rafmacalaba/datause-encoder-data huggingface dataset:
investment decisions (Baum 2022), as firms often train workers in firm-specific tasks, and workers themselves gain tacit and firm-specific knowledge the longer they are employed in a given job – which in turn makes them more productive. The relationship between tenure and productivity may not, however, be linear and, in fact, it may plateau at very long tenure values (Gagliardi, Grinza and <u>Rycx 2022). Finally, from a household perspective, changes in job tenure also impact wealth</u> accumulation. Households with more stable jobs -i.e., longer-tenured jobs- accumulate more wealth even when controlling for income (Kuhn and Ploj 2020). This effect grows with age, and workers who start their careers in unstable jobs see their whole life-cycle wealth accumulation affected. Analyzing the evolution of job tenure can thus be informative about changes in the labor market and also provide indications of the expected evolution of household welfare.

Motivated by these arguments, this paper first main objective is to provide an accurate assessment of the evolution of job stability, measured by changes in job tenure, across generations in Europe. The analysis is based on data from the Eurostat Labor Force Surveys (EU–LFS) on 29 European countries over the period 1995–2020 (Eurostat 2021)<sup>4</sup> .

The initial assessment is that job tenure has shrunk for workers from more recently born cohorts. For example, a worker born in 1980 has, on average, 3.6 years shorter tenure than a worker born in the 1940s. This result is obtained using an age-period-cohort decomposition of the group-level trends in job tenure, controlling for lifecycle changes and aggregate labor market shocks that are common across age cohorts.

This result is also confirmed when we model the duration of tenure at the individual level to account for changes in the labor market that might affect the composition of characteristics within the cohorts. We estimate the probability of having a job of three lengths of tenure—short, medium, and long—conditional on a set of individual characteristics (such as age, gender, and education) and employment-related characteristics (such as occupation and the size of the firm). After controlling for these characteristics, the probability of having a medium- or long-term job declined and having a short-term job increased over the survey period. The probability of having a short-term job

> 4 We replicated the analysis presented in this paper on a sample that excludes data from the 2020 EU-LFS. Our concern was that the COVID-19 pandemic and the non-pharmaceutical interventions implemented by EU governments had such a profound impact on the EU economies and labor markets that 2020 could be an outlier in tenure trends and other dimensions we study in this paper (see <u>Demirguc-Kunt et al. 2021). However, the results of these estimations are</u> qualitatively very similar to the results based on the 1995 – 2020 sample. (These results are available from the authors on request.)

3
# **How Concentrated are Global Infrastructure Markets?**

### **Daniel Benitez**

Université des Sciences Sociales Toulouse I daniel.benitez@univ-tlse1.fr

### **Antonio Estache**

World Bank and ECARES, Université Libre de Bruxelles aestache@worldbank.org

## **Abstract**

In infrastructure, the possibility of a positive relationship between operators’ profitability and the degree of concentration is a major political issue in view of the wide diversity of feelings about the potential role of the private sector. This is particularly important in view of (i) the large residual degree of monopolies, (ii) the protection they are granted through exclusivity clauses built in service delivery contracts and (iii) the widespread sense that the same operators tend to be present in most of the privatized operations. The main purpose of this paper is to provide a first set of quantitative assessments of the degree of concentration in infrastructure at the global and at the regional level. Concentration issues were identified in only about 20% of the cases studied, a presumption of concentration was found in another 30% of the cases. We find no correlation between the degree of concentration and the degree of reform adopted by a region or a sector. In more general terms, we find no scope for simple encompassing regional or sectoral statements because issues are region- and sector-specific. We conclude by arguing that there are a few cases and regions in which it would make sense for a supranational competition or regulation agency to ensure that the interests of the users are protected more effectively against the risks of collusion and other types of anti-competitive behaviors local regulators would not be equipped to address.

#### World Bank Policy Research Working Paper 3513, February 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ We are particularly grateful to Marcelo Celani, Claude Crampes, Mathias Dewatripont, Jose-Luis Guasch, Anne Perrot, Russell Pittman, Richard Schlirf, Tomas Serebrisky, Chris Snyder and Lourdes Trujillo for useful conceptual discussions and suggestions in the (slow) preparation of this note.
**Table 4:** Parametric estimates of regional childhood exposure effects

