rafmacalaba/datause-encoder-data huggingface dataset:
conditions and assets-based wealth index that is over 1 standard deviation higher. Applicants are generally slightly less likely to be working than tertiary educated young women as a whole, of whom 56% are currently working, with wage work and self-employment accounting for 71% and 24% of the jobs, respectively.

## **2.5 Compliance**

Despite the late change in announced venue,<sup>20</sup> and the significant time commitment of the training, attendance was fairly high with 74% of the individuals assigned to treatment attending at least one session.<sup>21</sup> Attendance at each of the daily training sessions was also high: of the 334 individuals who attended at least 1 session, 325 attended 10 or more sessions. A post-training participant evaluation conducted on the last day of the training suggests that participants were satisfied with it: over 90% of attendees indicated that they were either satisfied or very satisfied with the content, delivery, and relevance of the training.<sup>22</sup> Participants were slightly less satisfied with the duration, pace, and exercises of the training, with respondents suggesting that future iterations be conducted over a longer period. The high satisfaction bears out in ex-post participant-reported willingness-to-pay for the training, with participants answering that they would pay US$208 for the training. We discuss how this compares to the cost of implementation in the Discussion section, below.

# **3 Analysis**

In this section, we report intent-to-treat (ITT) estimates of the training. Treatment assignment was random within strata,<sup>23</sup> so the pooled impacts of the intervention across three survey rounds on a given outcome _Yi_ , _t_ can be measured using the following ANCOVA regression specification: where _yi_ , _t_ represents outcome _y_ for household _i_ at follow-up _t_ , Treat _i_ is an indicator variable equal to 1 if individual _i_ was invited to the training, _Yi_ , _t_ 0 is the baseline value of outcome _y_ for individual _i_ , **_ξ_** _t_ is a vector of survey round indicator variables, and _δstratum_ is a series of strata

> 20As mentioned above, one week before the training, the implementation partner shifted from offering venues in three regions of the country as announced in the public lotteries, to having only one venue in the South of the country. To compensate for this shift, there was a significant investment in contacting participants and facilitating their transportation to the venue.

> 21Appendix Table A8 examines the correlates of attendance in the treatment group, finding lower attendance among individuals originally assigned to complete the training in Kigali, as well as those who reported positive pre-program wage or casual employee work.

> 22Appendix Table A9 presents summary statistics from the participant evaluation.

> 23There were 18 strata corresponding to: 6 lotteries in Kigali (three for men and three for women), 6 lotteries in the South (four for men and two for women), and 6 lotteries in the North (four for men and two for women).

9
**Table 2:  Industry and Manufacturing Share of Employment and GDP Across Countries**

||Employment in<br>industry as %<br>total<br>employment in<br>2000|Value added in<br>industry as %<br>of GDP in<br>2000|Value added in<br>manufacturing<br>as % of GDP<br>in 2000|2002 GDP per<br>capita (in 2000<br>USD)|
|---|---|---|---|---|
|India|18.2|26.3|15.6|480|
|Brazil|19.3|28|17.1|3473|
|China|23|45.9|34.7|1106|
|Indonesia|17.3|45.9|27.7|844|
|Pakistan|18|22.6|14.8|532|
|Vietnam|12.4|36.7|18.6|444|
|Low-income countries|12.3|26.6|14.1||
|Lower-middle income|||||
|countries|18.5|38.3|24.2||

_Source:_ World Development Indicators 2005. _Notes:_ Industry includes manufacturing, but also mining and quarrying (including oil production), construction, and public utilities (electricity, gas, and water). Lower-income countries and lower-middle income countries are defined based on the World Bank classification.

**Table 3:  Growth in Sectoral Value-Added Across Countries**

||Growth<br>ma|in value added in<br>nufacturing|Growth in val<br>agricul|ue added in<br>ture|Growth i<br>added in s|n value<br>ervices|
|---|---|---|---|---|---|---|
||1995-00|2000-05|1995-00|2000-05|1995-00|2000-05|
|India|28.1%|38.4%|13.7%|14.9%|48.4%|50.3%|
|Brazil|5.7%|5.6%|17.3%|23.7%|26.5%|31.8%|
|China|57.6%|67.4%|18.5%|21.2%|57.3%|61.2%|
|Indonesia|14.6%|27.5%|7.0%|17.3%|-2.4%|35.7%|
|Pakistan|17.1%|56.6%|26.5%|12.0%|20.9%|29.9%|
|Vietnam|70.3%|73.8%|24.2%|20.7%|31.9%|40.0%|

_Source:_ World Development Indicators 2005. _Note:_ The table shows grow rates for each sector in total value added (in constant local currency units) between year t and year t+5.

