rafmacalaba/datause-encoder-data huggingface dataset:
to improve skills in three competency areas which are considered “foundational competencies” for employment in the business processing outsourcing (BPO) sector (Eduquity 2012): communication (oral and written), computers, and cognitive skills.

The government implemented the ACCESS Nigeria IT job skills training program in five cities in 2012 (Abuja, Enugu, Kaduna, Kano, and Lagos). Outreach efforts through university campuses and via radio advertisements in these cities encouraged applications from university graduates, particularly those finishing their final year of university and those currently doing their year of service in the National Youth Service Corps – a mandatory internship that must be completed after university but prior to formal sector employment. The ACCESS Nigeria program consisted of 85 hours of classroom-based training spread across 10 weeks. At the end of training, program participants could also take an assessment exam which had been recognized by the domestic ICT industry as a form of certification to work in business processing activities.

A consulting firm designed a curriculum based on curricula used in other countries (Egypt, India, Jordan, Philippines, Singapore, and Sri Lanka), an initial evaluation of recent university graduates in Lagos, and an assessment examination that had been endorsed by an industry consortium as certification for the sector. The government chose training providers in each of the five cities through a competitive bidding process, and training providers had to transform the curriculum into concrete lesson plans. The chosen training providers were private firms, except in Abuja, where a public sector institution won the bid. In addition to the core competencies, these training providers also were required to cover “soft” skills, such as cultural sensitivity, teamwork, and stress and time management.

Figure 1 presents a timeline of all intervention and evaluation activities. The radio advertisements and outreach activities in local universities attracted 3,018 eligible applicants to the program. ACCESS Nigeria funds paid for the training slots, with an average cost of approximately $600 per trainee. From March to April 2011, all applicants were invited to come to training centers to take a computer-based, self-administered assessment exam, the content of which heavily overlapped with the certification exam. Using this data, we generated random numbers and assigned them to applicants within strata defined by scores on the pre-assessment, as well as gender, test center location, and an applicant’s academic status (final year student in university, participant in the National Youth Service Corps, and all others). Within each stratum, the 60 percent of applicants 6
while those for 2000 are in Tables 14 through 16. These results are expressed in terms of probabilities compared to a comparison group. In this paper, the comparison group is children that go to school and do not work. Therefore, all coefficients should be interpreted as such. A negative coefficient should be interpreted as a decreased probability of a child’s working compared to a child that goes to school and does not work.

Age, gender and being indigenous do have a significant effect on schooling and work decisions in every model. Older children are more likely to work and not attend school. Older children are also more likely to be in the not working and not going to school state. Girls are less likely to go both to school and work. However, it must also be said that girls are also more likely to stay in the no-school/no-work state than to go to school only as shown by the negative coefficient for gender both in the sequential model, the first two negative coefficients in the multinomial logit model and the positive third coefficient in the multinomial logit model. This evidence supports the findings of Canagarajah and Coulombe (1997) that state that girls do more household chores.

Among the characteristics of the household head, a more educated head increases the probability of a child going to school and decreases the probabilities of working, as shown by the first coefficient of the sequential probit model. A more educated head decreases the probability of a child to go to school and work, or not to go to school and work, or not to go to school and not working, compared to children that go to school and do not work, as shown by the multinomial logit model. A married or living in free union head increases the probability of a child to go to school and not work, as shown by the sequential probit model.

Household composition is another variable that shows significance in some cases. Interestingly, an increase in the number of children under the age of 12 decreases the probability of going to school and increases the probability of working in 1997. This evidence support the quality-quantity tradeoff for children hypothesis. The number of children between the ages of 12 and 16 years is not significant in most of the cases. The 21
15 nonnegative) values of _V reinforcing_ relatively large (positive) values of _H_ and _R_ , so that the future for both China and Vietnam may be one of _rising_ income inequality. This possibility, which has already been raised by others (World Bank 2002, Ravallion and Chen 2004), leads one to wonder whether, by implementing a number of one-off inequality-reducing reforms alongside market-oriented ones, China and Vietnam have simply been able to _postpone_ the rise in income inequality that transition economies seem to experience in the move to a market economy (cf. Milanovic 1999).
struments: The main speci...cation of second-stage regression for the ZINB, the NB, the ZIP and the Poisson models is The control variables included in Zij are the standard gravity variables: the log of the gross domestic product (GDP) of the importer and the exporter, the bilateral distance between the importer and the exporter, the landlocked indicators for the importer and the exporter, the common boarder indicator, and also a dummy controlling for other types of non-border NTMs measures. The bilateral coe¢ cients of tari¤s and NTMs, �<sup>t</sup> nij<sup>and �NTM</sup> nij ; are obtained by using the interaction terms based on the importer’s share in the world market, shareni and the exporter’s share in the world market, sharenj: For log-OLS, the second-stage regression is similar to equation (16) but with the log of the quantity imported, ln (Qnij) ; as the dependent variable.

