rafmacalaba/datause-encoder-data huggingface dataset:
poverty almost identical to module 2 – 55.1 percent – while the other recall modules report poverty headcounts all over 60 percent, with module 4, the collapsed list, yielding a poverty estimate of 66.8 percent. Poverty numbers derived from household diary figures also record substantially higher poverty levels than the benchmark – the infrequent household diary records the poverty rate at 55.6 percent and the frequent diary at 59.5 percent.

The poverty headcount is attractive for its ease of understanding, but is also notorious for its implied welfare discontinuity at the poverty line as well as the inability to measure the extent or depth of poverty (Ravallion, 1996). Figure 1b presents two additional poverty measures, the poverty gap and the squared gap measures, that give greater weight to the poorest households among those below the poverty line. The overall message in Figure 1b is the same as Figure 1a. The lowest poverty figures are for the benchmark module 8 while the highest poverty measures are observed in recall modules, especially modules 1, 4, and 5. The ranking of poverty scores varies somewhat by poverty measure. While the 7-day full recall module 2 had the second lowest headcount estimate (54.9%) followed by module 3 (55.1%) and then module 7 (55.6%), module 7 has the lowest poverty gap measure (18.9%) followed by module 2 (19.1%) and then module 3 (21.4%). The largest proportional differences across module are apparent in the squared gap measure where the highest estimated squared poverty gaps, those for modules 4 and 5, are more than twice as large as the benchmark squared gap measure. The changes in module rankings across the different poverty measures, as well as the greater proportional differences in the squared gap measures compared with the poverty headcount, reflect the differing degrees of inequality below the poverty line measured by the various modules.

Clearly poverty measures are highly sensitive to the mode of consumption measurement and care must be taken when poverty studies combine measures of consumption poverty, either across countries or within a country but over time, derived from differing survey designs. Another important goal of poverty measurement, however, is to identify the characteristics of poor households in order to inform the targeting of social policy or to better understand poverty determinants. Hence an equally important consideration, other than whether survey design influences estimated poverty levels, is whether the mode of survey design influences the identified characteristics of the poor. We approach this question through a simple regression framework similar to equation (2) where, using a linear probability model, we regress the household specific poverty indicator on module type interacted with household characteristics. The results, in Panel A of Table 8, again adopt the $1.25/person/day line used in Figures 1a and 1b. A complementary approach to the same general question of the relative rankings of households is to utilize the full information of the continuous consumption distribution, as opposed to the binary poverty indicator,
# **VI. Channels of change in the distribution of regional intersectoral productivity gaps**

To further identify potential channels behind the distribution of regional intersectoral productivity gaps, we examine the change in this distribution and the relationship between the change and the pattern of structural transformation in Ghana since the early 2000s. The analysis here examines the intersectoral (informal-formal) productivity gap in industry and in services.

As discussed in Section III.C, the intersectoral productivity gap averaged across the five richest regions is three times larger than that for the five poorest regions. Refining further, the intersectoral productivity gap in industry averaged across the five richest regions (180.8 thousand cedis per worker) is also roughly three times larger than that for the five poorest regions (66.8 thousand cedis per worker) (Figure A2). On the other hand, the intersectoral productivity gap in services averaged across the five richest regions (55 thousand cedis per worker) is about 1.6 times larger than that for the five poorest regions (32.8 thousand cedis per worker). These numbers suggest that the difference in the intersectoral productivity gap between the richest and the poorest regions of Ghana is driven by the corresponding difference in industry.

We cannot examine the change in the distribution of regional intersectoral productivity gaps over time in industry and services because the previous rounds of establishment censuses in Ghana only surveyed industrial establishments. As a second-best option, we examine the change in the distribution of regional intersectoral productivity gaps only in industry, by comparing statistics from the National Industrial Census (NIC) 2003 to those from IBES 2014, a period of roughly a decade.<sup>21</sup> In doing so, we can only obtain a partial picture of the change in the distribution of regional intersectoral productivity gaps in the nonfarm sector. To allow for a comparison of statistics between 2003 and 2014, we redefine some key variables. Specifically, an informal enterprise is redefined as an enterprise that is not registered with RGD, labor productivity is redefined as revenue per worker (given the absence of value-added data for enterprises in the NIC 2003 data), and the intersectoral productivity gap is redefined as the log of the ratio in labor productivity between the informal and formal sectors (given the absence of sector- and enterprise- specific price deflators over time).

We do not see any clear pattern in the change in the distribution of intersectoral productivity gaps between the richest and the poorest regions (Figure 13). Except in Northern,

> 21 See GSS (2006) for further information on the design and implementation of the National Industrial Census 2003.