South Asia suffers from three infrastructure deficits. First, there is a _service deficit_ , as the region’s infrastructure has not been able to keep pace with a growing economy and population. Power outages and water shortages are a regular occurrence in India and Bangladesh. Rural roads are impassable in lagging regions in India (e.g., Bihar, Uttar Pradesh) and Sri Lanka. India has 6000 km of four lane highways and China in the last 10 years has built 35,000 km of four to six lane highways. Every month, China adds power capacity equivalent to what exists in Bangladesh. Second, South Asia suffers from a _policy deficit_ , given highly distorted pricing, poor sector governance and accountability, and weak cost recovery. It is estimated that eliminating the financial losses from the power and water sectors alone would provide a substantial chunk of the incremental funds for infrastructure investment that India needs. Third, South Asia suffers from a _cooperation deficit_ . India, one of the energy thirstiest nations sits next to an immensely 24
||Table lb. Base and Final Subsidy Commnitme<br>Selected Commodities in Major Subsidizing Cou|nts for<br>ntries||
|---|---|---|---|
||000**metric**tons|||
||Beef|& Veal||
||Subsidized Exports|||
||Country<br>1986-90|1995|2000|
|EU|1,034.3|1,118.7|817.1|
|Brazil|106.7|105.2|91.779|
|Austria|X0.9|90.1|63.882|
|Poland 1/|51.7|49.9|40.9|
|Hungary|36.0|35.0|28|
|Total (tops)|1,309.6|1,398.9|1,041.7|
|Total 2/|1,372.4|||
|Top5**ms% **of Tot|al Export Subsidies<br>95.4|||
||Pigme|at||
||Subsidized Exports|||
||Country<br>1986-90|1995|2000|
|EU|508.6|490.8|401.8|
|Hungary|115|111|91|
|Poland 1/|51.7|49.9|40.9|
|Sweden|47|45.1|37.1|
|linland|8.1|11.3|6.4|
|Total(tops)|730.4|708.4|577.2|
|'otold 2/|741.0|||
|World Trade|2,441.0|||
|Tol)5 as % of Tot|al Export Subsidies<br>98.6|||
|**Top**5 as % of W|orld Trate in 1991/92<br>29.9|||

||Polltry|||
|---|---|---|---|
||Subsiidizeed<br>Exports|||
|**('ountry**|1986-90|1995|2000|
|Il:|367.S0|440.10|290.60|
|**liiigary**|141.00|136.00|111.00|
|l3ra7il|97.94|96.57|84.23|
|IIS|35.44|34.20|27.99|
|Poland|16.40|_15.80_|13.10|
|**.To:j**(top5)|65S57|722.66|526.92|
|T'otal 2/|663.68|||
|Worl**lI'raile**|2074.00|||
|1'1) 5 as'..**of'T'otal**<br>**I'xlxrt**Subsidies|0.99|||
|**'op**5 as**% oil'**Worldl**l'ritde**in I**99)**I**/92**|31.75|||

1/ **Tlhe volume** of exlxwt subsidy **for** l'oland includes all meats except for poultry 2/ **Total E'xpkort** Subsidies Scource: **Author's** calculation.s: **hasic** data **are** from l **'nastsy** Roundl country scheduLles.

12
Figure 7 Contribution of the intensive and extensive margin to China-OECD imports growth, 1963-2006 <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1963-1975 1975-1993 1993-2006<br>extensive margin intensive margin<br><!-- End of picture text --> <!-- Start of picture text --> 1963-1975<br><!-- End of picture text --> <!-- Start of picture text --> 1975-1993<br><!-- End of picture text --> <!-- Start of picture text --> intensive margin<br><!-- End of picture text --> Thus, although OECD imports are increasingly concentrating on Chinese products, evidence from section 3 shows that OECD countries continue to open new imports lines with extra-OECD countries. Put differently, China’s expanding exports to the OECD do not seem (yet) to crowd out the entry of new exporter/product pairs on OECD markets.

