rafmacalaba/data-use-mentions-extended huggingface dataset:
over the period of analysis can be adequately summarized by the set of indexes introduced in section 2.

Until very recently, the only institutions considered to affect unemployment were labor market institutions such as unemployment benefits or employment protection legislation. Those institutions are important, among other things, because they affect the expansion and contraction of established firms, which have an important impact on unemployment.

But apart from the expansion of established firms, there is another source of job creation, namely, _the birth of new firms_ . The few statistics that we have show that between one third and one fourth of new jobs are created by new, genuinely private, firms in the ECA countries. Hence new firm creation is a very important source of new jobs in the group of countries we consider.

The rate of creation of new firms, and therefore job creation, depends on those institutions that shape the business environment where firms operate, described in section 2 of this paper. We intend to explore the impact of each of those institutions on the labor market.

The list of institutions considered in the regression model is as follows:

- Administrative burdens on firm creation (start-up costs)

- Cost of going bankrupt (exit costs)

- Access to finance (cost and availability of finance)

- Quality of the legal system (efficiency and independence of courts)

- Market regulations (credit and labor market regulation)

- Corruption (perceived corruption level, from Transparency International)

- Tax burden (tax revenue as percentage of GDP)

For a detailed account on the construction of indexes able to capture those institutions, please refer to Appendix 2.

The ECA countries have been hit by a major shock over the last decades: the fall of the communism regime and transition to a market economy. We will use the EBRD transition index to size the shock across countries and to follow its evolution along time.

The EBRD transition index aims to reflect each country’s moment in the transition towards a market economy. Hence it deals, among other things, with
.<sup>16</sup>

# **Energy and water**

Despite being devoid of energy resources and dependent on neighboring Malaysia for half of its daily water needs, disruption of utilities supply is a rare occurrence in Singapore. The electricity and piped gas industries had traditionally been vertically integrated and governmentowned, managed by the Public Utilities Board (PUB). In 1995, the various undertakings of the

> 16  In its 22 June 2000 edition, the Economist magazine remarked, “When it comes to e-government, there is nothing to match Singapore”.  In the annual survey by consultancy firm Accenture, Singapore has retained its position as having the second best e-government service in the world for 4 consecutive years starting from 1999, after Canada.

8
(quadratic, cubic) of T� (recall length). We compare the linear and non-linear models using a variant of the Bayesian Information Criterion (BIC’) as a goodness-of-fit measure to guide which model is best suited.

Recall error may be exacerbated by certain household, individual, or farm characteristics. We explore interaction effects between the recall length and plot size, respondent characteristics (gender and education), and whether the respondent is also the plot manager. The latter is relevant because, to an extent, it is in the survey designers’ purview to enforce that the respondent be the plot manager.

# 4. Results

We find that the recall length has a significant impact on reported outcomes in all areas of interest of this analysis. Farmers report significantly higher quantities of plot-level variables, that is maize harvest, labor and fertilizer input, as the recall length increases. In contrast, farmers report fewer cultivated plots of land in longer recall periods.

## Land

Households list significantly fewer plots (and parcels) as the recall period increases. Results from the baseline linear OLS specification with a full set of controls, shown in Figure 3 and Figure 4 (and Table A.3 in the Appendix), are consistent across the three data sets. The results from a Poisson regression are in line with those from OLS. The magnitude of the effect varies by survey, however. It is small for Tanzania NPS 2012/13 at 0.03 fewer plots per month and Malawi IHS4 (cross section) at 0.05 plot per month, but larger in Malawi IHPS 2016/17 (panel) at 0.1 plot per month. This may be the result of several factors, which we cannot directly assess.

