rafmacalaba/datause-encoder-data huggingface dataset:
The nested counting index is well suited for dichotomous variables and its simple aggregation method – a weighted mean – is easy to interpret.<sup>20</sup> Yet, while each dichotomous variable represents a separate test or target, some tests may be related, giving rise to additional possibilities for variables and aggregation. For example, two distinct questions might refer to the system of national accounts being used by the NSS, where there are two levels of standards in common use. A first variable might ascertain whether the NSS is at least using the lower standard; the second might determine whether the higher standard is being employed. The three feasible combinations for the two variables are: (0,0), which indicates that neither system is being employed; (1,0), which indicates that the lower and not the higher standard is being employed; and (1,1), which indicates that the higher standard system is being employed.<sup>21</sup> It is possible to combine the two related variables into a single, non-dichotomous variable that would assign unique values to the three levels, say 1 to the higher outcome (1,1), some numerical value λ between 0 and 1 to the lower outcome (1,0), and 0 to the outcome where neither standard is in place (0,0). Constructed variables like this are commonly used in measurement exercises. Yet, assigning an arbitrary numerical value λ to a variable does not by itself make the variable cardinally meaningful or comparable to other variables. Consequently, the resulting index and country rankings will simply not be robust to admissible changes in values.<sup>22</sup>

> 20 Notably, other options exist for aggregating dichotomous variables, but these have limitations. For example, one option is the class of general means (or means of order α), which includes the geometric mean as a special case. Since variables here have 0 values, only general means with positive parameter α are applicable (the geometric mean is also problematic in that it takes on a value of 0 whenever any variable is 0 and thus violates monotonicity). Furthermore, a general means would rank countries the same way but lose the useful decompositions and simple interpretation of the index value. Another option is a dashboard approach, where disaggregated variables are presented in their natural state. Yet, this approach does not offer easy interpretation, especially in our case where the number of variables is large. 21 The outcome (0,1) would not be observed in this case of related variables, since achieving the higher level entails achieving the lower level.

> 22 Both cardinality and comparability can be defined in terms of admissible changes in the scales used to represent variables.

20
Table 7: Effect of Family Planning Access on Recent Birth or Pregnancy for Women Without Schooling

||Birth within last 12 mon<br>OLS|ths or current<br>IV Model<br>Plac|ly pregnant<br>of Predicted<br>ement<sup>a</sup>|
|---|---|---|---|
||Model I<br>Model II|Model III|Model IV|
|Family planning|_−_0_._063<sup>_∗_</sup>|_−_0_._071||
||(0_._034)|(0_._059)||
|Family planning_×_age 15-19|0_._095||0_._173|
||(0_._151)||(0_._170)|
|Family planning_×_age 20-24|0_._189<sup>_∗∗_</sup><br>(0_._074)||0_._304<sup>_∗_</sup><br>(0_._173)|
|Family planning_×_age 25-29|_−_0_._110<sup>_∗_</sup>||_−_0_._030|
||(0_._065)||(0_._156)|
|Family planning_×_age 30-34|_−_0_._114<br>(0_._075)||_−_0_._140<br>(0_._107)|
|Family planning_×_age 35-39|_−_0_._168<sup>_∗∗_</sup>||_−_0_._301<sup>_∗∗∗_</sup>|
||(0_._075)||(0_._086)|
|Family planning_×_age 40-45|_−_0_._029||_−_0_._056|
||(0_._056)||(0_._077)|

**Notes.** * sign. at 10%; ** sign. at 5%; *** sign. at 1%. Linear probability model with robust standard errors clustered at community level in parentheses. Family planning indicates whether there was family planning available within 40 km in 1990. Additional variables not shown are region dummies, ethnic group dummies, five year age group dummies, dummies for religion, area of wereda, rainfall and rainfall squared of wereda, dummy for urban area, dummy for market in area, and dummies for road access all year and road access only during dry season. Number of observations for all models is 1021; sample consists of women without education who are between 20 years and 39 years of age. Complete results including other explanatory variables are available on request. a Weighted IV estimation using Stata’s svy command with family planning access treated as endogenous.

woman has either had a birth within the last 12 months or is currently pregnant. As for Table 6, the results show the difference between having access to family planning for a substantial period of time compared with only having access for a relatively short period of time or not at all. In the OLS estimation, the average effect is negative and statistically significant. The IV results indicate that a woman with long-term access to family planning is around 7 percentage points less likely to have had a birth within the last 12 months or be currently pregnant compared to a woman with short-term or no access to family planning. The average effect masks substantial differences across age groups. For women younger than 25 access to family planning _increases_ the chance of a recent birth or pregnancy; the OLS and IV effects for women 20 to 24 are statically significant. For older women the effect of access is negative. The IV results show large reductions in the probability of a recent birth or pregnancy with women 30 to 34 are 14 percent less likely and women 35 to 39 are 30 percent less likely with access to family planning. These results are consistent with compression of fertility into a substantially shorter period of time with long-term access to family 21
19 The priority problem lies not in designing futuristic software bLt in improving the quality of data available for model testing and implementation. By means of touchscreen ATMvs and cartoon characters, even illiterate customers can be helped to fill out loan applications electronically. Expert systems constitute a change in technology that lessens rather than increases the skill demands being placed on loan officers and at the same time makes their job performance and instances of corrupt and fraudulent behavior easier for supervisors and examiners to monitor.

