rafmacalaba/datause-encoder-data huggingface dataset:
_Mexico: The 1997 Pension Reform_ _9_

- a new mandatory 2% employer contribution on wages paid to private and public sector employees channeled into individual retirement sub-accounts. These funds are channeled to the Central Bank through the commercial banking system. The Central Bank guarantees a real rate of return of at least 2% on these retirement sub-accounts which by end-1995 totaled about US$ 2.9 billion invested in Government securities; and

- a specialized housing sub-account managed by INFONAVIT. Although the contribution remained at 5% of wages, INFONAVIT's benefit provision was changed to accumulated balances (i.e., accumulated contributions plus a real return on these) as opposed to only nominal accumulated contributions. The Banco de Mexico transfers the resources of the housing subaccount to INFONAVIT. The institute would pay interests according to the operational surplus for the corresponding year.

Table 3 summarizes overall pre-reform social security contributions for workers in the private sector, including health insurance, professional risks insurance and childcare.

**Table 3: Social Security Contributions as Percentage of Payroll**

|-<br>,-1M<br>5,'.i :i :i'_ii_**0.6.**|E,32i<br>zMii-i<br>WRSSerMgd afor<br>ve3<br>ve--<br>**..........l**<br>rd<br>-~~|**8.5**<br>**3**<br>1.5<br>~~~~~~~<br>-<br>**0.4**|70<br>~~0.6|25|_5_|
|---|---|---|---|---|---|
|**-E-**<br>EiE|**g- . .ii**<br>**ilii**<br> i i-.^iiii.i* akMFgS12.5||70|25|_5_|
||HV,1isi<br>ft0*025W100|||**0**|**0**|
||iiiiiiiiiiiiii___i_____-,<br>|1.0<br>|100|0|0|
|_j:SAR-'T4_|_~;°~%~AWt_<br>**_ii5_**<br> <br>|<br>|**100**|0<br>°|0o 1|
||<br>.g**kotfr:ni**<br>l<br>|**2Vl**<br> <br>2<br>|**100**<br>|**0**<br>|<br>**0**<br>l|
|.<br>'|<br>i_,!<br>.<br>,~<br>' *****<br>'**y**|<br>15.5<br>|[_________<br>l|____||
|**Total**||,..<br>§|<br>:-g<br>.<br>25.2|-A-g<br>S<br>5.25|<br>"- Ji31.5<br>_1.05_|

Source: **IMSS**

# **_Lessonsfrom the SAR Experience_**

Substantial institutional and conceptual problems led to the failure of this first attempt to develop a complementary pension scheme to supplement the system's first pillar. The SAR's problems quickly discredited it arnong the population.

Slow progress and final failure of the SAR, can be attributed to:

- _lack of any consistent supervision of accounts held by commercial banks and the lack of a final regulatory framework for investment managers._ The regulatory agency - the National Commission for the Retirement Saving System _(Comision Nacional de Sistemas de Ahorro para el Retiro -_ CONSAR) - was established in July of 1994, two years after the SAR began operations;

Labor Law Article 165 as 90 days of minimum daily salary for each year of service) with either defined-benefit or definedcontribution schemes. Affiliation to the third pillar cannot substitute for affiliation to other pillars.
The conditional posterior of _g_ given _Mj_ is simply<sup>9</sup> The advantage of using the Gibbs sampler on ( _g, Mj_ ) is that it does not rely on approximations that are hard to control and makes prediction quite straightforward: for every _g_ drawn in the sampler we predict as with a fixed _g_ (Fern ́andez _et al._ , 2001a), and predictions are simply mixed over values of _g_ in the sampler. With Laplace approximations this seems much less straightforward and the quality of these approximations is not that easy to assess. Also, truncation, as in the Bottolo-Richardson and Forte _et al._ priors, can be dealt with easily in a Gibbs sampling framework.

We use a simple random walk Metropolis-Hastings step for _g_ with a log-Normal proposal centred over the previous value. Finally, we control the acceptance probability for _g_ by making the Metropolis-Hastings step adaptive.