The results from a quadratic specification confirm the overall negative relationship between recall length and plots listed (Table A.4). The BIC’ goodness-of-fit comparison suggests that the linear model is preferred in Tanzania NPS 2012/13 and Malawi IHS4 data and that the quadratic model provides
bank competition, the recent literature also emphasizes the differences between the two, suggesting concentration is a less reliable indicator of competition (Beck, Demirguc-Kunt, and Levine, 2006; Demirguc-Kunt and Martinez Peria, 2010). Nevertheless, as an alternative measure of competition, we use the three bank asset concentration ratio from the Financial Structure Dataset to measure banking concentration. We run the same regressions specified in equation and (9) replacing the Lerner index with bank concentration. Unlike the Lerner index, bank concentration is measured at the country level each year. All other control variables are the same as those used 17
country over‐time fluctuations in harmonized test scores for a given testing program reflect only changes in the test scores themselves, and not changes in the conversion factor between tests.<sup>8</sup> Harmonization is done at the subject x grade level. The country‐level test scores used in the HCI average across subjects and grades.

In most cases, the tests are designed to be nationally representative. There are however some notable cases where they are not, including:

- In a number of countries, EGRA assessments are not nationally‐representative and are identified as EGRANR in the data documentation.

- In India, the 2009 PISA was administered in two states (Himachal Pradesh and Tamil Nadu). However, a comparison with state‐level scores for all of India in the 2012/2013 National Achievement Survey (NAS) suggests that the average NAS score for these two states is quite similar to the national average NAS score, indicating that the 2009 PISA scores probably are roughly representative of India as a whole.<sup>9</sup>

- PISA scores for China in 2009 and 2012 are based only on reported data for Shanghai, and in 2015 for Beijing, Shanghai, Jiangsu and Guangdong (B‐S‐J‐G).  Shanghai and B‐S‐J‐G are both considerably richer than China as a whole.  Since test scores tend to improve with income within and across countries, reported PISA scores are unlikely to be nationally representative. Corroborating evidence can be found in Gao et. al. (2017) who implement PIRLS assessments in Shaanxi and rural Jiangxi and Guizhou provinces, the latter being among the poorest areas in China.  As noted in Gao et. al. (2017), test scores in these areas are among the lowest PIRLS scores observed globally.  Extrapolations using average household per capita income using (a) Shanghai and B‐S‐J‐G PISA scores, and (b) PIRLS scores in Gao et. al.
. Columns (1) and (4) include dummies indicating whether any household member is engaged in non-farm activities. Columns (2) and (5) disaggregates non-farm employment by employment type, while columns (3) and (6) does likewise for employment sectors. All specifications restrict the sample to rural households, and use household weights. Standard errors continue to be clustered by district, allowing for arbitrary correlation in consumption/poverty outcomes across households located in the same district.

Table 6 shows robust correlations between non-farm employment, education and household consumption. Column (1) shows that while having a literate member in the household increases per capita monthly consumption by almost 7 percent, the presence of a member

> 9 The consumption data obtained through the NSS survey rounds are used to construct state-specific poverty line thresholds corresponding to each round. Households with monthly per capita consumption less than this threshold are classified as below the poverty line or BPL.

20
The survey migration rates match the migration numbers provided by BMET. Assuming the same migration rate for the entire lottery program, our estimates suggest that about 10,700 would have migrated from the entire pool of 1.43 million applicants. This is only slightly higher than the official count of 9,800 provided by BMET officials in March 2018. This suggests that the rate of government intermediation in our sample is similar to that of the entire program even though we surveyed in divisions closer to the capital.

G2G intermediation led to migrants migrating much earlier than the control group. By the time of our survey, it had been 33 months since the migrants in the control group had migrated. That is, among those in the control group who migrated, they did so about 34 months after the lottery was conducted in February 2013. The T2 group migrated at about the same time as the control group – 33 months after the lottery. The T1 group migrated about 19 months earlier – 15 months after the lottery. Among the G2G migrants, the average T1 (Phase 1 winner) migrant migrated 14 months after the lottery, whereas the average T2 (Phase 2 and 3 winner) migrant migrated 26 months after the lottery. As expected, Malaysia was the most popular destination for the migrants in the T1 and T2 groups because of the nature of the government intermediation. Even without the government intermediation, Malaysia was still the destination for about a quarter of migrants in the control group. In that group, about two-thirds migrated to Gulf countries, including 22 percent going to Saudi Arabia, 21 percent to Oman, and 14 percent to Qatar.

