral donors, as well as new insights about the robustness and usefulness of such measures. The 2005 Paris Declaration on Aid Effectiveness and the follow-up 2008 Accra Agenda for Action have focused attention on common donor practices that reduce the development impact of aid. Using 18 underlying indicators that capture these practices—derived from the OECD-DAC's Survey for Monitoring the Paris Declaration, the new AidData database, and the DAC aid tables—the authors construct an overall aid quality index and four coherently defined sub-indexes on aid selectivity, alignment, harmonization, and specialization. Compared with earlier indicators used in donor rankings, this indicator set is more comprehensive and representative of the range of donor practices addressed in the Paris Declaration, improving the validity, reliability, and robustness of rankings. One of the innovations is to increase the validity of the aid quality indicators by adjusting for recipient characteristics, donor aid volumes, and other factors. Despite these improvements in data and methodology, the authors caution against overinterpretation on overall indexes such as these. Alternative plausible assumptions regarding weights or the inclusion of additional indicators can still produce marked shifts in the ranking of some donors, so that small differences in overall rankings are not meaningful. Moreover, because the performance of some donors varies considerably across the four sub-indexes, these sub-indexes may be more useful than the overall index in identifying donors’ relative strengths and weaknesses.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to determine how aid can more effectively promote development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sknack@worldbank. org and hrogers@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
Page 47

# Promising Approaches in Small-Scale Credit

114. In the meantime, suggestions for promising approaches to credit can be drawn from the Bank's experience in lending for small-scale enterprises, innovative credit programs (in Indonesia, for example), and non-Bank programs that reach the poorest women (particularly the Grameen Bank in Bangladesh and SEWA in India -- see box). It appears from these and similar efforts that poor women borrow to a limited extent through informal networks of kin or from moneylenders and often demonstrate a strong repayment performance. But ordinary banks and other lending institutions are often reluctant to lend to SSEs because of administrative problems in handling many small loans and the possible weakness of small-scale operators in the general economy. They are demonstrably reluctant to lend to women, who seldom have more than a modest fraction of credit from such institutions (Schumacher and others 1980; World Bank 1989a, 1989b; Hossain 1987). (This varies, of course, but only in Latin America do women often obtain as much as a third of the credit under SSE programs, and even in that region the situation differs by country and area.) Women can be good credit risks (often better than men) (Schumacher and others 1980; Hossain 1987). But women face all the usual problems of SSEs compounded by a relative lack of education and business experience, a legal and regulatory environment that may discriminate against them, collateral requirements they cannot meet because men tend to have official title to land and other assets even whier, women manage them, and cultural traditions that generally hamper women's enterprises (Schumacher and others 1980; Hossain 1987; World Bank 1989a, 1989b).

115. Varying cultural and economic circumstances will require different approaches. But in general it is important to consider whether the broad policy framework (laws, policies, and regulations) provides und.storted incentives to smaller enterprises and to women in particular. Overvalued exchange rates, for example, may reduce demand for goods produced by SSEs. Or does that framework unduly hamper the ability of SSEs, particularly women, to respond to whatever incentives exist? For example, production or location permits, foreign exchange quotas and licenses and other bureaucratic requirements may favor large-scale producers (especially educated or powerful men) who have the know-how and resources to cut through red tape. Small-scale entrepreneurs, especially women, may lack the ability to organize, to "group for scale", and so may lose opportunities to buy bulk inputs at lower cost or to share the costs of marketing assistance. Access to credit may be a particular problem.
higher grade. Those who were unable to attain the required learning levels would be provided with opportunities to achieve them in the higher grade (The World Bank, 1999).

# III. **Data**

This study utilizes data collected in the context of a randomized controlled trial of the impact of financial incentives on student performance. The experimental study was implemented in public schools in Nepal to understand if financial incentives are effective in boosting student achievement. The intervention was applied on grade eight students in semi-urban areas of Kathmandu during the 2009 – 2010 school year lasting from April 2009 to March 2010. From a pool of 33 schools, a randomly selected subset of 11 schools comprised the treatment group while the remaining schools constituted the control group.