Throughout, we use MCMC chains of length 1,000,000 after a burn-in of 500,000, which was found to be more than sufficient for convergence. The Fortran code used for this paper is available from the authors upon request.<sup>10</sup>

# **7. Simulations**

First, we examine simulated datasets to mimic the kinds of situations typical for many applications in econometrics (and in other areas as well). We generate datasets from three different model structures, which closely correspond to those in Fern ́andez _et al._ (2001a). For Models 1 and 2 we generate an _n × k_ matrix _R_ for _k_ = 15 regressors where the first ten columns ( _r_ (1) _, . . . , r_ (10)) are drawn from independent standard normal distributions, and the next columns are constructed as where _E_ denotes an _n ×_ 5 matrix drawn from independent normal random variables. This induces correlations between the first five and the last five regressors ranging from 0.153 to 0.74. The demeaned version of _R_ is then the matrix of regressors _Z_ = ( _z_ (1) _, . . . , z_ (15)) used to generate the data according to: Model 2: _y_ = _ιn_ + _σε,_

> 9 Indeed, if we take the null model for _Mj_ we get _kj_ = 0 and _Rj_ 2<sup>=0andwegetexactlytheprioron</sup><sup>_g_</sup> back, as we should (since the null model does not involve _g_ ).

> 10 Fortran code for the fixed _g_ case as described in Ley and Steel (2009) is available online in the _Journal of Applied Econometrics_ archive corresponding to the latter paper. Code in R (which accommodates hyper- _g_ priors) is available from http://bms.zeugner.eu/.

18
# **5 Robustness: IV estimates of trade deflection and trade depression**

## **5.1 Panel data regression model**

Given that our estimates of equations (2) and (3) could be sensitive to the choice of countries _d_ (India), _i_ (Japan), and _k_ (Korea), we present a final check on the robustness of our results by examining an alternative model that relies more on cross-sectional variation across 6-digit products and countries to obtain identification. This has some similarities to the approach taken in Bown and Crowley (2007).<sup>22</sup> In this alternative approach, we start with the time difference of (1): where we assume that country _i_ ’s trade policy toward China is constant over the time period under investigation. Then, we use 6-digit product fixed effects and lagged export growth to proxy for time-varying cost or productivity shocks at the product level. Our estimating equation is then: where in estimating we apply the instumental variables techniques of Anderson and Hsiao (1981, 1982) because the autocorrelation of the dependent variable implies that least squares estimation

> 22Bown and Crowley (2007) estimate trade deflection and trade depression associated with U.S. antidumping against Japanese exports in a panel data model in which Japanese industry-level covariates proxy for technology and cost shocks. The analysis above, in contrast, uses the difference-in-difference equation (2) that does not require productlevel controls to estimate trade deflection. This is useful because comparably disaggregated data to proxy for technology and costs shocks is not available for China during the sample. As a robustness check to the panel data model in Bown and Crowley (2007), they also estimated the Japanese sample on a similar model with product-level fixed effects and obtained consistent results, thus motivating our robustness check here. Nevertheless, a weakness with the IV approach is the lack of valid instruments. In the approach we adopt below, the second lag of of the log level of imports has strong predictive power for the lagged growth rate of imports. Nevertheless, a potential argument against using this instrument is that it requires the exclusion restriction that the second lag of the log level of imports has no direct effect on the current growth rate of imports

19
used in the U.S. (Danielson, 2007). The observer evaluated the teachers’ effective use of 21 different activities over the course of a full 45-minute lesson. Based on these observations and a detailed rubric, the observer provided a subjective score, on a scale from zero to three, of four components of the lesson: communication of lesson objectives, delivery of material, use of assessment, and student engagement. The teacher’s incentivized score, as well the measure of pedagogy used in our analysis, is defined as the average of these ratings across the four domains.

## **3.5 Balance**

We use the baseline data described in this section to check whether the second-tier randomization produced an appropriately ‘balanced’ experienced treatment assignment. Table 2 confirms that across a wide range of school, teacher, and student characteristics there are no statistically significant differences in means between the experienced P4P and FW treatment arms.<sup>25</sup>

# **4 Results**

Our two-tiered experiment allows us to estimate impacts of pay-for-performance on the type of individuals applying to, and being placed in, primary teaching posts (the compositional margin), and on the activities undertaken by these new recruits (the effort margin). We report these results in Sections 4.1 and 4.2 respectively. Of course, the long-run effects of pay-for-performance will depend not only on selectionin, but also selection- _out_ , as well as the dynamics of the behavioral response on the part of teachers who stay. We address dynamic issues in Section 4.3, and postpone a substantive discussion of results until Section 5. All statistical tests are conducted via randomization inference with 2,000 permutations of the experienced treatment.