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||IGM|IGM|IGM|IGM|IGM|IGM|IGM|IGM|IGM|IGM|IGM|IGM|
|_β_: 1-4, base|-0.000785|-0.0267**|-0.0144|-0.0319*|0.0114|-0.0429*|0.00791|-0.00404|0.0106|0.0322***|0.0236|0.0465*|
||(0.008)|(0.010)|(0.009)|(0.017)|(0.016)|(0.023)|(0.009)|(0.033)|(0.017)|(0.011)|(0.029)|(0.025)|
|_γ_: 5-11, base|0.0491***|0.0409***|0.0517***|0.0404***|0.0333***|0.0462***|0.0597***|0.0424***|0.0636***|0.0501***|0.0474***|0.0583***|
||(0.004)|(0.006)|(0.004)|(0.007)|(0.011)|(0.009)|(0.004)|(0.007)|(0.006)|(0.007)|(0.011)|(0.007)|
|_δ_: 12-20, base|0.0288***|0.0188***|0.0350***|0.0282***|0.0253**|0.0311***|0.00301|0.00610|0.00707|0.0244***|0.0261**|0.0378***|
||(0.004)|(0.004)|(0.004)|(0.010)|(0.011)|(0.009)|(0.006)|(0.006)|(0.007)|(0.009)|(0.010)|(0.013)|
|_β_: 1-4, dif|0.00580|0.0103|0.00804|0.0383**|-0.0430*|0.0284|-0.00961|-0.0260|-0.0313*|-0.0414**|-0.0730|-0.0866**|
||(0.010)|(0.018)|(0.011)|(0.018)|(0.025)|(0.025)|(0.010)|(0.029)|(0.018)|(0.019)|(0.053)|(0.037)|
|_γ_: 5-11, dif|0.000200|-0.00371|-0.00182|0.0112**|0.00726|0.00708|-0.0155***|-0.00562|-0.0183***|0.00139|-0.0125|-0.00720|
||(0.003)|(0.003)|(0.004)|(0.005)|(0.012)|(0.008)|(0.006)|(0.008)|(0.006)|(0.010)|(0.013)|(0.012)|
|_δ_: 12-20, dif|-0.0361***|-0.0195***|-0.0402***|-0.0144|-0.0138|-0.0125|0.0264**|0.0142|0.0286***|-0.0116|-0.0186|-0.0229*|
||(0.006)|(0.004)|(0.008)|(0.010)|(0.010)|(0.009)|(0.013)|(0.011)|(0.010)|(0.011)|(0.012)|(0.013)|
|R-squared|0.105|0.689|0.103|0.126|0.684|0.125|0.099|0.685|0.096|0.097|0.685|0.095|
|N|436792|271984|271984|422994|263373|263373|433793|270266|270266|436792|271984|271984|
|Household FE|No|Yes|No, hhfe sample|No|Yes|No, hhfe sample|No|Yes|No, hhfe sample|No|Yes|No, hhfe sample|
|Base category|Male|Male|Male|Rural|Rural|Rural|Nonowner|Nonowner|Nonowner|Negative|Negative|Negative|

Notes: The dependent variable in all specifications is an indicator that takes the value of one for children of parents without completed primary school who have completed at least primary education and zero otherwise (IGM). The independent variables comprise a linear in origin-average-IGM (calculated for the birth-cohort relevant to the individual among nonmovers) term, age-at-move indicator variables, birth-decade _×_ destination indicators interacted with destination-minus-origin differences in upward IGM, all of which are not reported, and three linear terms for destination-minus-origin differences in the relevant-birth-cohort-nonmover average IGM for moves taking place when the child moves, ages 1-4, 5-11, and 12-18. Double clustered at the origin and at the destination region standard errors are reported in parenthesis.
corridors. Combining gravity coefficients to estimate the program’s trade impact with World Bank data on road improvement costs ($127,000/km for the median project) they found that the payback horizon would be barely over one year, with $254 billion of additional trade generated over the project’s lifetime at a cost of about $32 billion. A similar exercise performed by Shepherd and Wilson (2006) for the ECA (Europe and Central Asia) region reached similar conclusions: A complete upgrading of the road infrastructure in Hungary, Romania and Albania (at about $227,000/km) would generate an “on-impact” (annual) trade increase of over $35 billion for a onetime cost of $3 billion.

The extremely high rates of return on road investments identified in the trade literature were substantially above, but broadly consistent with, high rate-of-return estimates found in the macro growth literature—typically over 200%—which put road investments on top of other types of infrastructure investments such as telecommunications or energy (see Estache, 2007).<sup>10</sup> Thus, after almost two decades of multilateral donor emphasis on structural adjustment and policy reform, by the mid-2000s empirical research was suggesting that the pendulum should swing back toward (infrastructure) capital accumulation.