Grade eight curriculum consists of five mandatory subjects – Nepali, English, Math, Science, and Social Studies – and four peripheral courses. The exam for each core subject is worth 100 points and the pass mark is 32. The exams for the non-core subjects are worth 50 points each with pass mark of 16. Eighth graders take three exams every year: two trimester exams internal to the school and the year-end exam common at the district level. Students in the treatment group were eligible for cash rewards in direct proportion to their scores in each of the three exams. Incentives based on average aggregate score were offered for all three exams taken in the year. In this paper, I focus on the scores in the five core courses for the sake of comparability across all schools.<sup>5</sup> Two student surveys, one at the beginning of the academic year before the announcement of the incentives and one towards the end of the year, collected information on household characteristics and student time use at home. Because the survey was administered in school, the survey information is missing for students who were not present in school on the day of the survey. Since the official information on student attendance is not always reliable, I use the number of students who filled out the baseline and exit surveys as measure of school attendance.<sup>6</sup>

> 5 Schools have some leeway in selecting the elective courses.  Health and Physical Education, and Population and Environment are taught in all but one school, 15 schools teach Moral Education, and 25 teach Vocational Studies.  The results reported in this paper are robust to calculating average aggregate score using all nine subjects.

> 6 In most cases, appointment for surveys was made either the previous evening or the same morning so the visits are unannounced for all practical purposes.  Attendance rate and other information might be nonrandom in the few occasions when the request was not met and the visit was scheduled for a later date.

5
the city level is not a concern. We choose to focus on firm productivity and employment, exploiting the variability of the data at the firm level. Even though we include a variable on capacity agglomeration, we do not intend to draw conclusions that involve the interpretation of firm agglomeration and sorting variables at the city level. The limitations of the data will become more pressing if the analysis wants to be extended at the city level.

# **Table 1.  Variable definitions and Sources**

|**Variables**|**Definition and source**|
|---|---|
||One-year employment growth rate. First calculate 3-year growth rate as the change in<br>permanent employees in 3 years divided by initial number of permanent employees. Then|
|L-grow|convert it to one-year growth rate (g1). The L-growth = 2*g1/(2+g1), which is the Haltiwanger<br>measure of labor growth, bound by -2 and 2 to avoid the typical extreme outlier problem<br>associated with the un-transformed g1.|
|lnLP|The logarithm of labor productivity (LP). LP is measured as sales over the number of permanent<br>employees. Winsorized at tail 2 percent to avoid the outlier issue.|
|LP-growth|The annualized (Haltiwanger) LP growth rate. The procedure is the same with L-growth except<br>that the basic building block is LP instead of L.<br>Total factor productivity, estimated as the residual from industry-specific production function|
|TFP|with log value added as the dependent variable and log capital and log labor as the<br>independent variables. K is replacement cost of land and machine. L is the number of<br>permanent employees plus 0.5 times the number of temporary employees.|
|Foreign|The share of foreign ownership of the firm.|
|OwnBiggest|The ownership share of the largest owner.|
|L020-100<br>(100+)|The firm’s number of employees three years ago was 20-100 (more than 100).|
|Age 6-10 (10+)|The firm’s age is between 6 and 10 years (or 10 or more years).<br>The firm is located in a city of a million residents or the capital. This variable has 20.5% of<br>missing, and to avoid loss of sample, we impute the missing with World Bank income group|
|BigCity|dummies, regional dummies (LAC, MNA, SAR, ECA, EAP, Africa), service industry dummy, size<br>dummies (10-20 employees, 20-60 employees, 60+ employees), age dummies (firm age<br>between 6 and 10 years old; 10 years plus).|
|Small|The firm is a small enterprise, that is, employing fewer than 50 employees.|
|Young|The firm’s age is younger than 10 years.|
|Rich|The firm is located in a country that is below the medium GDP per capita in the sample.|
|Exporter|The firm is an exporter.|
|High-k|Log of the industry average capital-labor ratio is above the medium of the industries.|
|lnPop1|Log(once-lagged population level)|
|lnGDPPC1|Log(once-lagged GDP per capita in real US dollars).|
|lnPopDen1|Log(once-lagged population density)|
|XX Obstacle|City-level average of the firm’s answer on whether “XX’ constitutes an obstacle, ranking from|
|c|0 to 4 (with 4 being more severe obstacles). XX is one of the following: corruption, skilled labor|

