rafmacalaba/data-use-mentions-extended huggingface dataset:
instruments for the attainment of target reductions in the wage bill, including reductions of the rate of salary increase, postponements of increments and promotions, and overall reductions in employment. Other studies on this issue include Sunita Kikeri, "Compensation Systems for Public Enterprise Managers in Africa" (1987) and Peter Heller and Alan Tait, "Government Emplayment and Pay: Some International Comparisons," which was carried out by the IMF's Fiscal Affairs Division. The former looks at comparison schemes and performance criteria in public enterprises in Africa; the latter examines comparative data for public sector employment and suggests ways to measure and evaluate them. In addition, a recent paper by Ruth Klinov, "PublicSector Wages and Employment in Bolivia" relates public employment issues to overall economic trends in Bolivia. And, John R. Harris, Edward Andoh, Kojo Evlo, and Martha Starr have analyzed several African pay and employment experiences in their recent paper, "Experiments in Wage Bill Containment in Africa," sponsored by the Bank's Special Office on Africa. In addition, Inayatullah, "Public Employment and Pay" provides a general review of public sector employment issues in developing cuuntries, from the viewpoint of civil service senior management.

- 2/ The overview papers include David Lindauer, "Parastatal Pay Policy in Africa," 1986; Lindauer, Public Sector Wages and Employment in Africa: Facts and Concepts," 1981, and Lindauer, Meesook and Suebsaeng, "Government Wage Policy in Africa: Summary of Findings and Policy Issues," 1986.
Most countries in the world are affected by forced displacement. There are at least 150 countries that host at least 100 refugees each and there are at least 141 countries that generated at least 100 refugees each. If we add the numbers for the internally displaced, almost no country in the world can be said to be immune from forced displacement.

Some countries and some regions are more affected than others. Over 53 percent of all refugees come from only three countries (Somalia, Afghanistan and the Syrian Arab Republic) and the great majority of displaced people are hosted in the Middle East and North Africa (39%), Africa (29%) and Asia and the Pacific (14%) regions.<sup>6</sup> Europe accounts for only 6% of the forcibly displaced and the Americas account for 12%, most of which are IDPs in Colombia.

The incidence of refugees on host countries is greater in low and middle‐income countries and this is true whether we consider population or GDP statistics, refugees or IDPs. Figure 2 shows the number of refugees per 1,000 inhabitants and it is visible that Lebanon is by far the country with the highest incidence of refugees followed by Jordan. Almost all countries in the figure are either middle or low income countries and almost all are in the MENA or Africa region. Figure 3 shows the number of refugees per USD estimated in Purchasing Power Parity (PPP) per capita. Ethiopia and Pakistan lead this particular ranking and the MENA and African region together with South Asia are the regions with countries showing the highest incidence. This principle also applies to IDPs. The top ten countries with the largest populations of IDPs are Colombia, the Syrian Arab Republic, Iraq, Sudan, the Democratic Republic of Congo (DRC), Azerbaijan, Somalia, Bosnia and Herzegovina, Afghanistan and Pakistan, essentially low and middle income countries. Quantitatively speaking, the heavy burden of refugees and IDPs is carried by low and middle income countries and not by wealthy OECD
club convergence materialized at significantly difference levels. While the northeastern region pulled ahead, districts in other regions formed clusters. The worst performing cluster consisting of five districts lying in both the northwestern as well as the southern region experienced not much expansion during 2012-17.

# **4.3   Agricultural credit over the two sub-periods**

The previous two sub-sections show that there is vast heterogeneity in yield progression across districts. Some have seen significantly higher growth while some have also witnessed contraction of late. There exists evidence of club convergence in which districts are converging to yield levels with considerable variation. Both relative and absolute gaps in yields between the leading and the lagging clubs appear to be increasing. More importantly, the results show that the club formation does not align well with the ACZ classification, especially so in the non-leading clubs. Therefore, it is likely that the divergence in yields indicates inefficiency in which districts in the lagging clubs are not able to reap productivity gains.

To address the concern of low gains in lagging districts, it is essential to identify areas that policy could target or where the current policy is lacking. While I am not attempting to address this issue in detail, I check whether the differences in the supply of agricultural credit can explain the divergence patterns observed more recently. Though not a control for the universe of agricultural inputs, credit holds a special status as it is closely tied to the usage of more direct factors such as seeds, fertilizers, machinery, etc.

The government also believes the supply of credit to be a critical policy instrument when it comes to boosting agricultural productivity. Credit allows farmers to make many decisions that are directly related to productivity – adoption of capital-intensive technology, managing risks associated with investments, using yield-enhancing inputs like better seeds and fertilizers etc. Moreover, many observers argue that cooperative credit crowds out borrowing from local moneylenders as they carry a much lower rate of interest. The state economic surveys contain consistent long time-series data on the supply of 12
occupations) and is often left to the discretion of enumerators and data entry personnel, without clear guidance on how to classify farmers.<sup>3</sup> Under the 19th ICLS standards, the distinction between own-use and market-oriented work in agriculture has been propelled to the forefront of labor statistics in developing countries.