# _c. The ‘Soft’ Side: Customs and Regulation_

For interventions such as customs reform, it may be possible in principle to generate a control group by introducing elements of targeting through progressive phase-in during a pilot phase, staggered for example across different border posts, or through selective implementation covering only some

> 10 These too-good-to-be-true rates of return were reminiscent of the “Aschauer debate” on infrastructure and growth (see Estache and Fay 2007 and references therein for an overview). An internal evaluation of World Bank infrastructure projects over 1999-2003 produced an economic rate of return of 43%, by all means a respectable rate but nowhere near the miracles suggested by the literature (see Estache 2007). However, the ranking of rates of return also put road investments on top, suggesting the same lending priorities.

11
migration on income distribution.

# **7.1. Estimation of the participation and income equations**

Table 2 reports the estimates of the participation equation base on Probit model. Most variables carry the expected signs. The coefficient of household’s land area is significantly negative. The shortage of land, the major physical capital of a household, is an important motivation of migration. Households with more workers are more likely to participate in migration. Other things being equal, a larger household will have a lower opportunity cost of having some members working outside.

## **Table 2 - Estimation of the participation equation (Probit)**

Endogenous <u>variable = 1 if household participates in migration</u>

||Regressi<br>Fo<br>rall hous<br>|on 1<br>s<br>ehold<br>|
|---|---|---|
|Number of workers in the household|0.256 ***|(7.59)|
|Average number of years of education (ref.: 0-4 years)|||
|4-8 years|0.393 ***|(3.00)|
|8-10 years|0.570 ***|(4.11)|
|10 years or above|0.372 **|(2.26)|
|Nu<br>mber of dependents|-<br>0.048|(-1.37)|
|Number of children|0.125|(1.15)|
|Land area of the household|**<br>-0.013 *|(-2.59)|
|Distance from household’s residence to the bus station|-0.020 ***|(-3.48)|
|Distance from household’s residence to the county capital|-0.007 **|(-2.22)|
|Distance from household’s residence to the rural fair|-0.002|(-0.24)|
|Logarithm of per capita gross output value of the village|-0.033|(-0.39)|
|Proportion of non-farm production of the village (/100)|0.058|(0.29)|
|Cultivable land per capita of the village|-0.017|(-0.24)|
|Proportion of paddy field of the village (/100)|0.417|(1.22)|
|Proportion of vegetable field of the village (/100)|-0.097|(-0.31)|
|Constant|-0.525 *|(-1.86)|
|Maximum likelihood in log|.65<br>-730|3|
|Pseudo_-_<br>2<br>_R_|0.094||
|Percenta<br>o<br>ge f correction predictions (%)|66.9||
|Number of observations|1208||
|Note: t-statistics are in brackets. *** significant at 1%; ** signific|ant at 5%; * sig<br>nific|nt at 10%.<br>a|

Households with better educated labor are more likely to participate in migration for two 18
**Table 3: Summary of household characteristics in Uganda, 1999 and 2002**