Besides tracking changes in risk, evidence of changing default probabilities can also be used to set up a market-based approach for establishing a watchlist and reserving for loan losses. Whether used to automate loan decisions or merely to monitor them, credit-scoring models can be used for timely identification and prompt downward or upward revaluations of problem loans. Updated data on borrower attributes can be used to track the changing probability of default fir a loan has been made. While post-loan revaluation is still at an early stage of development, American Management Systems, Inc. has developed what it calls a Strata software system that uses scoring models to predict a customer's disposition to pay back a delinquent loan.

Outside Monitoring oVa Bank'q **Credit Anal si** Credit analysis may be conceived as a management system that conducts statistical and sensitivity analyses of the credit-application database. These analyses have two principal objectives: to calculate applicants' repayment probabilities and to value the lending opportunities being offered to the bank. In every loan made, managers should bc able to identify the risks in the deal and how much the bank is being paid for bearing these risks. Each contract must be structured to offer a risk-return combination that makes the probability of repayment and the loan interest rate high enough to promise a reasonable total profit on the loan.

Outside stakeholders need to monitor and second-guess a bank's risk-classification and pricing procedures. Bank examination is a process that looks to test a bank's information and management systems for flaws. The first requirement of effective supervision is access to the data and assumptions from which the bank calculates repayment probabilities and asset values.

Even in G-1O countries, supervisory technology falls far short of technological possibilities. Increasingly, in these countries the data and assumptions an examiner needs
**Policy, Rosarch, and External Affairs**

# **Financial Policy and Systems**

WPS 595 This paper- a product of the Financial Policy and Systems Division, Country Economics Departmentis part of a larger effort in PRE to analyze sector reformn in socialist economies in transition. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Zena Seguis, room N9-005, extension 37665 (26 pages, including figures).

Financial reforms in forrnerly centrally planned economies take a different form than in market economies because they imply not only liberalizing the system but also reshaping the structure and functioning of financial markets. And the reforms must be designed to facilitate the conduct of monetary policy under rapidly changing economic circumstances. To fulfill this role, financial reforms should (1) provide the authorities with monetary policy instruments that contribute to short-term stabilization and (2) provide the incentives for inducing a more efficient intermediation of savings through the financial markets.

Hungary has made substantial progress, they conclude, but macrofinancial indicators suggest thai administrative and technical obstacles remain and that supporting measures must be deepened. Four steps in particular are needed: c The ability of the monetary authority to conduct monetary polic) must be enhanced.

* The operating and financial condition of financial intemiediaries must be improved.

> * Healthy competition among financial intermediaries must be encouraged.

In uhis context, Blejer and Sagari identify the main tasks and targets of financial reform and comment on the key developments of the Hlun_arian process.

> * A prudential regulatory framework that does not discriminate against the development of a securities market must be established.

The l'RE Working Paper Series disseminates the Findings of work under wasa in the Bank's Policy, Rcsearch, and Extemal Aff airs Complex _._ An otjectiv cof the scrics is to get these findings ,Jt quickly, cv en ifpresentations arc Icss than fully polished. The findings. interpretitions, and conclusions in thesepapersdonotnecessarily reprcsent official Intemational Monetarv Fund or World Bank polkcy.

> Prediw-'d h\ the l'RE Di)sserinmiton CCTIter
Tuungane was implemented with a variation in treatment, which allows us to explore, in addition to the program’s overall effect, the impact of having women in leadership positions. Fourth, Congo provides a good setting because CDD programs often take place in the context of weak state capacity.

We collected outcome data in 735 of a targeted 781 villages, which makes this one of the largest CDD field experiments to date. As a comparison, Fearon et al. (2009) examine 83 villages, Casey et al. (2013, 2019) survey 236 villages, Beath et al. (2013, 2015) study 217 villages, while Avdeenko and Gilligan (2015) investigate 24 communities. This study builds on data from village chief and household surveys and a carefully-designed facility audit to explore the longer term impacts of the Tuungane program on eight outcome families that we pre-registered prior to data collection: service provision (in the health and education sector), health, education, economic welfare, women’s empowerment, governance, intra-village and inter-village social cohesion.<sup>4</sup> We find evidence for the physical endurance of infrastructure built by the program. Treatment villages are served by primary schools and hospitals that have higher quality infrastructure, and their hospitals are better stocked. However, we find no evidence that the program had an impact on other dimensions of service provision, health, education, economic welfare, women’s empowerment, governance, and social cohesion. These findings are broadly consistent with findings from Casey et. al (2019); the only other study, we know of, that investigates the longer term impacts of CDD programs. Thus, while CDD programs appear to effectively deliver public infrastructure in the short and longer term, their impact on economic development and social transformation appears to be limited, even in the longer term.

