rafmacalaba/datause-encoder-data huggingface dataset:
strongly depends on entry of new firms, while high degree of concentration implies that new entry is limited. This means that in Lithuania job creation must be high in _existing_ firms, which enjoy a significant degree of market power.'<sup>0</sup>

**Table 10 Job Turnover and Concentration of Employment within Industry, 19902000**

|**Concentration**|J<br>**Jobcreationrate**|ob destructio<br>**rate**|n<br>N<br>**Jobturnoverrate**|etemployment<br>growth|Excessjob<br>reallocation rate|
|---|---|---|---|---|---|
|Low|11.3|16.4|27.7|_-5.0_|22.7|
|Moderate|5.9|12.8|18.7|-6.9|11.8|
|High|11.8|8.4|20.2|3.4|16.8|

Note: The degree of concentration is measured by Herfindhal-Hirschman Index (HHI) and is categorized as follows: Low = HHI is less than 1,000 points Moderate = HHI between 1,000 and 1,800 points High = HHI in excess of 1,800 points _Source. Annual survey of wages and salaries, 1999 and 2000, Bank staff calculations._ The pattern of job destruction is different: the rate of job destruction monotonically decreases with market concentration. It is lowest (8 percent) in industries where market concentration is high, it is significantly higher (13 percent) in industries in which market concentration is moderate and is by far the highest (16 percent) in industries in which market concentration is low.

Consequently, job turnover is the highest in industries in which market concentration is low and thus competition is high, which is what one would have expected. Less intuitive is the finding that job turnover is the lowest in industries characterized by moderate degree of market concentration. Also the observed pattern of employment growth depending on the degree of competition seems surprising. Employment grows the fastest in industries in which market concentration is high and falls most rapidly in industries in which market concentration is moderate.

> 10 This is broadly consistent with findings of Carlin et al (2001), as the present study finds that "some market power but not too much" has positive effects on sales and productivity growth and other indicators of firms performance. In their paper "some market power but not too much" occurs when firms are facing one to three competitors, which by our measure would most probably translate mto high degree of market concentration.

24
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Second, in a training system where a given module may be a component of several different courses, the replacement of an outdated module ensures that all affected courses are upgraded systematically. This is one of the poten'-ial strengths of the fine-textured ILO approach, where occupational training programmes are broken down into modules of employable skill, and these in turn are broken down into training units which may be common to several modules, and of the V-TECS approach, both of which are based on taskanalysis (ILO, 1973, 1984).

Third, modularization permits individuals to customize their training to some extent and hence respond to changing patterns in the demand for skills. This flexibility is enhanced by the fact that the training design follows the cluster principle (see, for example, Ma'ey, 1975) in which the introductory modules are typically broad-based and common to a number of related occupations, subsequent ones becoming progressively more occupationspecific, with the consequence that the trainee can postpone making a commitment to a particular occupation until he has completed a substantial part of the training.

# Innovation

In addition to responding promptly and flexibly to changes in the demands placed upon it, it is important that a training system should be capable of acting as a conduit for innovation. In this respect both institutionalized and employer-based training have their strengths and weaknesses.

# 4.3 Economies of scale

A donor agency recently received a request to support the introduction of a training scheme for lift technicians in a Sub-Saharan African country. On investigation it transpired that the country possessed precisely one lift. This is an extreme example, but the fact of the matter is that there is surprisingly little interest in systematically -monitoring unit costs in both public and private training schemes, in developing and developed countries alike, and hence there is a lack of awareness of the possible tradeoffs for different types of education and training, given a fixed budget.

Typically such unit cost studies as do exist have been undertaken on an ad-hoc basis by consultants or researchers and they are limited to evaluating how much more one type of training costs than another type at a similar level.

The direct unit costs (not including administrative and boarding overheads), and hence cost-effectiveness, of a training scheme automatically depend on two factors: the cost of acquiring the services of an appropriate instructor and facilities, and the intensiveness with which these services are utilized.

The first factor causes the cost of occupational training to be approximately twice that of non-technical training in center-based or school-

- 24 -
# **4.2. Life satisfaction**

We construct an index of average satisfaction with life for every country, measured as the simple weighted average of answers of respondents to the statement: _All things considered, I am satisfied with my life now,_ with the following coding: 1. strongly disagree; 2. disagree; 3. neither disagree nor agree; 4. agree; 5. strongly agree. We find that changes in satisfaction with life at the country level are correlated with the severity of the crisis impact, as measured by GDP growth (see Figure 9). Life satisfaction captures, at the individual or national level, many measures of well-being (Layard, 2005). It is worth noting that, because our baseline period is 2006, we are not able to compare changes in life satisfaction just before and after the crisis. The period since 2006 has included both periods of strong growth and the contraction during the crisis. We nevertheless analyze changes in life satisfaction with regards to the crisis, assuming that recent events matter most when answering life satisfaction evaluation questions.