## **4.1 Compositional margin of pay-for-performance**

We study three types of compositional effect of pay-for-performance. These are impacts on: the quality of applicants; the observable skill and motivation of placed recruits on arrival; and the student learning induced by these placed recruits during their first and second year on the job.

> 25Since the teacher inputs described in Section 3.4 were collected _after_ the second-tier randomization, they are not included in Table 2. See instead Appendix Table A.2.

19
# Table 4: Board composition in the largest 100 **fims** in 1986

|Member's institutions|# of<br>Boards|of<br>Board<br>members|
|---|---|---|
|Private banks|59|114|
|Other banks|37|51|
|Employees/Managers from industry|81|368|
|Politicians/Government employees|24|69|
|Other representatives of Capital|61|147|
|Members of trade unions|77|197|
|Thereof: Unions associated with the DGB|76|181|
|Other labor representatives|83|520|
|Total|84|1466|

# Total

# source: Herrhausen (22,p. 108]

One may even consider that bankers hold the presidency of the Board more often, because they represent a third group of stakeholders having 'capital-like" incentives due to their equity holdings and market making activities in common stock, as well as "labor-like" like incentive owing to the fact that debt as well as labor represent a fixed claim on the firm, in which case they would be an ideal mediator in a German Board. This, however, must remain a speculative issue as labor representatives are the first to attribute banks to the "capitalist" side (see Greiffenberg [16]), and one cannot dismiss the possibility that also the bank as a lender can theoretically make decisions to expropriate labor.

Yet, one banker interviewed in the course of this research stated that he usually defended labor interests during Board negotiations, for example by sponsoring increases in the company's pension fund for its employees. This is definitely not in the immediate interest of the bank, and only supports the role of the Board president to act as a mediator between capital and labor interests. These issues lead directly to the heart of the discussion on the power of the German banks in German industry, namely the possibility of conflicts of interest that could induce the banker to exploit his position of influence in contradiction to the intent of the position.

# 3.4. ConfUlcts of interest

In this section we have to address the question more closely, in how far the controversial issues in German banking, namely equity ownership, proxy votes, and Board memberships, interfere with the "proper" incentives and interests of a universal bank, and how a bank with significant influence in any of the three areas can use its power to the detriment of any other group, and there are obviously many: banks are dealing with managements, union representatives, labor leaders from within the respective firms, other 19
# **Little Nomads: Economic and Social Impacts of Migration on Children**<sup>*</sup>

Magdalena Cortina<sup>†</sup> Juan Miguel Jimenez<sup>‡</sup> Sandra V. Rozo<sup>§</sup> Originally published in the Policy Research Working Paper Series on _January 2024._ This version is updated on _February 2024._ To obtain the originally published version, please email prwp@worldbank.org.

**JEL Classification:** F22, O15, R23 **Keywords:** Children, Migration.

> *This research project received financial support from the Research Support Budget of the Development Vicepresidency of the World Bank. We are grateful to Catalina Amuedo-Dorantes for suggestions.

> †Robert S. McNamara Fellow, World Bank. E-mail: mcortinatoro@worldbank.org

> ‡Vancouver School of Economics. E-mail: jmjimen@student.ubc.ca

> §World Bank, Development Research Group. Corresponding author, E-mail: sandrarozo@worldbank.org
the change in low work intensity between 2005 and 2008 is not predicted well by our simulation model, again because the changes in Hungary between 2005 and 2008 are driven more by changes in the model parameters than by changes in the characteristics.

The 2008 severe material deprivation index, on the other hand, is not very well predicted by the simulation model in either old or new EU Member States. Only in the Czech Republic and Estonia are the actual and predicted values within 2 percentage points of each other, while in all other countries the difference is wider. Other studies have also found severe material deprivation difficult to explain using observed variables. One hypothesis for these findings is the considerable subjective element in the questions about whether a household could afford some of the items in the material deprivation measure (for example, the cost of a week’s holiday away from home is highly variable).