The present paper analyzes the implications of the 19<sup>th</sup> ICLS standards on measures of employment obtained from multi-topic household surveys. We focus on Sub-Saharan Africa, where we expect a disproportionately large decline in measured employment using the revised standards, due to the high prevalence of subsistence agriculture in the region.<sup>4</sup> The main contribution of this paper is as follows: first, we provide a brief history of the evolution of labor statistics standards and the rationale for changing the definition of employment. Second, using multi-topic household survey data sets from three Sub-Saharan African countries, we illustrate how measures of employment of male and female farmers change under the 19<sup>th</sup> ICLS standards, comparing different measurement approaches, and the implications on country-level headline indicators of employment. This complements the extensive qualitative and quantitative work carried out by the ILO in piloting alternative survey questionnaires to support the operationalization of the 19<sup>th</sup> ICLS standards in labor force surveys (Benes and Walsh 2018). Third, the paper contributes to the broader literature on the sensitivity of labor statistics to survey methodology in developing countries (Bardasi et al 2011; Heath et al, forthcoming; Arthi et al 2018; Gaddis et al, forthcoming).

Our main findings are as follows. First, even with a geographical focus on rural Africa, there is large contextual variation in the share of farmers classified as employed under the revised standard, depending on spatial differences in the commercialization of agricultural production. In parts of Malawi and Nigeria 70 to 80 percent of farmers report producing only or mainly for family consumption at the beginning of the agricultural
similar pattern of net coverage rates below the regional average, combined with significantly higher shares out of school that reach between 43 and 47 percent, twice as high as the Latin America average. Thus, in these countries, overage is lower, but only because more youth have already left the schooling system by age 17.

Students who are overage due to starting late and/or repeating grades are more likely to drop out, as demonstrated with administrative data by Manacorda (2006) in Uruguay and Branson et al (2014) in South Africa. This relationship may exist because the same factors that drive late school entry or poor performance also drive later dropout, because the opportunity costs of remaining in school rise as children age, because overage and poorly performing students are pushed out of the system rather than supported, or any combination of these and other factors. Regardless of the underlying drivers, the existence of a correlation suggests that the results in Figure 11 are an indication of the prevalence of dropout in Central America in the near future.

# **2. Consequences of dropout in Central America**

The previous section documented the patterns of school enrollment and dropout at the critical ages where individuals would be expected to be in secondary school. In this section, we explore some of the consequences of prematurely leaving school.

Leaving the schooling system before US completion can have a wide array of effects. Here we explore some of its labor market consequences, which depend on the interaction between the accumulation of human capital acquired through education, and the characteristics and evolution of the productive sectors of the economy that generate the conditions for its use. For instance, if there are restricted employment opportunities, the actual effect of early exit on economic wellbeing might be lower than expected.<sup>13</sup> The probabilities of working, and of working in the formal sector, are strongly related to educational attainment throughout individuals’ working lives. As shown in Table 2, unemployment rates among 25-45 year olds are higher among
empirical procedure that not only permits simultaneous estimation of two unobserved fixed latent factors (household and school effects), but also allows alternative assumptions regarding their covariance to be handled in a transparent fashion. This provides (strict) bounds on the variance contributions of interest. Furthermore, in our empirical implementation, we validate (cross-test) our results showing that extreme assumptions about the between-factor covariance can be ruled out.

The rest of this paper consists of four sections. Section 2 sets out a general framework to account for IOE, which also provides a window on findings of previous studies. It also describes the empirical strategy we use to identify the household and school factors, treating them both as unobserved effects, and proposes a simple mechanism that offers a unified variance decomposition strategy, covering the full range of alternative assumptions regarding the between-factor covariance.

Section 3 applies the proposed approach to extensive test score data on over 1,000,000 school-aged children from three East African countries (Kenya, mainland Tanzania and Uganda). It compares variance-decomposition results from three choices of the initialization parameter, each of which corresponds to an intuitive characterization of the between-factor covariance. Using both unconditional and conditional models, we show that household and school circumstances _jointly account_ for nearly half of the variance in test scores (normalized by age). However, confirming the limitations of past studies, households cannot be considered the primary or only source of IOE.