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At the upstream service provider level, the hypotheses to be tested are: _H3: The presence of competition-related pressure is the most significant factor associated with the development of innovative road freight service offerings._ The analysis will attempt to distinguish the relative importance of different sets of factors in affecting the provision of innovative business services, in particular the most appropriate measure of intensity of product-level competition relative to other factors such as access to finance.

_H4: Larger, internationally-connected service providers are more able to offer or respond to innovative business user requests than smaller, locally-based enterprises._ The analysis will try to disentangle the relative importance of foreign ownership, enterprise size (only large providers, mix of large and small), the legacy of state ownership, provision of exclusively local, nation-wide and/or international haulage services, and factors such as technology and timing (older firms benefiting from first mover advantages versus recent entrants) in stimulating user-related innovation.

# **3. The enterprise-based surveys and data description**

The survey instruments were implemented in summer 2000 and during the subsequent months to complete all sectoral quotas. The surveys are cross section, though they ask detailed questions not only about the latest full calendar year (end 1999) but also often comparable values for 1997 to allow calculation of changes over three years. By sampling design, the objective was to administer the provider questionnaire to 50 road freight logistics providers in each of the Czech Republic and Hungary and to 60 providers in Poland as the largest economy of the three, with quotas to randomly select an appropriate number of enterprises in each size category to roughly match corresponding population ratios.<sup>6</sup> The only other deviation from full randomization was a requirement to try to include a minimum number of foreign-owned enterprises (company’s or mother company’s headquarters located in another country) for each of the industrial sectors, in order that a sufficient number of enterprises with linkages to international best practices be represented.<sup>7</sup> A similar approach was taken for the user enterprise questionnaire, with the objective of surveying 50 food processing, 50 food distribution and 50 auto production enterprises in each of the Czech

> 6 For the road freight provider sampling, the size quotas were biased towards larger enterprises given the overwhelming number of small operators in the population.

> 7 In the end, this foreign-ownership requirement was in any case not met for providers in the 3 countries and for food processing and distribution industries in Poland as very few locally-based enterprises in the population in these industries have their company’s or mother company’s headquarters in another country.

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**Figure 6.  Projected Youth Population with and without AIDS Population Aged 10-24 (1980=100), U.N. Medium Variant Projection and "No AIDS" Projection**

<!-- Start of picture text --> 500<br>Zambia - No AIDS<br>450<br>Zambia - AIDS<br>Botswana - No AIDS<br>400<br>Botswana - AIDS<br>South Africa - No AIDS<br>350<br>South Africa - AIDS<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050<br>Year<br>Population (1980=100)<br><!-- End of picture text --> 32
Some depositors may consider that they will have early warning of a suspension and choose to earn the risk premium as a form of carry trade.

The interest premium on hyphenated dollars varies within each country over time and very considerably as between different countries. Interest rate data is not as plentiful as that on the volume of dollar deposits, but there is sufficient to allow some general statements. Interest premiums have declined on average, but in every year since 1990 there has been at least one country with an interest premium in excess of 800 basis points. At 2004, interest rates reported to IFS on US dollar deposits ranged from 0.25 percent per annum (Argentina) to 8.71 percent (Azerbaijan) (at a time when onshore US CDs were yielding 1.5 percent).<sup>12</sup> Figure 9 suggests a small positive correlation between dollarization rates and the USD deposit interest rate at that date.

<!-- Start of picture text --> Dollarization and USD deposit interest rate, 2004<br>10<br>9<br>AZE<br>8<br>7 UKR<br>MDV<br>6 GEO<br>MDA<br>5<br>VCT<br>4<br>NIC<br>3<br>2<br>BOL<br>1 URY<br>0<br>0 20 40 60 80 100<br>Deposit dollarization %<br>USD deposit interest rate %<br><!-- End of picture text --> Figure 9: _Dollarization rates and USD deposit interest rates, 2004_ Source: Dollarization rates as in note to Fig 3; interest rates IFS line 60L.F.

Modelling determination of national interest rates in such different countries over a long time period is an elaborate exercise which is not attempted here (cf. Honohan, 2001). However, simple regressions (with autoregressive disturbance) on the data pool of some 1300 observations do suggest that higher dollarization is associated with higher interest rates and higher spreads. Table 3 summarizes the results of these regressions. When dollarization share was included alone the equations displayed severe autocorrelation, hence the inclusion of first and second-order autoregressive parameters. Their inclusion reduced the size of the coefficient on dollarization share and sometimes made it insignificant. However, whenever significant, the coefficient is always positive.<sup>13</sup>

> 12 Lower than onshore US rates in some countries can result from the effects of exchange controls. Additionally, the comparability of the terms and conditions for the reported rates may differ from country to country.