||||**Reg**|**ions**||||**Quinti**|**les**||
|---|---|---|---|---|---|---|---|---|---|---|
||**All Uganda**|**Central**|**East**|**North**|**West**<br>**2002**|**1st**|**2nd**|**3rd**|**4th**|**5th**|
|Head's age (years)|39.12|38.74|39.36|39.39|39.13|44.21|41.01|38.64|35.81|34.42|
|Female head (%)|24.53|27.08|21.34|30.21|20.39|25.84|23.16|21.80|22.53|29.20|
|Head's education (years)|6.11|7.18|5.73|5.11|5.91|3.80|5.22|5.90|7.41|8.91|
|Maximum education in household|8.03|9.12|7.77|6.46|8.08|5.85|7.30|8.01|9.03|10.56|
|Household size|5.23|4.88|5.58|5.19|5.36|6.23|5.77|5.43|4.66|3.77|
|Monthly consumption expenditure (US$, 2002)|85.27|123.55|71.77|46.82|79.42|34.21|54.26|72.49|94.21|187.31|
|Monthly expenditure on health (US$, 2002)|5.00|5.74|4.85|2.75|5.99|1.49|2.55|4.38|6.17|10.40|
|Value of assets (US$, 2002)|1,528|1,867|1,327|836|1,825|833|1,206|1,520|1,771|2,519|
|Iron sheet roof|64.42|88.36|54.72|14.10|81.78|38.14|56.03|68.91|76.66|89.77|
|Cement floor|26.04|49.22|15.66|7.12|21.54|4.31|10.23|22.86|34.43|65.74|
|Toilet facility|74.96|80.64|65.03|58.32|90.62|57.40|72.25|77.64|84.74|87.35|
|Rural households|83.27|66.92|90.02|92.11|90.40|96.55|94.47|88.61|80.03|51.99|
|Observations|9,711|2,831|2,675|1,730|2,475|1,898|1,897|1,897|1,897|1,897|
|||||||**1999**|||||
|Head's age (years)|42.88|41.89|43.25|42.47|44.11|47.34|45.26|42.78|40.08|36.81|
|Female head (%)|25.18|27.65|21.57|32.24|20.38|27.21|23.35|23.38|24.58|27.63|
|Head's education (years)|5.11|6.09|5.09|4.04|4.67|3.13|4.42|4.96|5.88|8.10|
|Maximum education in household|7.10|8.05|7.11|5.53|7.06|5.10|6.58|7.09|7.85|9.75|
|Household size|5.45|5.01|5.56|5.60|5.80|6.37|6.07|5.51|4.91|3.89|
|Monthly consumption expenditure (US$, 2002)|84.30|119.40|71.96|43.54|83.11|34.81|57.17|73.50|96.16|188.90|
|Monthly expenditure on health (US$, 2002)|4.92|6.46|3.63|3.06|6.26|1.71|3.13|4.41|7.02|10.87|
|Value of assets (US$, 2002)|1,445|2,336|1,047|361|1,547|495|882|1,170|1,980|3,245|
|Iron sheet roof|58.24|84.45|48.95|9.10|72.04|29.88|53.85|61.10|70.74|86.19|
|Cement floor|21.87|42.13|15.38|4.61|15.60|2.79|8.75|16.15|31.53|61.82|
|Toilet facility|65.77|76.74|55.02|42.21|81.39|47.02|64.15|69.54|76.69|77.26|
|Rural households|84.41|67.25|90.14|94.47|93.03|97.81|95.24|91.19|81.44|46.57|
|Observations|10,695|3,110|2,865|1,801|2,919|2,082|2,081|2,081|2,081|2,081|

Source: Own calculation from UNHS 1999 and 2002.

22
asymmetry, as it seemed to be impossible for the patient to keep track of as to which of the 1,090 procedures covered by RSBY was performed on him or her”.

Jain (2021) is the first study from an LMIC that looks at hospital pricing and coding systematically in the context of an insurance scheme. She combines administrative claims data with a large household survey for the Indian state of Rajasthan, which allows her to better understand how hospitals react to changes in administrative prices. Without the household survey, for instance, it would have been impossible to determine how much households are asked to pay out-of-pocket because the practice is illegal and therefore off the books. She finds that providers do not respect administrative prices: 41 percent of patients paid for their treatment even though the care was supposed to be free and the average payments were $35, which is a large sum for poor households and represents a 37 percent increase over the insurance reimbursement rate.

Moreover, hospitals react rapidly to adjustments in reimbursement rates. Jain (2021) finds that with every additional Rs.100 in reimbursements, prices charged to patients decreased—but only by Rs.55. She also uses an event-study to show that when the relative reimbursement rates within a category change (for instance, childbirth with and without an episiotomy), so do the reported procedures. Within a week of a price change, a 1 percent increase in the reimbursement rate induced a 0.4 percent increase in its claim volume. She suggests that this reflects up-coding, whereby health care providers submit codes for more expensive care than actually provided, but a bigger worry, which she does not rule out, is that hospitals changed the treatments that patients received.

Other studies provide systematic evidence on differences in quality of care by insurance status, at least for outpatient care. One set of studies finds that when patients get health insurance, their satisfaction remains the same or worsens (Bauhoff, Hotchkiss, and Smith 2011 and Robyn et al. 2013). Two studies have used standardized patients, varying their insurance status across visits. In South Africa, Sripathy (2020) shows that clinical effort is higher for standardized patients with insurance, but the proportion correctly treated does not change and the extent of unnecessary and more expensive treatments increases. In China, Lu (2014) finds a similar result, with the greater use of unnecessary antibiotics for insured patients, but only when doctors have a direct financial incentive associated with the purchase of the medicine. In the Philippines, Gertler and Solon (2000) find that because hospitals do not charge the mandated administrative price, insured patients pay 86 percent of what the uninsured would pay, sharply limiting any financial protection offered by the scheme. Finally, in Burkina Faso, Fink et al. (2013) find that 21
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thus not be perfectly consistent with other data sources for any specific variable (e.g. 2001 exports of a specific commodity between a pair of countries, measured at the tariff-line from COMTRADE). However, they provide the global consistency necessary for analytical modeling. Also, given that official African trade data are of varying quality and may not account for informal trade flows (RATIN, 2009), the GTAP Database has the advantage of being a globally consistent and balanced database. Additionally, the analysis in this paper focuses on the sensitivity of this ‘representative’ Tanzanian economy<sup>9</sup> to a very specific set of stressors, where the comparability of the effects of the different realizations of the stressors (i.e. the climate shocks) will be maintained as long as the same benchmark representative dataset is used in every realization.