More specifically, we find the upper-bound variance share attributable to schools is generally as large as the upper bound attributable to households, which in itself is indicative of a positive association between the latent factors. And under our preferred model specification, we find evidence of substantial positive sorting whereby higher ‘quality’ households tend to be matched to higher quality schools. In Section 4 we validate the main findings using alternative estimation procedures and investigate heterogeneity in the variance decomposition. We find systematic patterns in the level of inequality and the magnitudes of the variance components across different sub-groups and geographical locations, including a larger contribution of
applicants who had ever migrated to New Zealand (Figure 1). Since non‐ compliers were sampled at a lower rate, we use the administrative data to reweight the migrants and non‐complier samples to be representative of the ballot winners. We intentionally surveyed only enough ballot losers to optimize the power to detect impacts of migration and the vast majority of ballot losers we made contact with agreed to be interviewed.

Table 1 then provides some basic summary statistics for the ballot winners and ballot losers. Given the time that has passed since individuals applied for the ballot, we have few pre‐determined characteristics on which to check balance: age, gender, and place of birth. We see that the principal applicants are 41 on average, two‐thirds are male, and almost one‐quarter were born in the outer islands. Although the ballot losers are slightly older, a joint test of orthogonality cannot reject the null hypothesis that age, gender, and birthplace are unrelated to treatment status (p=0.306). This suggests that our treatment (ballot winner) and control (ballot loser) samples are comparable and we can use the random assignment

> 3 Villages in Tonga average about 90 dwellings and so door‐to‐door methods are a feasible way to locate ballot losers and non‐migrant winners.

6
occupations in the job postings data and use the skills profiles of those occupations as a proxy for the skills profile of missing occupations.<sup>18</sup>

# _Description of the occupational skills profiles_

The average number of, and types of, skills listed in (or imputed from) the job vacancy data differs by occupation. Occupations require an average of 6 skills, ranging from 2.2 to 12 skills (Table 4). Heavy truck and bus drivers require the fewest skills (2.2) while software and applications developers and analysts are at the other end of the distribution requiring 12 defined skills. On average, occupations require one digital skill, less than one cognitive and socioemotional skill each, and more than three other skills, which include languages and other technical skills, highlighting the high degree of jobspecific skills.

**Table 4. Count and share of requirements for skills categories and their subsets across the 127 occupations**

||**Variable**|**Mean**|**Std dev**|**Minimum**|**Maximum**|
|---|---|---|---|---|---|
|**Average number of skills**|**Total**|5.6|1.8|2.2|12.0|
|**(count)**|**Digital**|1.0|1.1|0.0|7.6|
||Advanced|0.3|0.8|0.0|6.4|
||Intermediate|0.3|0.4|0.0|2.5|
||Basic|0.4|0.2|0.0|1.9|
||**Cognitive**|0.7|0.3|0.2|
., 1993) and most of the empirical literature has focused on metropolitan areas, based on the fact that communication is more intensive there (see Lucas, 1988). To widen the scope of the study to non-urban areas I use “concelhos” as the geographic unit<sup>20</sup> . The Portuguese Administrative Law establishes that the division of Portugal’s mainland is done into 275 “concelhos”, grouped together

> 18 Except those firms without wage-earnings employees and Public Administration.

> 19 In 1994 the classification of economic activities changed to match the European Community criteria and the correspondence between the two classifications is not unique.

> 20 Other papers have also used different units of observations: Peri (1992) uses a data set with information at the city and province level which cover all Italian territory and Combes (2000) uses “zones d’emploi” for France, which is the most similar unit to the one used in this study.

12
McKenzie, David J. (2003) “How do Households Cope with Aggregate Shocks? Evidence from the Mexican Peso Crisis”, World Development 31(7): 1179-99.

McKenzie, David J. (2004) “Asymptotic theory for heterogeneous dynamic pseudo-panels”, Journal of Econometrics 120(2): 235-262.

Moffitt, Robert (1993) “Identification and estimation of dynamic models with a time series of repeated cross-sections”, Journal of Econometrics 59(1): 99-124.

Piketty, Thomas (2000) “Theories of Persistent Inequality and Intergenerational Mobility”, Chapter 8, pp. 429- 476 in A.B. Atkinson and F. Bourguignon (eds.) Handbook of Income Distribution Volume 1, North-Holland: Amsterdam Strauss, John, Kathleen Beegle, Agus Dwiyanto, Yulia Herawati, Daan Pattinasarany, Elan Satriawan, Bondan Sikoki, Sukamdi and Firman Witoelar (2004) Indonesian Living Standards before and after the financial crisis: Evidence from the Indonesia Family Life Survey, RAND Corporation: Santa Monica.

Thomas, Duncan, Elizabeth Frankenberg and James P. Smith (2001) “Lost but not Forgotten: Attrition and Follow-up in the Indonesia Family Life Survey”, Journal of Human Resources 36(3): 556-592.

Verbeek, Marno and Francis Vella (forthcoming) “Estimating dynamic models from repeated cross-sections”, Journal of Econometrics.