The evidence in this section shows diversification taking place over time and as a function of income levels, subject to two caveats: (i) China’s growing share of OECD imports, which caused a recent re-concentration, and (ii) an ultimate reversal (i.e. a re-concentration) at the intensive margin at very high levels of income. However, the evidence so far does not say what drives this progressive diversification, nor what counter-forces, if any, may be at play, preventing it from being instantaneous. That is, prima-facie evidence does not tell us when the doors of OECD markets open and when they close. In order to deal with this question, we now explore empirically the implications of the quality-search model of section 2.

27
this means the population in this area faces the highest chances of remaining poor when poor and of falling into poverty when not poor.

# 3.2. Absolute three-way economic mobility

In this section, we go beyond a two-way poor/non-poor classification and split the income distribution into three groups, incorporating vulnerability as an analytical concept. This enables us to study absolute mobility for a larger group of Malaysians. We focus on those who experience either upward mobility (moving up one or two groups between surveys), downward mobility (moving down one or two groups), persistent security (remaining in the richest group), or persistent vulnerability (stuck in the middle group).

Researchers have used We employ two different scenarios for this three-way classification.<sup>20</sup> various ways of defining the concepts of vulnerability and middle class, we apply two such approaches in this study. It is important to note that these scenarios depart from different analytical positions, such that results have somewhat different interpretations. In order to sketch what part of the income distribution we are considering, Figure 17 in the Appendix shows how the two scenarios map onto the distributions in 2004 and 2022. The scenarios are as follows:

1. A division into poor, vulnerable and economically secure groups, as described in section 2.23. Here we conceive of vulnerability as an elevated probability of falling into poverty. This employs the poverty line of poverty scenario 1 at RM 527 and a vulnerability line of RM 1,120. The latter is estimated by applying the method proposed by Dang & Lanjouw (2017) to the 2004-2007 transition interval and selecting a vulnerability index of 0.3333. It means that those with an income of between RM 527 and RM 1,120 in 2004 faced a one in three chance of having an income below RM 572 in 2007.

2. A classification where we derive the income thresholds by applying the Chaudhuri method (see section 2.3). Based on a 10 percent probability of future poverty, this yields a Chaudhuri vulnerability line of RM 920 and a ‘middle class line’ of RM 1,590.<sup>21</sup> The low-income group consists of the poor and vulnerable, the middle group is labelled ‘aspiring middle class’, and those with incomes above RM 1,590 cover the middle and upper classes in economic terms. By coincidence, RM 1,590 is also approximately the mode of the income distribution in 2022.

> 20 Note that all income levels are again in 2016 Malaysian ringgit, in per capita terms per month.

> 21 The poverty line here is the average of the official 2019 household-specific poverty lines (our scenario 2 poverty line). We also investigated results based on a 20 percent maximum probability of poverty (at thresholds of RM 742 and RM 1,063, respectively). This gave qualitatively similar results, so we do not report on the outcomes.

19
10 first and second groups contain one adjuster each (Senegal and Burundi, respectively), and the third group contains three adjusters (Ghana, Kenya and Togo); the different patterns are thus not tied strictly to adjustment status. Adding the WFS observations does nothing to clarify the situation: only for Senegal do the WFS and DHS series come close to linking up; for Kenya, the DHS estimates appear below the WFS ones, and for Ghana, the WFS estimates are below the DHS one. The figure gives little support to the idea that the pace of decline in child mortality has changed in the recent past. But the figure does suggest a great deal of variation among countries in both levels and trends. The trends can be examined more formally by regressing the child mortality estimates for the periods zero to four years and five to nine years before each DHS survey on the estimate for the period 10 to 14 years before the survey and on measures indicating the number of years included in 1980-84 and 1985-89 for each survey period. The regression coefficients on each time period dummy should thus indicate the mortality decline associated with those periods. Results are shown in table 1.