For the zero-in‡ated speci...cations, such as the ZINB and the ZIP models, we also include a logit regression to explain the presence of excessive zeros in the bilateral trade. Although it 21
income. While the survey data will have a bias that primarily affects the top of the income distribution, the bottom segment of the population might be much better represented in the survey. For simplicity purposes, suppose that all incomes below a threshold _a_ are well captured in the household survey. Similarly, for the case of tax records, only individuals with incomes above a threshold _c_ report taxes, so only this high-income population is well represented. In this case, the dataset will correctly represent the conditional distribution of income above that threshold.

Suppose now that _c < a_ . In other words, suppose that the level of income that triggers tax reporting is below the level of income above which we fear significant censoring in the household survey. Then, any income level _b_ between _c_ and _a_ ( _c ≤ b ≤ a_ ) will satisfy the following two properties: (1) all incomes above _b_ are well represented in the tax records data (since it is above the minimum threshold _c_ that triggers tax filing); and (2) all incomes below _b_ are well represented in the household survey, because censoring only occurs above _a_ , and _b < a_ .

Any such _b_ would be a valid income threshold to apply the methodology above. In practice, it is hard to know where censoring in the household survey starts ( _a_ ). This suggests that rather than viewing _b_ as a purely free parameter, one should optimally choose _b_ to minimize the potential bias due to household survey underreporting at the top of the income distribution. If tax records are representative of incomes above the minimum threshold to file ( _c_ ), the natural choice for the optimal _b_ would be precisely _c_ . In other words, since we fear that household surveys underreport at the top of the income distribution, we optimally choose the lowest possible _b_ that still ensures that incomes above _b_ are represented in the tax records. The natural choice is the income level that triggers mandatory tax filing.

This intuition about the optimal threshold _b_ and its implications for the estimation of inequality measures are the distinguishing features of the methodology presented in this paper relative to the existing literature. In particular, the methodology of Alvaredo (2011) – applied to the case of Colombia by Alvaredo and Londoño (2013) – combines an estimate of the Gini coefficient for the bottom 99 percent of the income distribution ( _G_<sup>_∗_</sup> ) with 8
**Figure 5. Concentration shares of fiscal interventions (% of total interventions per quintile)**

<!-- Start of picture text --> 100<br>90<br>94<br>80<br>70<br>60 61<br>50 56<br>40 48<br>20<br>30<br>12<br>20<br>21<br>10<br>6<br>0 1<br>1 2 3 4 5<br>Quintiles of per capita market income plus pensions<br>Direct Transfers Direct Taxes Indirect Taxes<br>Health Education MIPP<br>% of total expenditure/tax<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal.

# 3.4 Progressivity

Most direct transfers, education, and health benefits are pro-poor because the absolute value of the transfer is higher for lower-income households. Rural pensions, Bolsa Família, and BPC have the highest Kakwani indexes, meaning that they are the most progressive fiscal interventions because most of the benefits go to the bottom quintile and a smaller share goes to the top quintile (Figure 6). Both primary and preschool levels are more concentrated in low-income households. However, tertiary education benefits are progressive but not pro-poor. 32F<sup>33</sup> That is, the Kakwani coefficient is positive, but lower than as the Gini of MIPP (0.585). This is also the case of direct taxes and Abono Salarial. The social security contributions are proportional to the market income, with a slight tendency to be progressive, while the employer contributions to the pension system (Cota Patronal) and employer contribution to FGTS are proportional, leaning to be regressive.