We retain the empirically robust assumptions of constant returns to scale and perfect competition, and introduce factor market segmentation, following the methodology of Keeney and Hertel (2005). This segmentation is particularly important in countries where poverty occurs in rural areas. Farm and non-farm mobility of factors are restricted by specifying a constant elasticity of transformation function which allows “transformation” of farm employed labor and capital into nonfarm uses and vice-versa. This allows for persistent wage differences between the farm and non-farm sectors, and is the foundation of the inter-sectoral distributional analysis. In order to parameterize these factor mobility functions we draw on the OECD’s (2001) survey of agricultural factor markets – the best available database of the necessary elasticities with the widest country coverage. However, given the uncertainty about the appropriate values of these parameters for Tanzania, we conduct a systematic sensitivity analysis with respect to these parameters. We assume a constant aggregate level of land, labor, and capital employment reflecting the belief that the aggregate employment of factors is unaffected by the climate shocks that are affecting grain production.

The model is adjusted to distinguish between land classes with different biophysical characteristics, following the approach of Hertel et al. (2009). This distinguishes land by Agro-Ecological Zone (AEZ), based on the data of Lee et al. (2009) and Monfreda et al. (2009). The model is then

> 9 The data for domestic production and consumption in Tanzania is based on a 1992 input-output table that has subsequently been updated to be consistent using 2001 macro-economic and trade data. While a more recent input-output for Tanzania would be preferable, this was the most recent data made available during the construction of the GTAP Database, and has passed several consistency checks. The full details on the input-output contribution, the database construction process, and the consistency checks can be found in the documentation for Dimaranan (2006).

11
in the burden of care. The country has smaller wage gaps than the OECD average, but this gap is associated with the unexplained component of the observables and is greater at the extremes of the income and education distributions.

# **3. Data**

# **Sources and definitions**

We use the Great Integrated Household Survey (GEIH), a probabilistic sampling survey that gathers data on individuals' employment status, including their occupation, income, access to social security, and job search activities, as well as their demographic characteristics such as gender, age, education level, and sources of income. The GEIH is nationally representative and uses a self-weighted sampling method stratified by unequal clusters. The survey's primary aim is to address the factors that affect labor participation and estimate gender-based labor gaps. Additionally, the survey includes information at the regional, departmental, and department capital levels.

The GEIH's sample frame was updated in 2022 with information from the 2018 National Population and Housing Census (CNPV), resulting in a structural change in trend series compared to the previous sample frame collected until December 2021. Because our work requires comparing results over time since 2015 and using information from the Mission for the Splicing of the Employment, Poverty, and Inequality Series (MESEP) as a source for labor income variables, we use the 2005 frame GEIH data between 2015 and 2021.<sup>5</sup> The labor income variable comprises five categories of income that vary depending on the type of employment relationship, including First Activity Monetary Income (IMPA) for wage earners and self-employed, Income in Kind (IE) for wage earners only, Second Activity Income (ISA) for wage earners, self-employed, and family workers, Monetary Income of the Unemployed and Inactive (IMDI) for the unemployed and inactive, and Income from other sources (IOF) available to everyone. From 2009 onwards, the survey covers 437 municipalities and annually visits an average of 248,000 households concentrated in 22,548 segments.

There are limitations to the survey data collected in 2020. Due to government-imposed restrictions in the first few months of the pandemic, data collection in cities was done over the phone. From March to April, the survey was restricted to 39 questions from the usual 200 to reduce respondent rejection. Moreover, data collection restrictions were only relaxed from June onwards, making it unclear how these measures affected data collection and comparability. Therefore, we only make comparisons with 2020 when analyzing primary labor indicators such as unemployment, participation, and employment, as we have information for March and April. When including 2020

> 5 Nevertheless, the previous section made a descriptive reference to the main indicators for the years 2021-2022 with the information from the new sample framework of 2018.