Table 1. Regression-Based Estimates of Child Mortality Decline in the 1980s for 10 Sub-Saharan African Countries with Demographic and Health Surveys

|Child Mortality||Regression co|efficients|for|R<sup>2</sup>|
|---|---|---|---|---|---|
|Measure|Const|ant<br>Base|1980-|84<br>1985-89||
|(<br>)|(|)<br>Value|(|)<br>(<br>)|(<br>)|
|5qo|74.2<br>(2.5)|0.706<br>(10.6)|-12.6<br>(-1.9)|-12.1<br>(-2.6)|0.905|
||42.3|0.664|-5.5|-5.9|0.853|
||(2.3)|(8.2)|(-1.4)|(-2.1)||
|4qo|45.2|0.693|-9.1|-8.1|0.927|
||2.8|(12.5)|(-2.3)|(-2.9)||

Note: T values in parentheses.
The pilot exercise in Peru was the first implementation of the EFL tool in Latin America and relied on the “Africa v2 psychometric credit score”, which was based on information from 920 observations with loan repayment data, almost all of which were from Africa. The financial institution participating in the exercise, one of the five largest commercial banks in Peru (we refer to this bank as our “partner bank”), piloted the EFL tool starting in March 2012, with the goal of expanding its SME portfolio. At the time, our partner bank was not very active in the SME market. Its conventional screening method, which relied on a three-digit credit score from Equifax Peru and a site visit to the SME, was better suited for larger companies and resulted in a high rate of credit rejection in the SME segment.<sup>7</sup> During the pilot exercise, SME loan applicants were screened by the EFL tool and received a three-digit psychometric credit score. All applicants who achieved a score higher than a threshold set by our partner bank were offered a loan, making it possible to use a regression discontinuity (RD) design to evaluate the effectiveness of the tool.<sup>8</sup> The cutoff was set arbitrarily by our partner bank, as the bank had no historical information with which to set it. Applicants with a score below the EFL threshold were also offered a loan if they were approved under the bank’s conventional screening method. Only SMEs that were rejected under both screening methods were not offered a loan from our partner bank.

The way in which our partner bank applied the EFL tool differs from the way other financial institutions have used it. When credit bureau information exists, financial institutions typically use this information to approve prospective good credit clients and reject prospective bad credit clients. Some of these rejected applicants may however be profitable clients, particularly if they are rejected not because of bad credit bureau information but because of lack of information i.e. because they have thin credit bureau files. The EFL tool is often used to assess the creditworthiness of these thin file clients. Different from this typical use, our partner bank used the EFL tool to grant credit to all types of applicants including those with thin credit bureau files and those with bad credit bureau information.

A reason why our partner bank was willing to test the EFL using applicants with bad credit histories may be that in order to calibrate the model, they needed to observe a minimum number

> 7 Peru’s credit bureaus cover 100% of the adult population, so that everybody has a credit score, but for individuals without a credit history this credit score is primarily based on demographic information.

> 8 Figure A1 in the appendix shows the distribution of EFL scores for all SME loan applicants in our sample, below and above the selected threshold.

3
6 At least 10% of surveys were monitored in person, and most surveys were checked for inconsistencies by field monitors. Surveys with significant inconsistencies were either readministered or a different replacement household was selected to complete a new survey.

## **2.1.2 Rwanga Sample**

A second data source we draw upon is from a sample of recently or currently active female job seekers, in partnership with a local non-profit, Rwanga Foundation– we refer to this as the “Rwanga sample” throughout the paper. Rwanga maintains a jobs portal, where students and other job seekers are recruited from universities and job fairs to register, and employers are invited to post new opportunities each month. Rwanga has offices and outreach programs in the three urban areas of Erbil, Dohuk, and Sulaymaniyah. We discuss findings from both a longer diagnostic exercise with a smaller sample of 100 job seekers, as well as a short survey with a larger sample of 1,780 job seekers (all of whom were registered with Rwanga at the time of the study) that we conducted. This sample of female job seekers is different from the social norms sample; in particular, women in the sample were younger and better educated, and most had completed a college degree.