A large share of applicants who migrated are still abroad. About 75 percent of the applicants from group T1 who migrated were still abroad at the time of the survey. The proportion is slightly higher for applicants who migrated from group T2 or the control group, at 78 percent. On average, the time of the survey was almost four years after the initial migration of applicants in
|**Tool name:**|**SimSIP Poverty**|
|---|---|
|**What is it?**|SimSIP Poverty is a generic Excel-based simulator that allows one to estimate the changes in poverty<br>and inequality over time resulting from changes in output and employment growth.|
|**What can it be used for?**|This tool may be used to simulate the poverty and inequality impact of policies affecting sector-level<br>output and employment growth.|
|**What does it tell you?**|It simulates poverty and inequality measures nationally and within sectors (urban and rural, agriculture,<br>manufacturing, and services). It may simulate the impact that various sectoral growth patterns and<br>population shifts between sectors will have on future poverty and inequality.|
|**Complementary tools:**|Other tools for poverty forecasting include PovStat (see the table on PovStat) and DAD (a software for<br>distributive analysis).|
|**Key elements:**|On the basis of existing information on group-level household survey data (typically by deciles or<br>quintiles), the software translates differential output and employment growth across sectors into<br>differential growth in the per capita income or consumption of households across these sectors. The<br>tool simulates the impact on poverty of policies affecting output by using the fact that poverty changes<br>can be decomposed into two parts: a component related to the uniform growth of income and a<br>component due to changes in relative income. The simulations are made under the assumption that the<br>policy analyzed will be distribution neutral or, conversely, that there is a specific, quantifiable form for<br>the distributional change. Changes in employment distribution are accommodated by reweighing the<br>sample households.|
|**Data/information:**
million establishments. The samples are nationally representative for establishments with 5 or more workers. For each country, the sampling frame is based on the most comprehensive and up-to-date establishment-level census data available from the respective National Statistical Office (NSOs) or similar authority. The survey is stratified on three dimensions - sector, firm size, and region - so that we can construct representative measures of technology for aggregates along these dimensions. Sampling weights are based on the inverse probability of selecting establishments within each stratum.<sup>8</sup>

> 8Table A.15 provides information about the distribution of firms by country, sector, and size groups within the universe covered by the FAT survey. For the state of Ceará in Brazil and the Indian states of Tamil Nadu, Uttar Pradesh, Gujarat, and Maharashtra, it is representative at the state level. Section A of the Appendix provides more details on the sampling frame, survey implementation and data collection, and sampling weight.

11
such labor in all parts of the economy. Conversely, developing countries should specialize in goods that use less skilled labor more intensively and so liberalization should boost unskilled wages.

> 8 Lemieux (2003) is a recent investigation of whether the average wages for different classes of workers defined on the basis of their skills (education and experience) and other characteristics (gender in particular) in Canada and the United States have converged over the last two decades. He notes that aside from the restrictive conditions needed for factor price equalization to be observed, it is not very reasonable to expect national wages to be identical across countries if they are not equalized across regions of the same country (where labor and capital mobility should be much more powerful in equalizing factor prices). Using regional wage dispersion in Canada and the United States as a benchmark for assessing “how different” the wage structures in the two countries are, and controlling for national and regional differences in worker characteristics, he concludes that there has been _divergence_ between the wage structures in Canada and the United States over the last 20 years.

10
, and Dudel et al. (2021) suggest that the square-root adjustment performs well, particularly for larger households.

> 6 The square-root adjustment is often applied in research work (Johnson et al., 2005; Ravallion, 2016; Smeeding, 2016; Taylor et al., 2011), policy work (OECD, 2015; US Congressional Budget Office, 2018), and international comparison of poverty and inequality, as done with the Luxembourg Income Study (LIS) (Buhmann et al., 1988).