**Figure 9. Crisis severity and changes in life satisfaction**

<!-- Start of picture text --> .5 Azerbaijan<br>Montenegro<br>Mongolia<br>Bosnia-HerzegovinaMacedonia<br>Serbia Turkey<br>Moldova Poland<br>Georgia<br>Bulgaria<br>Tajikistan<br>0 Armenia RussiaCzech Republic<br>Slovak Rep.<br>Albania<br>Ukraine Hungary Kyrgyz Rep. Kazakhstan Uzbekistan<br>Croatia<br>Estonia<br>Slovenia<br>Belarus<br>Latvia<br>Lithuania<br>Romania<br>-.5<br>-10 -5 0 5 10<br>Average GDP growth in 2009-2010 (percent)<br>Change in average life satisfaction (2006-2010)<br><!-- End of picture text --> Source: LiTS I (2006) and LiTS II (2010), IMF WEO (October 2010 Update).

Note: Change in average life satisfaction is the difference between the 2006 and 2010 average life satisfaction, measured on a 1-5 scale (see footnote 20).

19
such as social security and labor policies (including education and training). Well-designed credit, housing and place-based polices may also facilitate adjustment. Trade-specific adjustment programs may play a complementary role. B&R economies more exposed to competition shocks should consider whether their inclusive policies are appropriate to deal with the adjustment costs imposed by trade shocks, and potentially include these policies in the negotiated trade package. While this paper aimed to provide a general overview of the exposure of each B&R economy to supply and demand shocks associated with further integration with China, more definite conclusions require complementary analysis based on production and employment data, along with a deeper assessment of country-specific institutions.

# **References**

Almeida, R., Behrman, J. and Robalino, D. (2012). “The Right Skills for the Job?” Human Development Perspectives, World Bank.

Autor, David, David Dorn, Gordon Hanson, and Jae Song (2014). “Trade Adjustment: Worker Level Evidence”, _Quarterly Journal of Economics_ , 129(4): 1799-1860.

Autor, David David Dorn, Gordon Hanson (2015). “Untangling Trade and Technology: Evidence from Local Labor Markets”, _Economic Journal_ , 125(584): 621-646.

Autor, David, David Dorn, and Gordon Hanson (2013), “The China Syndrome: Local Labor Market Effects of Import Competition in the United States”, _American Economic Review_ , 103(6): 2121-2168.

Bloom, Nick, Mirko Draca and John Van Reenen (2015). “Trade induced technical change: The impact of Chinese imports on innovation, diffusion, and productivity,” _Review of Economic Studies_ , 83: 87-117.

Branstetter, Lee, and Nicholas Lardy (2006). “China’s embrace of globalization” NBER Working Paper 12373.

Broda, Christian and David Weinstein (2006). “Globalization and the gains from variety.” _Quarterly Journal of Economics_ , 121(2): 541-585.

Busso, Matias, Jesse Gregory and Pat Kline (2013). “Assessing the incidence and efficiency of a prominent place-based policy,” _American Economic Review_ , 103: 897-947.

Chen, Yuyu, Ginger Zhe Jin, and Yang Yue (2010). “Peer migration in China” NBER Working Paper 15671.

40
Two main findings are highlighted by this exercise and illustrated in tables 2 and 3 below. Firstly, most displaced routine workers end up either not employed or in non‐routine manual jobs and only a few can move up to non‐routine cognitive jobs. In addition, disproportionally more women, and younger and less educated people fall down in the distribution of occupations when de‐routinization hits. Both these shifts translate into large losses in terms of earnings, as shown in figure 11, and ultimately explain the rising inequality of the distribution of earnings.

More in details, Table 2 describes for the three countries, where each individual of a group of 100 counterfactual routine workers actually ends up in the 2013 observed data. Consider, for example, the case of Germany: 15 workers are now not employed, 8 of them are in non‐routine manual jobs, and only 6 are in non‐routine cognitive jobs; and up to 71 remain in routine jobs. For all the countries the movement into non‐routine manual intensive jobs is larger than the movement into non‐routine cognitive task intensive jobs, which is consistent with the predictions of Autor (2010) for the US.