> 33 In the CEQ methodology, a government transfer is considered regressive if the benefits are distributed more unequally than pre-fiscal income (MIPP). This concept is different from the “concentration coefficient” of a transfer that only describes the distribution of the transfer across income groups.

23
Figure 1. Frequency of “global recession” in web search and news <!-- Start of picture text --> A. Web search on “global recession” B. News articles with “global recession”<br>Index, peak popularity = 100 Thousands<br>100 5<br>80 4<br>60 3<br>40 2<br>20 1<br>0 0<br>2004 2007 2010 2013 2016 2019 1987 1991 1995 1999 2003 2007 2011 2015 2019<br><!-- End of picture text --> Sources: Factiva, Google, World Bank.

Note: Panel A shows monthly index numbers that represent search interest for the term “global recession” relative to the highest point (i.e., peak popularity for the term, equal to 100). The information is obtained via Google Trends on January 22, 2020, with the following features: worldwide coverage, all categories, and web search. Last observation is December 2019. Panel B shows total numbers of articles (in English) in Reuters News that carry the term “global recession” during each year. A total of 17,084 articles are found in Factiva over 1987-2019 and reported as annual data. Shaded areas indicate global recessions in 1991 and 2009, and global downturns in 1998, 2001, and 2012. The information is as of January 22, 2020.

30
Section 4, we implement our methods in a large-scale randomized communication campaign, we describe the administrative data used in the analysis, the empirical strategy, and evidence of direct and spillover effects. Section 5 provides some practical recommendations for designing and analyzing partial population experiments. Section 6 concludes.

# **2 Why is Cluster Heterogeneity Important?**

We consider a population where units are grouped into mutually exclusive and independent clusters. Common examples of this type of clustering are students in schools (Miguel and Kremer, 2004; Beuermann et al., 2015), family members in households (Barrera-Osorio et al., 2011; Foos and de Rooij, 2017), job seekers in local labor markets (Cr ́epon et al., 2013), employees in firms or organizations (Duflo and Saez, 2003), or households in neighborhoods, villages or other geographic administrative units (Angelucci and De Giorgi, 2009; Ichino and Sch ̈undeln, 2012; Haushofer and Shapiro, 2016; Gin ́e and Mansuri, 2018). In our application, a local property tax reminder information campaign, the population of interest consists of taxpayers in residential blocks. Within this population, we study an experimental design where treatment assignments can vary both between and within clusters.

Figure 1 shows the distribution of cluster sizes in six partial population experiments, including our analysis sample and five published papers (Cr ́epon et al., 2013; Gin ́e and Mansuri, 2018; Haushofer and Shapiro, 2016; Ichino and Sch ̈undeln, 2012; Imai, Jiang and Malani, 2021). The figure reveals substantial variation in cluster sizes. When cluster sizes are heterogeneous, it is likely that the distribution of outcomes will vary across clusters as well. For instance, one may expect the mean and the variance of the outcome to be different in large clusters compared to small clusters. We refer to the variation in outcome distributions across clusters as _distributional heterogeneity_ .

Intuitively, with heterogeneous clusters, the variance of an estimator of interest _β_<sup>ˆ</sup> , such as a difference in means between units in treated and untreated clusters (we define the estimators of interest precisely in the next section), can be decomposed into four parts:

|+ clustering with equally-sized clusters|(2)|
|---|---|
|+ cluster size heterogeneity|(3)|
|+ cluster distributional heterogeneity|(4)|

The first term is the variance that would be obtained if observations were uncorrelated within clusters. The second term is an adjustment factor that accounts for the within-cluster correlation, often known as the “design effect” or the “Moulton factor” (after Moulton, 1986) that depends on the average cluster size. The term in the third line represents the additional variation due to the heterogeneity in cluster sizes, 5
Table 4. VECM estimates for rural and urban wage models

