rafmacalaba/datause-encoder-data huggingface dataset:
machine learning (‘applied machine learning is basically feature engineering’ (Ng, 2011)). A brute force data mining approach such as Blumenstock et al. (2015) would algorithmically extract indicators while being agnostic towards the outcome variable. However, such approaches can pick up spurious correlations that make them unreliable in practice (Lazer, Kennedy, King, & Vespignani, 2014). With infinite data and variation, a machine learning approach would drop spurious variables on its own; but when data and variation are finite, focusing on features with intuitive or theoretical links to the outcome of interest can improve stability. Intuition can also suggest more nuanced indicators which a brute force method would neglect. Our approach generates indicators intuitively linked to repayment, which may pick up how consumers manage usage over time, social connections, and consumers’ potential capacity to repay. Indicators that have an intuitive link are also more palatable to implementation partners who can be wary of ‘black box’ methods.

This paper show that these behavioral indicators can reduce the uncertainty around a person’s type. They can derisk interactions between large firms and the poor, at scale. The largest promise of these new technologies is not to duplicate the familiar loan offerings that evolved under the constraints of physical interaction, but to enable new types of formal lending that would not be feasible under historical constraints.

This paper demonstrates the method with data from a telecom in a middle income South American country that is transitioning subscribers from prepaid to postpaid plans. In this country, only 34% of adults have bank accounts but 89% of households have mobile phones.

It tends to be difficult to quantify the performance of scoring rules in mature applications because of a ‘missing labels’ problem: one only observes repayment among people who were granted credit in the first place. As a result, if a lender already uses a sophisticated selection rule that fully incorporates all available information, little can be learned about the performance of different rules. In this regard, our setting has two crucial features. First, in the exploratory phase observed, the telecom sought to learn the risk of different types of applicants, and so extended credit permissively. It selected subscribers with sufficient usage from across the distribution of credit histories (including no history at all). Our results are 4
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capital expenditure decisions at the firm-level by looking at financing patterns. Specifically, if the market perception hypothesis is correct and managers respond to market valuation even when it exceeds their own assessment of fundamentals or when equity is overpriced relative to fundamentals, firms should issue new equity and finance investment out of this new equity. However, as noted by Blanchard et al. (1993), it is not necessary that the firm always use the proceeds of the stock issue for capital expenditures. The firm could also invest these proceeds in financial assets like Treasury bills. What the firm does in practice therefore is an empirical issue.

Therefore, firms can be expected to issue equity when equity is overpriced relative to fundamentals or when market valuation is expected to be more important than fundamentals. On the other hand, firms would finance internally, issue debt or buy back shares when equity is underpriced relative to fundamentals or when fundamentals are expected to dominate market valuation. In the literature, this is also termed as the "market timing" hypothesis. The undervaluation and overvaluation of equity can be assessed by looking at trends in a broad market index like the S&P 500 or changes in the price earnings ratio.

There is broad support for the market timing hypothesis in the literature. For instance, using U.S. data, Taggart (1977) showed that firms preferred to issue equity when the stock prices were relatively high. For the UK, Marsh (1982) found that the timing of security issuance was towards periods when the equity market in general and the firm's stock in particular were experiencing large price gains and when interest rates were at relatively low levels. This may reflect the effect of lower market discount rates increasing firm values at the same time that more investment projects became profitable.

13
are 31 (32) percent lower than mean estimated revenues based on our observed transactions and reported hours.

The implied level of underreporting of sales based on our direct observations of firms therefore accords very closely with the average level of underreporting that firms say firms like theirs do. It therefore seems reasonable to conclude that revenues are likely to be deliberately understated by firms. Nevertheless, when we adjust the revenue for each firm by the extent which it reports that firms like itself misreport, we find that both the Pearson and Spearman correlations between reported and estimated revenues are 0.66. This suggests that reported revenues still are providing reasonable information as to the scale of firm activities, just that most firms are understating this scale.

# **9. Conclusions and Discussion**

Few small firms keep business accounts in developing countries, yet data on the profits from such businesses is crucial for answering many important economic questions. We began this paper by showing that there is a very low correlation between what firms report as profits, and what they report as revenue-expenses. We show that a large part of these differences can be reconciled through adjusting revenue-expenses for business goods used for home consumption, and for mismatching of revenues with the expenses incurred to produce those revenues. Asking questions on mark-up can be done easily in cross-sectional surveys and provides a practical method to adjust for differences in the timing of inputs and sales of outputs. Smaller adjustments in profits occur when we also account for unreported drawings in the form of business revenues being used to pay for household expenses.