<u>Table 2 – Flows out of routine‐task intensive occupations</u>

|||Observed o|ccupations||Counter‐<br>factual|
|---|---|---|---|---|---|
||Not employed|Non‐routine,<br>manual|Non‐routine,<br>cognitive|Routine|All in routine|
|||Movers||Stayers||
|Germany|15|8|6|71|100|
|Poland|10|15|9|66|100|
|Spain|24|11|3|62|100|

Source: Authors’ calculations. Note: This table presents the observed occupations of 100 individuals who would be, in the counterfactual scenario, employed in routine task intensive occupations. For example, the first column shows individuals who are ‘not employed’ but that in the counterfactual simulation (obtained by shifting the parameters of the occupational model from their current 2013 values to the values they had at the beginning of the 1990s) would have been employed in routine task intensive occupations. The second column shows individuals who are employed in ‘non‐routine, manual task intensive occupations’ but would have been employed in routine task intensive occupations in the counterfactual scenario, and so on.

The characteristics of displaced routine workers who end up jobless or in lower occupations are quite different from the characteristics of those who remain in routine jobs or of those who move up to better paid cognitive jobs. In Germany, as shown in table 3, the share of female workers is higher among the displaced routine workers who move into non‐routine manual and cognitive occupations, while the share of males is higher among those who become unemployed. In contrast, in Poland and Spain displaced workers are more likely to be female regardless of their new status when compared to those who kept their routine occupation in the simulation. In Germany, Poland and Spain, routine workers who move into 26
Policy Research Working Paper 10749

# **Abstract**

The 2022 Household Income and Expenditure Survey enhances fieldwork, data management, and information quality but poses comparability challenges with previous rounds. This study proposes a two-step process based on statistical matching to fill the information gap in previous survey rounds. This methodology uses the more comprehensive 2022 information to reconstruct comparable consumption measures over time. This allows for a consistent assessment of poverty and inequality measures, providing insights into the changes for policy makers, researchers, and stakeholders over the years. The results reveal that integrating this correction into previous survey rounds would have reduced poverty rates by around 10.6 percentage points between 2010 and 2016 and a further decrease of 7.8 percentage points between 2016 and 2022. Likewise, extreme poverty rates would have witnessed a decline of approximately 3 percentage points in the earlier period and a more substantial drop of 3.6 percentage points in the more recent one. These poverty reduction trends mirror improvements in other dimensions of well-being, like reductions in infant mortality and stunting and increases in access to electricity, sanitary toilets, and literacy rates.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jfernandezromero@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
of children's growth. In Section 6, we describe the variables that were selected to be included in regression analyses of child anthropometry on the basis of theory, previous results and data availability. In Sections 7 and 8 we present and comment on the results of our modeling of child growth in rural Guatemala. Finally, in the conclusions section we summarize the main findings and discuss their policy implications.

# **II. CHILD ANTHROPOMETRY**

# **2.1 Measurement of Child Health Status with Anthropometric Data**

Among anthropometric measures, weight-for-height, height-for-age and weight-for-age indicators have been utilized extensively. Height-for-age, weight-for-height and weight-for-age deficits are commonly interpreted as indicative of chronic, acute and total malnutrition respectively. It is very important, however, to recognize that height and weight are measures of growth attainment rather than nutritional status _per se_ (Martorell 1982; WHO Working Group 1986).

The focus of this research is on children's height-for-age outcomes. We use the term malnutrition to refer to the cumulative consequences of inadequate intake of protein energy and micronutrients and frequent infection, as reflected in children's reduced skeletal growth. Given the cumulative nature of the human height process, other information beyond that obtained in a one-time measurement is needed to separate the effects of nutrition from those of genetic factors, past infections and other factors which affect the growth attainment of an individual child. Nevertheless, from a large sample of cross-sectional measurements it is possible to draw meaningful conclusions at the population level about the prevalence and the determinants of children's stunting.

# 2.2 **The International Reference Population**

The evaluation of growth attainment requires the use of a reference population that allows for normal variation at any age. Although there are obvious ethnic differences between adults, children of different ethnicity have the potential to achieve similar levels of growth attainment in the first few years of life. Many comparative empirical studies have demonstrated the greater importance of socioeconomic factors as opposed to race and ethnicity in determining children's height. Data from India (Rao and Sastry 1977) and Guatemala (Johnston, Borden and MacVean 1973) suggest that ethnic differences in growth potential are minor prior to puberty and that it is during this stage that major differentiation between ethnic groups takes place. Clear differences surfaced during the adolescence period for both sexes:

> See Martorell and Habicht (1986) for an excellent review of the main research findings on the characteristics of children's anthropometry in developing countries.

2
- Opoku-Afari, Maxwell, Morrissey, Oliver & Lloyd, Timaballero, 2004 – _Real exchange rate response to capital inflows: a dynamic analysis for Ghana._ Research paper No. 04/12. Published by the Center for Research in Economic Development and International Trade, University of Nottingham.