The remainder of this paper proceeds as follows. Section 2 discusses the Tuungane program and the experimental design. Sections 3 and 4 discuss the data collection and present results, respectively. Section 5 concludes.

# **Field Experiment in Congo**

The field experiment was designed around the Tuungane program, which was implemented between July 2007 and December 2012 in 1,250 villages throughout Eastern Congo (Appendix A). With, on average, 1,424 inhabitants per village, the program reached a beneficiary population of approximately 1.8 million people.

> 4 For the conceptual frameworks of how CDD may affect each outcome, we refer the reader to the earlier

5
# **2. Data**

The data used in this study are drawn from the national household survey (the _Encuesta Nacional de Hogares_ , ENAHO) carried out by the Peruvian _Instituto Nacional de Estadística e Informática_ (INEI). The ENAHO is an invaluable source of information which in 2009 covered more than 20,000 households. The survey collects extensive data on socioeconomic information as well as labor force activities, including whether the members of the households work in the public or the private sector, both formal and informal.<sup>4</sup> Based on 2009 survey, 15,613 employees provided information on their salary and the sector where they work (public or private). In particular, wage data are available for 3,670 public sector workers and 11,943 private sector workers (see Table 1).<sup>5</sup> Only one-third of these private sector workers have a formal contract (4032 private sector employees have a formal contract).

In order to analyze the differential between public sector and private sector pay, compensation has been computed by aggregating wages, in-kind payments, and bonuses. In addition, we focus on hourly compensation rather than annual earnings. As discussed by Blinder (1976) and Blomquist (1978), the estimation of an earning function based on annual earnings may confound pay differences and issues related to the amount of labor supplied. Based on descriptive statistics in Table 1, public sector pays higher wages than the private sector (8.81 vs 4.34 Nuevos soles per hour). Also total hourly compensation is higher in the public sector (9.38 vs 5.29 Nuevos soles).<sup>6</sup> However, caution is needed to interpret these results because of the different characteristics among workers in the two sectors and the fact that the private sector includes both formal and informal workers.

Table 1 also displays data on education attainment, age, marital status and whether workers live in an urban or rural location. Data shows that public sector employees achieved, on average, higher levels of education. The average age in the public sector is 41 years. Workers are younger in the private sector (32 years). The percentage of married employees and female employees is higher in the public sector.<sup>7</sup>

> 4 In 2010, ENAHO won the first prize of the World Bank Award to the Innovation in Statistics.

> 5 Public and private sector workers are defined based on their responses to the ENAHO (question #510). Different classifications have been used to test for the robustness of the compensation differential analysis (i.e., including/excluding soldiers, public company employees, special service firms (SERVICE) employees). Results are qualitatively the same.

> 6 It is important to note that private sector employees work longer hours. Because of this, take-home pay might be higher for private sector employees even if hourly compensation is lower.

> 7 Two tables summarizing descriptive statistics for the female (Table A.1 – Panel A) and male population (Table A.1 – Panel B) are provided in Annex 1.

4
groups. Low, medium, and high skilled workers therefore all seem to experience a reduction in migrant numbers when GDP falls and increase when it rises.

Panels B and C of Table 4 examine the responsiveness of median and mean wages respectively to GDP by skill quartile. Again we cannot reject equality of coefficients across the four skill categories at conventional skill levels and find point estimates which are mostly small in magnitude and statistically insignificant. An exception is the second quartile, in which we see a significant negative coefficient on median wages of -0.31, and a similar-sized, but statistically insignificant coefficient on mean wages. This suggests wages for individuals in this skill range may actually fall when economic conditions at destination improve, although if we control for multiple hypothesis testing by multiplying the p-values by the number of separate outcomegroup results being tested here for wages, then this result also would not be significant.

# **4.4 Does who migrates change over the business cycle?**

An alternative explanation for our results could be that the selection of who migrates is changing over the business cycle. In particular, in a market-clearing model with wages falling in a recession, we could observe in our data a reduction in the quantity of individuals migrating with no change in mean wage paid to migrants if low-skilled, lesser-paid, individuals experience more of a reduction in migrant numbers than higher skilled individuals do during recessions. Indeed Solon et al. (1994) show that such a change in composition leads aggregate wages in the U.S. to be less pro-cyclical than indicated by longitudinal microdata.