> 13 Inclusion of country fixed effects tends to eliminate significance of dollarization or reverse the sign.

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capturing personal (age, gender, education, marital status) and employment (full or part-time,<sup>1</sup> contract duration, type of occupation,<sup>2</sup> and firm size) characteristics. <u>Table 2 reports summary</u> statistics for the sample, aggregated over all countries.

Average tenure varies significantly across countries, ranging from about 7.4 years in Russia to about 13.1 years in Serbia (Figure 1). This span of tenure durations is not very different from that observed in the European Union, where it ranges from 7.4 years in Latvia to 12.7 years in Greece <u>(Bussolo et al. 2022). There is no common trend in tenure for all seven countries: Average tenure</u> increased in Albania, declined in <mark>Türkiye,</mark> and remained relatively stable in the rest of the countries over the period covered by the surveys. The stability of tenure duration in Bosnia and Herzegovina, Kosovo, North Macedonia, Russia, and Serbia is similar to the relatively stable tenure trends observed in EU countries and the United States (Molloy, Smith and Wozniak 2021).

Average tenure varies by population subgroup (Table 3). Women have longer tenure than men in all countries except Kosovo and North Macedonia, where the difference is small. This pattern is different from the pattern in EU countries, where men have longer tenure (Bussolo et al. 2022). As expected, older cohorts have longer tenure than younger ones, although the difference varies across countries. In Serbia, for example, workers older than 50 years spent 21 years in their current job; in Russia, the average tenure for workers in this age group is about 13 years. As in EU countries, less-educated workers enjoy longer tenure than more-educated workers, except in Kosovo and Russia, where the average tenure is higher for workers with more education. Workers on permanent contracts keep the same job longer than temporary workers do. The patterns of job tenure by employer size vary across countries. In Albania, Serbia, and <mark>Türkiye</mark> , tenure is longest among people who work in small firms (firms with ten or fewer employees); in the remaining countries, the longest tenure is among people who work in large firms (firms with more than 50 employees). In terms of occupational groups, people who work in routine jobs have shorter tenure than people who work in non-routine jobs in five of the seven countries.

> 1 Whenever possible, we maintain the codification presented in each labor force survey. When this variable is not reported, we consider a worker to hold a part-time job if he or she works fewer than 30 hours per week.

> 2 We distinguish three occupational groups based on their task content: routine occupations, non-routine cognitive occupations, and non-routine manual occupations. See the appendix in Bussolo, Torre, and Winkler (2018).

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Methodology. Our method consists in collecting Census or register data from a large set of receiving countries, with the highest level of detail on birth countries and (at least) three levels of educational attainment: s = h for high skilled, s = m for medium skilled, s = l for low skilled and s = u for unknown. Low skilled workers are those with primary education (or with 0 to 8 years of schooling in countries where the highest diploma is not provided); medium skilled workers are those with secondary education (9 to 12 years of schooling); high skilled workers are those with tertiary education (13 years and above). The unknowns are either due to the fact that some immigrants did not declare their educational attainment or to the absence of data in some receiving countries. Educational categories are built on the basis of country speci...c information and are compatible with Barro and Lee data on education in the sending countries. A mapping between the country educational classi...cation is sometimes required to harmonize the data<sup>6</sup> . Let Mt;s<sup>i;jdenotethestockofadult</sup> individuals born in j, of skill s, living in country i at time t.

Building an aggregate measure of emigration per educational attainment requires a rule for sharing the unknown values. Two resonable rules could be considered: either unknown values can be distributed in the same way as the known values or they can be assimilated to unskilled. We combine both rules depending on the information available in the receiving country. For receiving countries where information about immigrants’ education is available, we assimilate unknowns to unskilled workers. For example, Australian immigrants who did not mention their educational attainment are considered as unskilled. In receiving countries where no information about skill is available, we transpose the skill distribution observed in the rest of the OECD area. Note that the latter skill distribution is speci...c to each country of birth. For example, if we have no information about the skill structure of immigrants to Italy, Algerian emigrants to Italy are assumed to be distributed in the same way as Algerian emigrants to the rest of the OECD area.

Formally, the stocks of emigrants of skill s from country j at time t (Mt;s<sup>:j)are</sup> obtained as follows: 6For example, Australian data mix information about the highest degree and the number of years of schooling.