# **2.2 Measuring Social Norms and Cultural Beliefs**

A review of the social norms literature concludes that it is important to distinguish between measuring empirical and normative beliefs when measuring social norms (Mackie et al, 2015). These have been characterized as “social empirical” and “social normative” expectations (Biccheiri et al, 2014) or often as “descriptive” and “injunctive” norms (Cialdini, Kallgren, & Reno, 1991). Another way of stating these dual aspects is to say that social norms consist of a rule for behavior and common knowledge of that rule (Brennan, Eriksson, Goodin, & Southwood, 2013) or between following a “typical” or “appropriate” action in a group (Paluck and Ball, 2010). We will define and follow the interpretation as below:

- **Social Empirical expectation** _:_ the extent to which individuals believe that others in a relevant reference group/population conform to or engage in the behavior. This is a _firstorder_ expectation: a belief about “what others typically do” in a relevant group.

- **Social Normative expectation** : the extent to which individuals believe that members of a relevant reference group believe they ought to conform to a behavior and may sanction those who do not. This is a _second-order_ expectation: a belief about the beliefs of others, and specifically beliefs about what others in one’s reference group think one should do. We will often simplify and refer to it as “what others believe” in a relevant group.

<mark>We interpret results as follows; if both social empirical and normative expectations are consistently reported in a social group, there is strong prima facie evidence that a social norm exists.</mark>
The use of the U.S. data makes it impossible to estimate the effect of distance and source country trade costs together with time and host country effects, since the U.S. appears as the only source country in this sample. Also, since the U.S. is more skilled labor abundant than any other country in the dataset, there is no need to take the absolute value of the skill difference between the source and host countries. The results for these estimates are reported in Table 3, where the bottom panel of the tables shows the point estimates of the effects of Chinese and Indian FCS on LAC FCS.

The first column of the table shows the OLS estimation. Again, we do not find any evidence of substitution in these estimations. Although none of the variables of interest are statistically different from zero, the overall coefficient of the effect of China on LAC is positive and significant, which is consistent with our previous estimates. The overall coefficient for India is also positive, but not significantly different from zero. There is no evidence of an impact of China on U.S. FCS in other countries manufacturing industries. The overall coefficient for India on the other hand is 0.33 and significant at the 10 percent level.

The second column of Table 3 shows the Poisson estimates. As in the OLS estimation, the overall impact of China on LAC continues to be positive and significant and the overall impact of India on LAC is not different from zero. The impact of China in the manufacturing sector is also continues to be positive. Finally, the negative coefficient of the impact of China in LAC manufacturing is no longer significant once we control for the KCM variables, thus shedding more doubts on the substitution hypothesis.

The last column shows the results of the Negative Binomial estimator. The signs of the coefficients for the aggregate effect of China and India are consistent with the other 16
- 1.5 -

There are tradtional regional patterns In food consumption In Ghana. The major staple foods In the North are miliet, sorghum and yams. 'n the coastal regions, maize products are more predominant. Plantains and tubers are consumed more In the forest zone. With increasing migratlon these broad reglonal patterns now follow thin lines. Malze and rice are gradually becoming national food Items, particularly In the South, and they are consumed by a large cross section of the population.

Agricultural production in Ghana Is characterized by smailholders who use traditional methods with little or no purchased Inputs. The major inputs are land and labor. There Is not much pressure on land as revealed by the 1984 population census. On the average there are 51 persons per square kilometer. It is estinated that of the total land In Ghana about 10 million hectares of them are suitable for agricultural productlon. However, due to accessibility and other constraints, less than one third of this land is currently being cropped. Farm sizes tend to be small because of the production technology used and also the Inheritance and land tenure systems which lead to land fragmentatlon. Farm extensions therefore occur in the vertical direction with one farmer owning a few plots at different locatlons in the village or in the country.

There are variations in the land tenure system depending upon the area. Generally, in the North, all the lands are vested In the government and so outright sale can be obtained only from the government. However, temporary occupation of land can be arranged through the 'tindana' or caretaker of the land In the area concerned. In the South, the chiefs are the custodians of the lands for the people. In most places, In the South, outright sale of land Is forbidden but security of tenure can be obtained through a long-term lease or sharecropping arrangement. People who come from an area (citizens) can obtain land from the chief for farming purposes and the farm can be passed on to one's heirs.

The relative abundance of land has given rise to a bush fallow-cropping sequence, especially In the South where the vegetation and rainfall patterns can support