30
in Table 15, I rely on a first-difference model in which I examine the effect of the future use of Internet job search on past labor market outcomes. To do so, I additionally rely on the 2006 ELMPS survey round and follow the same individuals included in my estimation sample (the 2012 and 2018 panel) that were also surveyed in 2006. Focusing on working aged individuals, I examine the effect of the change in Internet job search between 2018 and 2012 on changes in labor force participation and employment between 2012 and 2006. The results reported in Table 15 show that the change in Internet job search between 2018 and 2012 is actually associated with lower labor force participation in the pre-period between 2012 and 2006, while my results in Table 6 actually show a positive association. This suggests that individuals who are using the Internet for job search do not initially have better labor market outcomes compared to individuals who are not—if anything, they seem to have lower labor force participation rates in the pre-period. Analyzing the impact on employment in Panel B actually shows no effect of the future use of Internet job search on past changes in employment, suggesting that the results are not driven by pre-existing trends in employment between individuals who rely on Internet job search and those who do not.

The second test consists in utilizing all the available ELMPS survey rounds conducted in Egypt since 1988 (1988, 1998, 2006, 2012 and 2018). Since attrition is problematic and it is impossible to follow individuals who remain in the working age category over such a long period of time, I rely on sub-district ( _shyakha_ ) level regressions.<sup>7</sup> In these regressions, I use the percent of male/female Internet job searchers at the sub-district level in 2018 and examine the effect of the latter on men and women labor force participation rates, unemployment, and employment at the sub-district level. The analysis is conducted separately for each survey round and by gender. Regressions are restricted to men/women of working age
of income shocks may lower the volatility of consumption as agents would be less likely to adjust their consumption in response to such shocks. If the persistence of the income shocks decreases around the time when the elasticity of consumption to income declines, the results could then be attributed to agents’optimal response to this drop in the persistence, and not necessarily to an increase in the degree of financial integration.

To check the role of the persistence of the income shocks, we analyze how the persistence of income shocks evolves over time using Total Factor Productivity (TFP) data from PWT 8.1. Specifically, we study the evolution of the median AR(1) coeffi cient of the changes in TFP for each country group. For the full sample, as well as for the advanced and developing country sub-groups, the median persistence has increased slightly until the late 1980s (from near zero in the late 1970s to close to 0.2 in late 1980s, but has not changed much since then). This would suggest that the decline in the sensitivity of consumption to income changes could not have been associated with the changes in the persistence of income shocks (Figure A1 in the Appendix).

The sensitivity of consumption to income can also be affected by the correlation between the permanent and transitory components of expected shocks to income (Deaton, 1987; Quah, 1991). The sensitivity of consumption to income would be lower if the correlation between the permanent and transitory shocks is negative. A decomposition of permanent and transitory components would require detailed information on expected income but it is not possible with the aggregate data we have here. However, this remains an interesting avenue for future research.

Changes in the correlation of income shocks: The elasticity of consumption to income also depends on the cross-country correlations of income shocks. On the one hand, if income shocks are highly correlated, this would lower incentives to share risk ex-ante with the rest of the world and may increase the sensitivity of consumption to domestic income. On the other
# **2 Data**

We use a unique data set by combining worker-level information from the World Bank’s International Income Distribution Data Set (I2D2), which contains harmonized household and labor surveys, with FDI project-level information from the fDi Intelligence database.

## **2.1 FDI**

fDi Intelligence has been tracking and verifying individual cross-border greenfield investment projects since 2003.<sup>5</sup> The database, available through fDi Intelligence, which is part of the Financial Times Group, provides project-level information on the value of investments and the estimated jobs created. Importantly, the data allow us to do our analysis both at the sub-national level and at the industry level, as well as to focus on the extensive margin of FDI, i.e. the number of greenfield projects.<sup>6</sup> Figure 1 shows the evolution of FDI projects in the seven emerging economies we study. Most of these countries saw a steady rise in the yearly number of FDI projects over the years, yet the last couple of years saw a slight reverse in the trend. Brazil saw a steady rise in FDI from 2006 to 2012, culminating with around 550 projects in 2011. Colombia saw a steady rise in FDI from 2003 to 2013, culminating in around 175 projects. As for Brazil, FDI inflows have been declining since 2013. South Africa and the Philippines saw similar increases until 2013 and 2014, whereas Vietnam peaked earlier in 2008 with around 350 projects. In Mexico and Ethiopia FDI projects have been growing since 2003 and have shown more resilience in the last years covered with no observed decline.

> 5The data are now a primary source of FDI information for UNCTAD, the World Bank, and the Economist Intelligence Unit.

> 6We aggregate FDI in 10 sectors, namely Agriculture, Extraction, Manufacturing, Construction, Utilities, Commerce, Transport and Communications, Financial and Business Services, Public, and Other Services. The number of regions varies by country: Brazil (