|Regression Number|1|2|
|---|---|---|
|Wage series included|Rural wages|Urban wages|
|Number of observations|37|37|
|R-squared: wage regression|0.58|0.45|
|R-squared: rice price regression|0.51|0.48|
|Short run coefficients:|||
|Wage regression|||
|Cointegration term, lag 1|-0.005 (0.035)|-0.04 (0.04)|
|Rural wages, lag 1|0.15 (0.159)|0.10 (0.17)|
|Rice prices, lag 1|0.14** (0.058)|-0.02 (0.05)|
|Rice price regression|||
|Cointegration term, lag 1|0.33*** (0.035)|0.38 (0.11)|
|Rural wages, lag 1|0.15 (0.159)|-0.31 (0.54)|
|Rice prices, lag 1|0.135 (0.058)|0.63*** (0.114)|
|Cointegrating relationship|||
|Wages|1|1|
|Rice prices|-1.11*** (0.13)|-0.78***|
|Constant|-1.56|-2.92|

_Source_ : Author’s estimates from Bangladesh Bureau of Statistics (BBS 2014).

_Notes_ : See text for definitions of the variables. *, **, and *** indicate significance at the 10%, 5% and 1% levels respectively.

These results should be interpreted with some caution—certainly they are still relatively imprecise and almost certainly lack broader external validity—but they have several important implications. First, the conclusion that the long-run rural wage-rice price elasticity is approximately equal to unity is similar to the conclusions of Jacoby (2013) for India, Lasco et al. (2008) for Bangladesh, and Ivanic and Martin (2014) for a broader range of countries. Second, the short-run food price elasticity is quite low (0.14 to 0.19), though our use of higher frequency data reveals that the response is relatively quick: wages start to change in the next quarter. Third, consistent with Headey et al.’s (2011) results for Ethiopia, urban wages might adjust to higher food prices quite strongly in the long run but they show no substantial adjustment in the short run. This further suggests that rural and urban labor markets may be quite weakly integrated, perhaps because of low levels of short-term mobility between rural and urban areas.<sup>17</sup> In summary, wage adjustments seem to provide some compensation for rural laborers—and relatively quickly at that—but it would appear that agricultural supply responses are likely to be the main explanation of the poverty-reducing effects of higher food prices.

> 17. This is in contrast to the assumption in Ivanic and Martin’s (2014) model, in which rural and urban labor markets are perfectly integrated.

18
# SUMMARY:

# CHECKLIST OF LESSONS LEARNED ABOUT TARGETED FIELD SURVEYS IN PSAs

# I. Why use targeted field surveys?

- A. To obtain the perspective of businesses confronting constraints and their perceived need for support.

- B. To understand the impact of and interaction between policies in their institutional environment.

- C. To measure costs, benefits and other magnitudes that require empirical data known by firms.

# II. How should surveys be focused and designed?

- A. Surveys should be used to fill gaps in knowledge and add the firm-level perspective. By their nature, they are not long enough to cover all issue areas in depth. Consult previous work and operational staff (and where relevant, Ministerial staff) to identify priority areas where information is lacking.

- B. Choose those industries and survey sites that are most likely to shed light on important issues. The breadth and depth of the sample population -- in terms of the number of firms, sectors and locations -- is highly dependent on the resources available to you for the study.

- C. Surveys should focus on individual firm experience of constraints and costs.

- D. The survey should be open-ended enough to allow unforeseen responses. Questions should never be leading in either wording or context.

- E. To the extent that you want to compare your study with other country studies, replicate their questions to facilitate comparisons.

# II. What types of questions work best in targeted firm-level surveys?

- A Short answer, open-ended, multiple choice and ranking questions each play an important role in surveys.

- B. Major categories of inquiry should begin with open-ended questions, before other questions can bias responses.

- C. Most questions should be either multiple choice or require a quantitative response -- to reduce ambiguity and facilitate data coding, processing and analysis.

16
|5|0.146|2.470|11.492|4.210|74.999|6.828|
|---|---|---|---|---|---|---|
|6|0.147|2.481|11.591|4.243|74.776|6.909|
|7|0.147|2.479|11.560|4.278|74.575|7.108|
|8|0.147|2.518|11.550|4.315|74.520|7.096|
|9|0.148|2.549|11.556|4.325|74.306|7.265|
|10|0.148|2.550|11.551|4.327|74.253|7.319|

Source: Author’s calculations Notes: GAP_DR_BK is the output gap for the Dominican Republic, GAP_HT_BK the output gap for Haiti, DLINFL_DUS_DR the inflation differential with respect to the US for the Dominican Republic, DLINFL_DUS_HT the inflation differential with respect to the US for Haiti, and GAP_US_BK the output gap for the United States. The Choleski ordering of the variables is as described in the text.