7
|||Factor|||Factor||
|---|---|---|---|---|---|---|
||Global<br>(1)|Advanced<br>(2)|Emerging<br>infows<br>(3)|Global<br>(4)|Advanced<br>(5)|Emerging<br>infows<br>(6)|
|BAA spread|-0.259<sup>_∗_</sup>|-0.493<sup>_∗∗_</sup>|-0.183|-0.201<sup>_∗_</sup>|-0.444<sup>_∗∗_</sup>|0.0431|
||(0.103)|(0.152)|(0.129)|(0.0796)|(0.132)|(0.0971)|
|US real|-0.0498|-0.133|-0.176|-0.0368|-0.122|-0.169|
|exchange rate (log)|(0.0678)|(0.0667)|(0.147)|(0.0642)|(0.0667)|(0.155)|
|Financial openness|4.424|2.518|17.17<sup>_∗_</sup>|3.963|1.918|6.241|
|<br>(average)|(3.540)|(7.053)|(7.258)|(3.312)|(6.989)|(5.689)|
|Yield curve|-0.0625|-0.220<sup>_∗_</sup>|-0.121|-0.0306|-0.184<sup>_∗_</sup>|-0.198<sup>_∗_</sup>|
|slope|(0.0716)|(0.0883)|(0.112)|(0.0533)|(0.0740)|(0.0866)|
|Commodity price|0.0189|-0.371<sup>_∗_</sup>|-0.0898||||
|(non-energy, log)|(0.111)|(0.141)|(0.161)||||
|Agriculture price||||-0.166|-0.397<sup>_∗∗_</sup>|0.218|
|(log)||||(0.0859)|(0.139)|(0.119)|
|Metals&minerals||||0.234<sup>_∗_</sup>|0.0428|-0.259|
|price (log)||||(0.102)|(0.135)|(0.130)|
|cons|-0.00393|-0.0461|-0.168|-0.000412|-0.0415|-0.0867|
||(0.0626)|(0.0940)|(0.128)|(0.0592)|(0.0912)|(0.106)|
|_N_|36|36|36|36|36|36|
|_R_<sup>2</sup>|0.482|0.593|0.311|0.603|0.624|0.303|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 Table A.7: Regressions in first differences, with the same regressors used in the VARX of Table 8. The Financial Openness measure is a 3-year moving of the Chinn-Ito account index over average capital openness averaged advanced and emerging countries.

55
# **Table 4. Descriptive Statistics**

||**Y**|**ear**|**Diffe**<br>|**rence**<br>|
|---|---|---|---|---|
||**2005**|**2012**|**betwee**|**n years**|
|**Socio-demographic characteristics for the full sample (%)**|||||
|**Adults (15 years of age or older)**|**65.8**|**67.7**|**1.9**|***|
|**People living in urban areas**|**27.4**|**27.2**|**-0.2**||
|**Women as percentage of adults**|**50.3**|**50.9**|**0.7**|*0|
|**Working adults (as percentage of adults)**|**60.7**|**62.7**|**2.0**|***|
|Women (as percentage of working adults)|35.0|38.3|3.3|***|
|Wage/salaried workers in the agricultural sector (as percentage of working adults)|21.9|18.3|-3.6|***|
|Wage/salaried workers in the non-agricultural sector (as percentage of working adults)|33.2|39.4|6.2|***|
|Self-employed workers in the agricultural sector (as percentage of working adults)|57.5|44.8|-12.7|***|
|Self-employed workersinthenon-agriculturalsector(as percentage ofworking adults)|12.9|12.4|-0.5|*|
|**Socio-demographic characteristics for urban areas (%)**||||0|
|**Adults (15 years of age or older)**|69.7|71.9|2.3|***|
|**Working adults (as percentage of adults)**|44.8|46.9|2.1|***|
|Women (as percentage of working adults)|21.0|23.9|2.9|***|
|Wage/salaried workers in the agricultural sector (as percentage of working adults)|5.7|4.0|-1.7|***|
|Wage/salaried workers in the non-agricultural sector (as percentage of working adults)|60.2|62.8|2.6|***|
|Self-employed workers in the agricultural sector (as percentage of working adults)|14.0|10.0|-4.0|***|
|Self-employed workersinthenon-agriculturalsector(as percentage ofworking adults)|26.8|25.8|-1.0||
|**Socio-demographic characteristics for rural areas (%)**||||0|
|**Adults (15 years of age or older)**|64.3|66.1|1.8|***|
|**Working adults (as percentage of adults)**|67.2|69.1|1.9|***|
|Women (as percentage of working adults)|38.8|42.3|3.5|***|
|Wage/salaried workers in the agricultural sector (as percentage of working adults)|26.3|22.2|-4.1|***|
|Wage/salaried workers in the non-agricultural sector (as percentage of working adults)|25.8|33.0|7.1|***|
|Self-employed workers in the agricultural sector (as percentage of working adults)|69.4|54.4|-14.9|***|

13