7
- 120 -

- Achio, Francoise. Les Politiques, la Planification et l'Administration de l'Enseignement Technique et Professionnel en C8te d'Ivoire, Abidjan 1983.

- Ainsworth, Martha and Juan Munoz. The C8te d'Ivoire Living Standards -

- Survey Design and Implementation, Living Standards Measurement Study Working Paper No. 26, The World Bank, Washington D.C., 1986.

- Blinder, Alan. On Dogmatism in Human Capital Theory, Jcurnal of Human Resources, Vol. XI (1), 1976.

- Coulibaly, Aly. L' Apprentissage Traditional, sa Contribution a la Qualification des Travailleurs des Secteurs Informel et Moderne, Cahiers Formation Emploi, No. 2, 1986.

-

- den Tuinder, B. Ivory Coast The Challenge of Success. Johns Hopkins University Press, Baltimore, 1978.

- FNR (Fonds National de R6gulation), Exercise 1983-84 - Statistiques, Abidjan, Mai 1985.

- Griliches, Z. Estimating the Returns to Schooling: Some Econometric Problems, Econometria, VoL 45 (1), **1977,**

- Grootaert, Christiaan. Measuring and Analyzing Levels of Living in Developing Countries: an Annotated Questionnaire, Living Standards Measurement Study Working Paper, No. 24, the World Bank, Washington D.C., 1986a.

- Grootaert, Christiaan. The Position of Migrants in the Urban Informal Labor Markets in C8te d'Ivoire, mimeo, Development Research Department, The World Bank, Washington D.C., 1987.

- Grootaert, Christiaan. The Role of Employment and Earnings in Analyzing Levels of Living - A General Methodology with Applications to Malaysia and Thailand, Living Standards Measurement Study Working Paper, No. 27, The World Bank, Washington D.C., 1986b.

- Guirassi, Ibrahima. La Formation en C8te d'Ivoire, Cahiers Formation Emploi, No. 2, 1986.

- Heckman, James. The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple
iii) extend the scope of investigations beyond the manufacturing sector (Javorcik 2009). The second research gap is also a function of the FDI measure being used. The econometric studies above, for example, measure FDI only at the broad sectoral level, but don’t include direct supplier relationships with multinational firms which are based on survey data and could reveal the exact underlying mechanisms (Javorcik and Spatareanu 2009).

Using newly collected survey data on direct supplier-multinational linkages in Chile, Ghana, Kenya, Lesotho, Mozambique, Swaziland, and Vietnam, this paper addresses these research gaps as follows. We first evaluate whether foreign investors differ from domestic producers in terms of 2
with the share in informal sector and, in particular, for parasite firms, which have a share of 80% (Figure 2, panel a).<sup>14</sup> A quarter of formal firms are in manufacturing (25%), while informal firms have a participation of more than 83% in the services and retail sectors (Figure 2, panel b). Informal firms also tend to be relatively younger than formal firms perhaps because of faster churning in the informal sector (Figure 2, panel c), while nearly half of the firms in the formal sector are established (with more than 15 years in operations). This result is consistent with the recent findings in Vietnam (Mccaig and Pavcnik, 2021) where entry and exit are relatively higher in the informal sector compared to that observed in the formal sector.

Given that the attributes of our sample from BPS data are in line with documented evidence about informal firms we are confident that our data are suitable for analysis on informality.

Figure 2: Characteristics of firms by type of informality <!-- Start of picture text --> (a) Size (b) Sector<br>100 100<br>Manufacturing<br>90 90 Retail<br>80 80 80 80 78 Other services<br>72 73 73<br>69<br>70 70<br>60 60<br>50 50<br>40 38 40<br>30 30 25<br>20 20 17 17<br>10 12 10<br>10 10<br>3 3<br>0 0<br>Necessity De Soto Parasite Fully formal Necessity De Soto Parasite Fully formal<br>(c) Age<br>100<br>Young<br>90 Maturing<br>Established<br>80<br>70<br>60<br>50 47<br>41<br>40 37 36 34 38 32 34
programmatic|(68)|(69)|(43)|(62)|(25)|(7)|