We then turned to the issue of recall errors. Monthly sales are understated when recalled after four months compared to one month. However, there is little recall error associated with annual sales compared to quarterly collection of monthly sales. We experimented with giving firms diaries to record daily revenue and expenses. The use of diaries increased both reported revenue and expenses, suggesting that recall errors are present in 21
particularly strong among affluent, high-income couples, can, in turn, have an impact on income inequality: Schwartz (2010) estimates that increases in earnings inequality would have been about 25-30 percent lower in absence of assortative mating. However, the impact of FLFP on inequality can go in a completely different direction. Several authors ( _inter alia_ , Fernandez and Rogerson, 2001; Esping-Andersen, 2007) argue that assortative mating increases household inequalities; an opposite equalizing effect (Maxwell, 1990; Harkness, 2010; Atkinson, 2015) will primarily emerge when the labor supply of lower educated women increases rapidly. In addition, in the long run, a rise in female employment may diminish the reproduction of inequalities.

The impact FLFP has on household income inequality is not unique and depends on several factors. First, it depends on the importance of assortative mating (Eika et al., 2019); if significant, the promotion of women’s labor force participation will give rise to widening household income inequality (Burtless, 1999). Second, different labor market regulations, fiscal policies and social situations have a significant impact on the final outcome (Albrecht et al., 2003; Mandel and Semyonov, 2006; Olivetti and Petrongolo, 2008; Betcherman, 2012). Finally, in accordance with the hypothesis that informs this paper, the relation can be non-linear. According to Sudo, (2017), inequality in incomes increases along with a larger percentage of FLFP (Elveren, 2014), as the number of high-earning women has an inequality-widening effect on income inequality. In a second phase, inequality reduces with an increase in the percentage of FLFP, as the reduced inequality of a wife’s income has an attenuating effect on household income inequality.

This inverted U-shaped relationship, while it has been theoretically formulated, has not received adequate attention from the empirical literature. This is because cross-country comparative studies have tended to focus on developed countries because of data limitations and also as – with some exceptions – income inequality levels are lower and female participation rates are high (Harkness, 2010); as Pasqua (2008) points out, inequality among women’s incomes tends to be higher in societies where FLFP is low.

> to marry those with similar educational attainment, while those with less formal education are also increasingly likely to marry those with lower education levels. Consequently, considering a household with individuals with lessthan-high-school education levels, family income based on the average of individual incomes would have been 77 percent of the national mean household income in 1960, dropping to 41 percent in 2005 (Greenwood et al. 2014).

5
14 included to provide a crude representation of the effects of labor force growth and technical progress on the capital stock, but was restricted to the cointegrating space since, as Table 4 showed, the firstdifferenced variables do not appear to display trends.

Because likelihood ratio tests of cointegration suffer from considerable small-sample bias (which typically results in their finding cointegration 'too often'), it is essential that the critical values be adjusted for sample size. Thus, the critical values presented in Table 5 were computed as indicated by Cheung and Lai (1993). In the present case, this results in an upward revision of the asymptotic critical values by roughly 46 percent.<sup>16</sup> The first column of Table 5 lists the number of cointegrating vectors (R) under the null hypothesis. From the second and third columns, the hypothesis of no cointegration can be rejected at the 5 percent level, in favor of at most one cointegrating vector, without much evidence of more than one.

# _4.3 Estimation of the conditional_ **_model_**

Rather than pursuing the estimation of the full reduced-form VAR system, we **tum** to the estimation of a structural model of _kpr,_ conditional on the other variables. 1<sup>7</sup> Since Table 5 suggests that there is only one cointegrating relation in the full system, a single-equation structural model is sufficient to estimate the parameters of the long-run relation (11). Thus the structural model is of the form: and is complemented with the reduced-form model of the conditioning variables: where w _= (kg, z, crpr, pd'_ is the vector of conditioning variables; aj and x are parameters, and _Aj 4P, B , C,_ and A are parameter vectors; _E_ and rj are random disturbances, and the term in round brackets in the 16 Asymptotic critical values were taken from Osterwald-Lenum (1992). Their (inappropriate) use in this smallsample context would lead to finding four cointegrating vectors.

17 Here and in the rest of this section the term 'structural model' is used in the simultaneous-equation sense that the equation of interest (12) includes the current values of more than one variable.
noted above, the presenters were all members of the immigrant groups themselves, to break down any cross-cultural communication barriers. It was emphasized that the aim of the training was to not advocate for any one particular remittance provider but instead aimed to help the participants become more informed consumers who could shop around for better remittance deals (see Figure 1b as an example).