The simulation model predicts 2008 anchored poverty rates reasonably well in new EU Member States, but not as well in old EU Member States. This pattern is explained by the fact that a change in anchored poverty is more strongly caused by changes in individual and household characteristics in new EU Member States than in old EU Member States. In old EU Member States, the structural relationship between individual and household characteristics and anchored poverty is unstable between 2005 and 2008.

**Table 5: Simulated 2008 poverty and social exclusion indicators using 2005 data**

|Country|Simulated<br>at-risk-of-<br>poverty<br>in 2008<br>using<br>2005 data|Actual<br>2008<br>at-risk-<br>of-<br>poverty|Simulated<br>low work<br>intensity<br>in 2008<br>using<br>2005 data|Actual<br>2008<br>low<br>work<br>intensity|Simulated<br>material<br>deprivation<br>in 2008<br>using 2005<br>data|Actual<br>2008<br>severe<br>material<br>deprivation|Simulated<br>anchored<br>poverty<br>in 2008<br>using<br>2005 data|Actual<br>2008<br>anchored<br>poverty|
|---|---|---|---|---|---|---|---|---|
|Czech Rep*|11.2|9.3|7.0|6.9|4.8|6.2|7.2|3.8|
|Estonia|19.3|19.5|7.5|5.5|3.6|4.2|7.8|5.2|
|Hungary*|12.1|12.4|7.8|15.9|10.3|18.2|8.8|9.4|
|Latvia|18.9|25.8|7.7|5.3|9.1|15.1|9.2|8.9|
|Lithuania*|16.3|20.1|7.8|6.2|5.5|12.0|9.6|5.1|
|Poland|14.9|16.9|13.0|10.0|10.3|17.2|5.74|4.3|
|Slovakia*|9.4|11.6|5.8|5.0|4.9|11.0|4.9|3.0|
|Greece|17.7|20.4|6.5|7.9|11.9|8.8|9.2|18.5|
|Ireland|19.5|15.3|12.2|13.4|20.1|5.3|13.2|10.2|
|Italy|17.5|18.7|9.6|9.7|13.8|8.0|10.1|18.0|
|Portugal|15.5|18.8|6.1|6.0|4.0|9.0|8.2|17.6|
|Spain|16.5|19.7|5.7|6.2|8.2|2.8|8.6|14.2|
|Sweden|18.3|12.2|5.4|4.9|8.9|1.2|5.7|8.5|

Note: ‘*’ indicates countries where gross income is used, and for the remaining countries, net Income is used. _Source:_ World Bank staff calculations using EU-SILC data.
Figure 1 displays the numbers of net inter-provincial migrants (defined as the balance between in-migrants and out-migrants) by eight regions/two major cities and by the two census periods. During the period 1994-1999, inter-provincial migration was driven by rural-urban flows mainly to two large cities and the Southeast region. The Central Highlands also attracted mainly agricultural migrants, partly stimulated by the coffee boom in the 1990s (Ha and Shively, 2008). During the period 2004-2009, the number of net migrants more than doubled from the prior census period in two big cities and in the Southeast, while the gain in the Central Highlands was much smaller relative to the previous period. The Central Coasts and Mekong River Delta experienced large net population losses to migration during the same period.

Figure 2 demonstrates the number of inter-provincial migrants by gender and by three census periods. Whereas male migrants outnumbered females during the census period 1984-1989, this was reversed in recent years as more females moved inter-provincially during the last census period. In particular, several studies point out a growing trend of internal migration among young women moving to the regions that are the main recipients of FDI (e.g., Dang _et al_ ., 2003; GSO, 2005, 2011).