**Note** :  The comparison is of countries in 2008.  Poorer and richer countries are those below or above the median per capita income in 2008; democracies are countries with competitive elections (legislative and executive indices of competitive elections from the Database of Political Institutions equal to seven)

Neither difference is significant comparing poor and rich non-democracies and ruling party age is actually somewhat lower in richer non-democracies. However, oil-rich countries disrupt this last comparison. As Gehlbach and Keefer (2009) argue, leaders of resource rich countries are less likely to institutionalize their political parties, but incomes are still likely to be high. Consistent with this, if Middle Eastern countries are excluded from the comparisons in Table 6, all of the other differences remain approximately the same, but the party years variable remains approximately 16 in the poor non-democracies, but rises to 24 in the (now only seven) rich non-democracies.
Second, we examine whether any of the interventions changed internal and regional migration. Take-up of the Dakar travel facilitation was low, with only 16 percent of this treatment group contacting our Dakar representative, and only 12 youth meeting with them in Dakar. We find no impact of this intervention on migration to Dakar. However, the offer of Senegal as a destination appears to have stimulated migration to other parts of Senegal, and we find a 2.5 percentage point increase in the likelihood of the youth being in Senegal at the time of the follow-up survey with this treatment (relative to a control mean of only 1.6 percent), with them doing less internal migration to the capital city of Banjul and other urban areas. Take-up of the vocational training intervention was also low, with 12 percent beginning, and only 5 percent completing training, in part due to delays in implementation and closures in training facilities during COVID-19. The offer of this vocational training also led to an increase in migration to Senegal and reduction in internal migration, with similar magnitudes to the Dakar treatment.

Finally, we test for changes in irregular migration behavior. The vocational training treatment resulted in a statistically significant, but small reduction in the number of steps taken towards backway migration during this period, while there was no significant effect of information or the Dakar offer. None of the three treatments had any significant effect on attempted backway migration or on making it to Europe. Irregular migration from the Gambia dramatically dropped during our study period, with Frontex data showing only 394 irregular migrants in European border data in 2019 and 285 in 2020, compared to 8,522 in 2017.<sup>4</sup> This appears to stem from a change in government in The Gambia reducing the chance of asylum in Europe, coupled with the effects of border closures and a reduction in the desire to migrate due to the COVID-19 pandemic in the second half of our follow-up period (Bah et al, 2021). The consequence was that, despite
there is no gender wage gap in the sample, though the sample includes only individuals under 25 years of age, when most men and women have still not experienced maternity in the country. In fact, the typical age for child birth in Kosovo is about 27.

# **2. Methodology and Data**

## **2.1. Data**

The econometric analysis is based on the ILO’s School-to-Work Transition (SWT) survey collected in Azerbaijan on a sample of young workers aged 15 to 29 in 2005 representative of the national population. As the name suggests, the survey collects information on the labor market experiences of young workers at the beginning of their career shortly after leaving school and entering the labor force. The data are at the individual level<sup>2</sup> with a sample size of 4,972. The survey includes three age groups: “teenage workers” (aged 15-19 years), “young adults” (20-24 years), and for lack of a better-term, the “prime-age workers” (25-29 years). The sample size is large for this segment of the labor force, which is generally under-represented in typical labor force surveys. The data thus provide an unusual opportunity to analyze the labor market performance of men and women in this stage of their careers.

Between 2004 and 2006, the ILO conducted SWT surveys in 8 countries (Azerbaijan, China, the Arab Republlic of Egypt, Kosovo, Mongolia, Nepal, and the Syrian Arab Republic). and subsequently made the micro data publicly available.<sup>3</sup> Although as a result of the lag in the public release of the data the data are now a few years old, the general paucity of data on earnings or wages in Azerbaijan and other countries, especially among young workers, means that SWT data provide a rare opportunity to assess the emergence of gender wage differentials.

## **2.2. Variables**

> 2 The