We have shown above that our results are robust to controlling for occupational categories, and that we cannot reject that the elasticity of migrant quantities to GDP changes at destination is constant across skill quantiles. Nevertheless, as a further check, we use the Survey of Overseas Filipinos to directly examine whether the observable characteristics of who is migrating varies over the destination business cycle.

The Survey of Overseas Filipinos is an annual survey which asks a nationallyrepresentative sample of households in the Philippines about members of the household who left for overseas in the past five years (see Yang, 2008). Since it is remaining members of the household who are reporting on the absent migrants, only basic details of the characteristics of 16
2 slightly above the average number of projects (7.4). The FY88 program reflects efforts to increase lending activities in Sub-Saharan Africa: five of the projects were in Africa, two in Asia and one in Latin America. Five operations were in countries borrowing for the first time in the PHN sectors (Burundi, Ethiopia, Guinea, Uganda and Sri Lanka). Two population projects (in Kenya and India) included substantial support for non-government efforts in family planning, reflecting an increaued effort to involve the private sector in PHN projects.

Table 1: **PHN LERDING FOM FnB** Commitment in US$ millions

|Country|Project|Am|ount|
|---|---|---|---|
|**AFICA**||||
|Burundi|Population & Health|14.0|(IDA)|
|Ethiopia|Family Health|33.0|(IDA)|
|Guinea|Health Services Development|<br>19.7|(IDA)|
|Kenya|Third Population|12.2|(IDA)|
|Uganda<br>**ASIA**|First Health|42.5|(IDA)|
|India|Fifth Population|57.0|(IDA)|
|Sri Lanka|Health & Family Planning|17.5|(IDA)|
|**IAC**||||
|Brazil|NE Endemic Disease Control|109.0|(IBRD)|
|TOTAL||304.9||

Table 2: PH3 LENDING PROGRAO, Fm83-87 Commitment in US$ millions (Number of projects approved)

|Region|F|Y83|FY|84|F|Y85.|FY|X86|..F|Y87<br>_._|Avera|ge 83-87|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|AFRICA<br>ASIA|22<br>27|(2)<br>(1)|31<br>155|(3)<br>(2;|65<br>85|(3)<br>(2)|82<br>242|<br>(5)<br> <br>(4)|31<br> <br>0|(4)<br>(0)|<br>46<br>102|(3.4)<br>(1.8)|
|EMENA|37|(3)|<br>0|(0)|43|(2)|<br>0|<br>(0)|13|<br>(1)|<br>19|(1.2)|
|IAC<br>TOTAL|34<br>120|(1)<br>(7)|58<br>244|(1)<br>(6)|_ 0<br>193|(0)<br>(7)|96<br>420|<br>(2)<br>(11)|10<br>54|(1)<br>(6)|40<br>207|(1.0)<br>(7.4)|
concordances (Muendler 2002) to measure the intensity of technology use across over 100 Brazilian industries, based on the U.S. data. As with the O*NET data, our use of U.S. data assumes that while the levels of technology-intensity may be different for a given industry across the two countries, the ranking of industries by their information technology intensity is not different. The data report that the “electronic computer manufacturing” and “peripherals manufacturing for data processing equipment” rely most heavily on technology, with over 85 percent of workers reporting using a computer at work. “Farm machinery and equipment manufacturing,” “paper manufacturing,” and “paperboard containers and coated paperboard manufacturing” measure the least technology intensive industries, with less than 30 percent of workers reporting use of a computer at work.

Returning again to Table 3.3, we report the average non-routine-to-routine relative task intensity, conditioning on whether the industry is technologically-intensive (as defined by the median value). Perhaps unsurprisingly, tech-intensive industries consistently employ higher shares of workers in non-routine tasks. The difference is increasing over time—techintensive industries employ relatively greater shares of non-routine skills over time as compared to non-tech-intensive industries. Our empirical strategy exploits both this demand side use of technology as well as the supply side access to technology to identify the technology shock.

# **3.4. Enforcement data**

Changes to the Brazilian Federal Constitution labor law in 1988 were very favorable to workers.<sup>12</sup> Following these changes in the labor code, the cost of labor to employers increased. First, the employer’s payroll contribution increased from 18% to 20%. Second, the penalty on the plant for dismissing the worker without cause increased from 10% to 40% of the total contributions to the severance fund, _Fundo de Garantia do Tempo de Serviço_

> 12 First, it reduced the maximum weekly working period from 48 to 44 hours. Second, it increased the overtime wage premium from 20% to 50% of the regular wage. Third, the maximum number of hours for a continuous work shift dropped from 8 to 6 hours. Fourth, maternity leave increased from 3 to 4 months. Finally, it increased one month’s vacation time pay from 1 to 4/3 of a monthly wage.

17