The results indicate that at a horizon of 10 periods, fluctuations in the output gap in the Dominican Republic are explained mostly by its own innovations (more than 80 percent). This is less so for Haiti, with own innovations accounting for about 70 percent, and innovations in domestic inflation accounting for about 21 percent of output gap fluctuations. In both countries fluctuations in inflation are accounted for mostly by their own innovations (about 80 percent), with a somewhat more significant effect of the domestic output gap in the case of the Dominican Republic. In neither case does the US output gap play a significant role in domestic fluctuations. There are also no significant cross-country effect between the Dominican Republic and Haiti. This could reflect the fact that economic fluctuations in both countries are subject to large temporary supply shocks; although these shocks can be correlated (as is the case with hurricanes), this is not always the case.

We examine next the impulse response function to an innovation in the US output gap and the response functions of domestic variables in response to shocks in the other country. The objective is to examine if and how each country’s domestic macroeconomic variables respond to the same external shock, and if and how they respond to shocks in the other country. To the extent that both countries respond in a similar way to the first shock (that is, by displaying a high degree of symmetry), are better potential candidates for a CCA. Moreover, the speed of response matters also; in general, the slower is the adjustment after disturbances, the larger will be the cost of maintaining a single currency.

28
**Figure 3:** **_Adequate Housing Index_ and Slum Population**

<!-- Start of picture text --> CRIEGY TUNTURMAR ROU ARGDOMMEXBRA UKR BLR UZB<br>VNM COL PER<br>ZAF IDN IRQ<br>GAB<br>GHA IND PAKNICPHL<br>LAO GMBSEN CMR BGDAGONPL CIV<br>RWA KENCOG NGAMMR DJI SDN<br>TZA BDIUGA TGOZMB BEN MWIHTI<br>MLI GIN BFA<br>SLE ETH MOZ<br>GNB COD<br>MDG<br>TCD<br>NER<br>0 20 40 60 80<br>Slum population in % ( UN - Habitat 2018 )<br>Source: UN Habitat<br>1<br>.8<br>.6<br>Adequate Housing Index .4<br>.2<br><!-- End of picture text -->

**Note** :  Relationship between the _Adequate Housing Index_ and urban slum population. The correlation coefficient is - .83. (Source: Authors’ calculation based on countries’ latest available household consumption and expenditure survey and UN-Habitat 2018).

# **_4.2 AHI – Within-Country Estimates_**

As the _AHI_ is micro-founded it can be disaggregated along segments of the population within countries. In this section, we present disaggregated estimates of the _AHI_ focused along several key dimensions.

## 4.2.1 AHI – Disaggregated by Household Income

In all 64 economies, adequate housing is often not delivered to the lower end of the income spectrum. With growing population and urbanization rates in the next decade, this issue will become even more acute. To reflect within-country differences in housing adequacy, we calculate the Gini coefficient of adequate housing per country, which reflects inequality in access to adequate housing (Table 2). The Gini coefficient is frequently applied in economic literature to assess the state of income inequality. Over time, the methodology for estimating the Gini coefficient has been extended beyond income and has been applied to measure the state of inequality in other areas such as education or human capital (Földvári & van Leeuwen 2011). In the housing literature, the Gini coefficient has been applied to the context of housing affordability (e.g., Ben-Shahar & Warszawski 2016; Buckley & Gurenko 1997; Landis et al. 2002). We propose and compute a Gini coefficient of housing adequacy based on the distribution of the _AHI_ .

In many emerging economies, HHs earning below median income have a significantly lower likelihood to live in adequate housing. Niger exhibits the largest adequate housing inequality in emerging economies reflected in a housing adequacy (Gini coefficient of 0.39). In Niger’s bottom half of the income spectrum, virtually every housing unit lives in an inadequate housing unit. Within the highest income decile, only 49 percent of HHs are likely to have access to adequate