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pregnancy or actual fertility. Table 17 also shows a marginally larger impact on protein consumption for men relative to women. We detect no statistically significant difference between the effects for family and reproductive health outcomes (Table 18) for younger women in the AGEI sample compared to older women.

# **VII. Summary and Implications**

In this section, we summarize the impacts of the Employment Fund’s training program. The EF program aims to increase employment among Nepal’s disadvantaged youth through skills training and job placement support. It is one of the largest providers of Technical Education and Vocational Training (TEVT) in Nepal, and it works with local non-governmental training providers to offer courses of up to three months in a variety of trades, followed by six months of employment placement support.

To understand the effects of the EF program, we compared the experience of EF program trainees with the experience of comparable non-trainees, approximately one year after the program. Our sample included both male and female trainees, with a particular focus on the AGEI population ( _i.e.,_ females aged 16-24).

Approximately one year after each baseline survey, we conducted a follow-up survey. The response rates were quite high for both follow-up surveys. Overall, the survey firm was able to track and successfully interview 89 percent of the baseline survey respondents. We detect a high degree of training uptake (65 to 74 percent) among the group offered training, but also a high degree of participation among the control group. Between 26 percent and 36 percent of the individuals in the control group participated in the EF training course that they applied for, even though their scores did not qualify them for admission.

Using a combination of difference-in-difference and propensity score matching methods, we find positive and statistically significant effects on labor market outcomes for EF trainees on the following outcomes:

- employment rates (any or non-farm);

- finding employment related to the skill they learned;

- hours worked

- earnings; and

- the proportion of people earning more than 3,000 NRs per month

Individuals selected for EF training programs experience an increase in non-farm employment of 15 to 16 percentage points for an overall gain of 50 percent. We detect an increase in average monthly earnings of approximately 72 percent. The gains for individuals who actually complete the training (not just those who are selected for training) are even larger. These results are available upon request from the authors.

We also find strong impacts of EF training programs on economic and psychological empowerment. Trainees had more control over economic resources and stronger self-assessed self-confidence. In contrast to the findings on employment and empowerment outcomes, we find limited evidence of impacts on reproductive health or household level outcomes.

Alongside the sizable general impacts on employment outcomes, we find that training courses in electronics, beautician services, and tailoring underpin most of the EF program’s impacts. These three 21
# _Accounting for sample selection_

The above two analyses—using ASER or PISA data—suffer from the potential fact that the profiles of students might be different across grades. This could be, for example, because poorer-performing students drop out at higher grades, or because they are held back and are therefore more concentrated in lower grades. More generally, students who are a grade ahead (or behind) their peers might be different in other ways, and not just in the amount of schooling they have had. In other words, selection on unobservables may bias the results. We therefore now turn to approaches that account for potential selection.

The approaches we use deploy a combination of regression discontinuity and instrumental variables estimation. Specifically, we examine random variation in birth month across school entry cutoffs. Students randomly born a few days after the school cutoff enroll up to a year later than their peers who are just a few days older. This enables us to estimate the causal effect of an additional year of schooling.

There is an extensive literature using random variation in birth month across school-entry cutoffs to identify the effect of _relative age_ differences on learning.<sup>21</sup> This literature relies on estimating test score variation across exogenously determined relative ages. Our approach is inspired by this literature and repurposed to assess _relative grade_ rather than _age_ since we care about how much is learned over the course of a year of schooling.<sup>22</sup> Specifically, we use PISA 2015 data, which assesses 15-year-olds across multiple grades. This dataset includes cohorts born in 1999 and 2000. Since PISA data only includes birth month and year, we are unable to examine precise effects around exact school entry dates as is typical in the literature. Instead, we explore effects around school entry months, which proxies for specific entry dates. An advantage of the PISA data is that it codes a variable for the gap between the grade a student is in and the expected grade given that country’s school entry age laws. For example, in Finland where school starts at age 7, students are expected to be in grade 9 by age 15. In Mexico, students start at age 6 and are expected to be in grade 10 by age 15. This means a PISA-taking

> 21 See, for example, Angrist and Krueger 1991; Dobkin and Ferriera 2010; Fredriksson and Ockert 2014; Smith 2009; Robertson 2011; Puhani and Weber 2007; Kawaguchi 2011; McEwan and Shapiro 2008; Bedard and Dhuey 2006; Elder and Lubotsky 2009; Pehkonen et al. 2015; and Crawford et al. 2010.

> 22 This is similar to work being conducted by Singh (2017) using data from the Young Lives project.

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