Although Vietnam received substantial FDI inflow in the 1990s, the contribution of FDI to employment was limited (Jenkins, 2006). Some economists attribute the slow growth of foreign jobs in the 1990s to the capital-intensive and domestic-market-oriented nature of production under protectionist trade and investment regimes (Athukorala and Tien, 2012). In contrast, partly stimulated by the BTA (2001) and Vietnam’s accession to the World Trade Organization (2007), the re-orientation of foreign firm activities toward export-oriented production in the 2000s appears to have had positive impacts on foreign employment growth.
|________________<br>Country<br>________________|____________<br>Model<br>____________|_____<br> <br>_____|____________<br>All<br>firms<br>____________|___________________<br>20 – 49<br>employees<br>___________________|______________________<br>50 – 249<br>employees<br>______________________|_____________________<br>250 – 499<br>employees<br>_____________________|_____________________<br>500 and more<br>employees<br>_____________________|
|---|---|---|---|---|---|---|---|
|UK|Pooled|ß1|0.025|-0.315|0.115|-0.101|0.182|
|1995 - 2004||p<br>ß2|[0.37]<br>0.034|0.00]<br>0.430|[0.002]<br>-0.065|[0.16]<br>0.095|[0.01]<br>-0.110|
|||p|[0.33]|[0.00]|[0.17]|[0.28]|[0.18]|
||Fixed|ß1|-0.015|0.011|0.054|-0.124|-0.127|
||effects|p|[0.71]|[0.91]|[0.30]|[0.26]|[0.19]|
|||ß2|0.090|0.230|0.015|0.118|0.151|
|||p|[0.05]|[0.04]|[0.81]|[0.31]|[0.20]|
||N||9,450|2,060|5,211|1,073|1,106|
||NxT||52,593|9,200|29,367|7,114|6,912|

________________________________________________________________________________________________________________________________ Note: Results are for firms from ISIC industries 15 – 36 with at least 20 employees at the median over the years covered in the panel. The firms with the bottom / top one percent of labour productivity (defined as total sales per employee) in a year are excluded from all computations. Firms are classified into size classes according to the median of the number of employees over the years covered. ß1 and ß2 are the estimated regression coefficients from an OLS-regression of log (labour productivity) on the share of exports in total sales and its squared value, respectively, controlling for the log of the number of employees and its squared value, the log of wages and salaries per employee, and a full set of interaction terms of 4-digit industry-dummies and year dummies; the fixed effects model adds firm fixed effects. p is the prob-value, N is the number of firms, NxT is the number of observations.

45
For example, we do not know what are the tasks for which computers or Internet are used or how are these technologies being used in production. To address this issue, the next section explores and measures the purpose for which technologies adopted by firms are used.

# **3 New Measures of Technology Adoption and Use: Linking Technologies to Business Functions**

To identify the purpose for which a technology is used by the firm, we link the information on the use of technology with specific business functions. We follow the methodology proposed by Cirera, Comin, and Cruz (2020) and split business functions in two groups: i) General Business Functions, which are common tasks that apply to all firms (e.g. business administration, sales, payment, quality control); and ii) Sector Specific Business Functions, which varies across each sector and is usually more related to core production functions.

The General Business Support Functions (GBFs) are commonly available across all firms, irrespective of the industries they are in. Therefore, they provide good comparison across firms, sectors and countries. The FAT survey identifies the purpose for which a given technology is being applied. Figure 4 describes the key GBFs covered by the survey and the technologies associated with them: 1) Business Administration; 2) Production Planning; 3) Sourcing and Procurement; 4) Marketing and Customer Information; 5) Sales; 6) Methods of Payment; and 7) Quality Control. The technologies associated with most business functions follow a ladder of sophistication that goes from the most basic (e.g. handwritten process for production planning) to the most sophisticated level (e.g. Enterprise Resource Planning (ERP) systems for production planning).

The Sector Specific Business Functions (SSBFs) are tasks that are associated with the core production or service provision activity and vary across sectors. The FAT survey in Senegal has specific sets of questionnaires for 8 sectors: i) Agriculture (Crops, Fruits, and Vegetables); ii) Agriculture (livestock); iii) Food Processing; iv) Wearing apparel; v) Retail and Wholesale; vi) Land Transportation; vii) Finance; and viii) Health. Among those, the survey was stratified for and provides a representative sample for firms in agriculture, food processing, wearing apparel, and retail.

The survey asks information on more than 300 technologies associated to almost 50 business functions. To analyze the level of technology adoption and use in a more systematic way, we convert the information for each business function into a technology index. The index, described by Cirera et al. (2020), varies between 1 and 5, where 1 stands for the most basic level of technology been used and 5 reflects the most sophisticated level been used.

9