The attendance rate for the training session was 148/177 for the Pacific Island treatment group (84%), while 3 of the control group also attended (1.7%), accompanying friends in the treatment group. For the East Asian migrants, attendance was 112/179 (63%) in the treatment group, with 26/173 (15%) of the control group attending, who were friends from the same churches as treatment group participants. Among the Sri Lankans, the attendance rate was 60/107 (56%) for the treatment group, and none from the control group. All analysis will be based on intent-to-treat effects, using the random assignment to be invited to training.

# **2.5 Follow-up Surveys**

At the end of each of the following 3 months after the financial literacy training, all respondents from the baseline survey were given a short follow-up survey on their remittance activity during the past month along with questions on major financial actions taken during the previous month, such as applying for a credit card. In addition, the one month survey also asked several financial literacy questions in order to measure whether financial knowledge had increased with the literacy training. Six months after training, all participants from both the treatment and control groups were invited back to community forums. A final round follow-up survey was conducted at the start of this forum, after which both treatment and control were given information on the main messages of the training course and information on new products and developments in the market that had occurred since the original intervention.

Table 2 shows the attrition rates by survey round and ethnic group sample. Attrition rates are lowest for the Pacific Island sample, averaging 5 percent at 1 month, 9-10 percent at 2 and 3 months, and 14 percent at 6 months. In no survey round can we reject balance between treatment and control groups. The East Asian sample has attrition rates of 9 percent at 1 month, 17 percent at 2 months, 23 percent at 3 months, and then 75 percent at 6 months, again balanced by treatment status. The Sri Lankan group had the highest attrition, with 45 percent of the treatment 13
# Table 5

||LRAZ<br>|<br>|A1G<br>|Mf<br>|BO<br>|L<br>|
|---|---|---|---|---|---|---|
||Inflation<br>(CPI)x|Se4gniorage<br>(X GDP)|Inflation<br>(CPI) X|Seigniorage<br>(X GDP)|Inflati4n<br>(CPI)X|SeIivlorwe<br>(X GDP)|
|1979|53|3.3|160|5.9|20|1.1|
|1980|83|2.0|101|4.8|47|3.2|
|1981|106|2.0|105|3.5|29|1.6|
|1982|98|2.1|165|7.8|133|12.2|
|1983|142|2.0|344|8.6<br>|269<br>|10.0|
|1984<br>|197<br>|2.7<br>|627<br>|7.1<br>|1281<br>|15.9|
|1985|227|2.7|672|6.5<br>|<br>11710|8.8|

Inflation: Af38 **averps.** (IFS) Seigtorge: Cailaated as chma da-tg th year of ase mney divided _by_ rnidnal GP. Fbr Artina w used M1 instead of baae money s1no this better the conapt of sei&iorge net of interest paid bazics' reservs.
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30
Appendix Chart 6 Contributions of Shocks to Output Growth (Percent of total variation) <!-- Start of picture text --> By global and domestic shocks<br>Global Domestic<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br>By structural shocks<br>Oil GS GD GMP DS DD DMP<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br>By types of shocks<br>Oil and supply Demand MP<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br><!-- End of picture text --> Sources: Authors’ calculations based on data from January 1970 through December 2023 for the G5 economies (Canada, the euro area, Japan, the United Kingdom, and the United States). Notes: Forecast error variance decompositions of domestic output growth on FAVAR models (as explained in Section IV) that consist of four global variables (global output growth, inflation, monetary and oil prices) and three domestic variables (domestic output growth, inflation, and monetary policy rates). “OP” = oil price shock, “GS” = global supply shock, “GD” = global demand shock, “GMP” = global monetary policy shock, “DS” = domestic supply shock, “DD” = domestic demand shock, “DMP” =domestic monetary policy shock, “Oil and supply” = OP+GS+DS; “Demand” = GD+DD; “MP” = GMP+DMP.

88
# **Tables**

**Table 1: Sample statistics on output and fertilizer use**

||**2004**|**2006**|
|---|---|---|
|Value of Output in Birr per ha|1,405.90|1,807.70|
|Chemical Fertilizer in Kg|42.00|55.70|
|Manure in Kg|229.00|549.30|
|Chemical Fertilizer in Kg per fertilized hectare|116.00|167.00|
|Percentage of households using:|||
|Chemical Fertilizer|68.50|63.30|
|Manure|28.10|58.00|
|Improved Seeds|35.60|30.10|

**Table 2: Method of fertilizer acquisition in 2004, (% of total)**

||**Organic Fertilizer**|**Chemical Fertilizer**|
|---|---|---|
|Cash|10.3|37.7|
|Credit|10.2|61.5|
|Own (Left over )|..|0.1|
|Land owner|13.5|0.1|
|For free|9.7|0.6|
|Own animal dung|56.4|..|

17