- Pindyck, Robert S., & Rubinfeld, Daniel L., 1998 – _Econometric models and economic forecasts._ Fourth edition, published by McGraw-Hill.

- Pitts, Graham Auman, 2021 - _Was Capitalism the Crisis? Mount Lebanon’s World War I Famine_ . Environment & Society Portal, Arcadia (Spring 2021), no. 3. Rachel Carson Center for Environment and Society. doi:10.5282/rcc/8801

- Presidency of the Council of Ministers, 2022 - Central Administration of Statistics _, Annual National Accounts 2004-2020_ .

- Presidency of the Council of Ministers, Economic Accounts mission, 2006 - _Economic Accounts of Lebanon, 2004._

World Bank, 2023 - _World Development Indicators_ .

16
# **V. Instrumental variable estimates of employment and earnings Using an instrumental variable to correct endogeneity bias**

The previous section shows that after correcting data censoring and factoring in the impact of the higher levels of participation in tertiary education by SMA graduates, graduation from an SMK does not seem to improve the chance of obtaining employment or an earnings premium, as commonly believed. However, an important point that could cast doubt on this conclusion is that there may be systematic differences between the types of student who attend SMK and SMA respectively. For example, if those who attend SMK are initially characterized by certain disadvantages that affect their labor market outlook, then the fact that the rates of employment and average salaries are similar to those of SMA students may indicate that SMK can actually improve their opportunities when they enter labor market. In other words, the endogeneity of SMK choice can lead to bias in estimating the effect of attendance at SMK on labor market outcome. In the example here, it would result in underestimating the positive effect of attendance at SMK.

This paper uses an instrumental variable to correct the endogeneity bias. The instrumental variable used reflects the supply of SMK in the community. IFLS household surveys are accompanied by community facility surveys, which include a survey of information regarding schools in communities. In the survey, names of candidate schools were obtained from household responses, in which the household head verified the name and location of all schools currently attended by household members under age 25. These schools constituted the sampling frame and were listed in order of the frequency with which they were mentioned. The final sample consists of the most frequently mentioned facilities. The proportion of SMK schools in all frequently mentioned high schools in the community is used as the instrumental variable.

The coefficient estimates of a binary choice model for SMK and SMA are reported in Table 6. The effect of the instrumental variable, the proportion of SMK schools among all frequently mentioned high schools, is highly significant in determining SMK/SMA choice.

16
herding is expected to decrease with the transparency of the assets for a given level of risk.<sup>2</sup> In other words, securities for which information is widely available and that entail less risk are less likely to induce herding patterns. We thus exploit our dataset to analyze herding by asset type by the same institutional investors. In particular, we calculate herding in corporate bonds, financial institutions bonds, government bonds, mortgage bonds, and equity.

Second, herding might also be explained by managers following similar trading strategies like momentum (Froot et al., 1992 and Gompers and Metrick, 2001). Momentum trading, defined as the purchasing (selling) of assets whose returns are positive (negative), is a popular investment strategy and has been often found in developed and developing countries. Its presence among US institutional investors (primarily mutual funds) has been widely documented in the literature.<sup>3</sup> Our data on returns allows us to test whether, to the extent that there is herding, it is driven by momentum strategies.

Third, managers might herd as a way to reduce risk. While traditional theories of asset allocation focus on the problem of an isolated investor whose goal is to maximize wealth or consumption at some point in time, several papers study the incentives schemes that arise in the context of financial intermediation. In particular, the conflicts of interest between fund managers and the underlying investors can affect manager risk-taking behavior (Sharfstein and Stein, 1990; Shleifer and Vishny, 1990; Chevalier and Ellison, 1999; Graham, 1999; Stein, 2003, 2005;

> 2 Although theoretical models of herding behavior do not focus on the characteristic of the assets as a determinant of herding, they typically imply a relation with opacity. For instance, in Sharfstein and Stein (1990), herding can only occur when there are “systematically unpredictable factors affecting the future state that nobody can know anything about,” that is, under the presence of an unobservable common component to prediction errors. This type of situation is more likely in less transparent assets. In transparent assets, most of the prediction errors should be idiosyncratic. Similarly, in the model of Banerjee (1992), the probability that no agent chooses the right investment option because of herding is declining in the probability that an agent receives a signal about the state of nature, and in the probability that the signal is true. In the context of asset selection, these probabilities are likely to be lower for more opaque assets.

> 3 See, for example, Grinblatt et al. (1995), Nofsinger and Sias (1999), Grinblatt and Keloharju (2000), Kaminsky et al. (2004), Sias (2004, 2007), and Greenwood and Nagel (2009).

5