rafmacalaba/datause-encoder-data huggingface dataset:
14 Mexico MFA

**Table 3a. Regression Results for Equation 1 -** Linear **Specirlcation.**

|Independent<br>Variables|All Groups|Bound Groups<br>(2,5,6)|Unbound Groups<br>(1,3,4)|
|---|---|---|---|
|Intercept|**_2.4515A _**<br>**(0.5964)**|0.6050**b**<br>**(0.3146)**|4.6597**a**<br>**(0.9263)**|
|Pjtu'<br>.______________|0.4094**9**<br> <br>**(0.0257)**|0.4676**A**<br>(0.0310)|0.4474**A**<br>**(0.0478)**|
|**ujt**<br>_______________________|-0.8343<br>**(0.9247)**|**-0.2752**<br>**(0.4502)**|-2.483**c**<br>**(1.4850)**|
|**Vjt**|-2.29 X**10-8**<br>(2 X**108)**|<br>-6.79 X**10-9**<br>**(I X lo-8)**|6.21 X**10i7**<br>**(7X 10-7)**|
|**Hijt**|**-0.0012**<br>(0.0022)|0.0029<br>(0.0011)|-0.0153**b**<br>**(0.0075)**|
|Adj. R<sup>2</sup>|0.872|0.942|0.775|
|# observations|60|30|30|
|t Test,=_1|-22.89 reject '|-17.16 reject**A**|-11.5 reject**a**|
|F Test, ,=1 and|338.71 reject '|292.09 reject&|268.81 reject '|

Standard errors in parentheses. **A** Significant **at 1** percent.

**b** Significant at **5** percent. **c** Significant at 10 percent.

Table 3a presents the results of running regression (1) on all groups, the<sup>quota bound groups</sup> and non-bound groups.<sup>12</sup> The first significant result is that for all three<sup>regressions the hypothesis of</sup> perfect competition (3=1 and a=y=5=4=0) and the<sup>hypothesis that the coefficient on the U.S. price ,B</sup> is equal to one, are both rejected. While this does not confirm<sup>the existence of rent sharing, it is</sup> consistent with the rejection of the hypothesis of perfect competition in the<sup>market for Mexican export</sup> to the U.S. in these groups of apparel. The intercept<sup>and the coefficient on the U.S. price are</sup> significant in all three regressions. In this context, the intercept<sup>can be interpreted as refiecting the</sup> effects of any fixed difference between the two prices common<sup>to all groups of apparel (the "fixed</sup> component"), and the coefficient on the U.S. price reflects<sup>the change in the Mexican price for every</sup> 12Tbe definition of the groups is based on the data in Table 2.
the depth of deprivations, not all of them ensure that the poorest among the poor population receive precedence over the less poor population during a poverty alleviation process. For that purpose, we introduce an intuitive concept of giving _precedence to poorer people_ in the _ordinal_ framework, in tune with the _prioritarian view_ which holds that ‘benefiting people matters more the worse off these people are’ (Parfit, 1997, p. 213).<sup>8</sup> Furthermore, our notion of precedence is presented in a general framework where the _degree of precedence_ to poorer people can vary between a minimum and a maximum.<sup>9</sup> Let us introduce the concept using the example in Figure 1. Suppose, there are ten ordered deprivation categories, where _c_ 1 is the most deprived category and _c_ 10 is the least deprived category. Denoted by black circles, categories _c_ 1 _, . . . , c_ 7 reflect poverty; whereas the other three categories, denoted by gray circles, do not. Thus, _c_ 7 is the poverty threshold category, which is highlighted by a large black circle in each distribution. The original distribution is **p** , where each individual experiences one of the ten categories.

Suppose the policy maker has the following two competing options: either (a) obtain distribution **p**<sup>_′_</sup> from **p** by assisting a fraction _ε ∈_ (0 _,_ 1) of poor people to move from category _c_ 1 to category _c_ 2 ; or (b) obtain distribution **q** from **p** by assisting a fraction _ε_ of poor people to move from category _c_ 2 to category _c_ 3. Which option should lead to a larger reduction in poverty? One way of giving precedence to poorer people is to require that the move from

> 8For an application of the prioritarian concept to the multidimensional context, see Bosmans et al. (2017). For further discussion, comparing the prioritarian view with the egalitarian view see Fleurbaey (2015, p. 208).

> 9The concept is analogous to the degree of poverty or inequality aversion in the monetary poverty measurement literature (Clark et al., 1981; Chakravarty, 1983; Foster et al., 1984), but not technically identical.

14
# **Appendix Table A7**

# **What Factors Determine the Share of Village Residents in the Migrant Network?**

First-Stage Regression Using the Sample of Individuals Completing Middle School, 1986-2003

||Depende|nt Variable: S<br>Wo|hare of Regis<br>rking as Migr|tered Village<br>ants|Residents|
|---|---|---|---|---|---|
|Model|1|2|3|4|5|
|Years Since IDs issued|-0.017*<br>(0.009)|-0.021**<br>(0.009)|-0.021**<br>(0.009)|-0.021**<br>(0.009)|-0.021**<br>(0.009)|
|Years Since IDs Issued|0.007***|0.008***|0.008***|0.008***|0.008***|
|Squared|(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|
|Years Since IDs Issued|-0.001**|-0.001***|-0.001***|-0.001***|-0.001***|
|Cubed|(0.0002)|(0.0002)|(0.0002)|(0.0002)|(0.0002)|
|(Years Since IDs Issued)<sup>4</sup>/10|0.002**|0.002***|0.002***|0.002***|0.002***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Ln(Village Average||0.020|0.020|0.019|0.019|
|Income Per Capita)||(0.015)|(0.015)|(0.015)|(0.015)|
|Total Land in Village (Mu)||0.0004|0.0004|0.0004|0.0004|
|||0.0003|0.0003|0.0003|0.0003|
|Cropped Land Gini||-0.013|-0.014|-0.011|-0.011|
|Coefficient||(0.087)|(0.086)|(0.087)|(0.086)|
|Size of Village Workforce(/10)||-0.001***<br>(0.0002)|-0.001***<br>(0.0002)|-0.001***<br>(0.0002)|-0.001***<br>(0.0002)|
|Cultivable Share of||-0.029|-0.030|-0.029|-0.029|
|Village Land||(0.038)|(0.038)|(0.038)|(0.038)|
|Gender (1=male,|||-0.001|-0.001|-0.001|
|0=female)|||(0.002)|(0.002)|(0.002)|
|First Born? (1=yes, 0=no)|||0.002|0.002|0.002|
||||(0.003)|(0.003)|(0.003)|
|First Born in Household is|||-0.003|-0.003|-0.004|
|Male? (1=yes, 0=no)|||(0.002)|(0.002)|(0.003)|
|Father’s Years of||||-0.001**|-0.001**|
|Schooling||||(0.0004)|(0.0004)|
|Mother’s Years of||||0.0002|0.0002|
|Schooling||||(0.0005)|(0.0005)|
|Number of Potential|||||-0.001|
|Migrants, Male|||||(0.002)|
|Number of Potential|||||0.001|
|Migrants, Female|||||(0.002)|
|Number of Observations|3160|3157|3157|3157|3157|

Notes: Columns 1 through 5 are the first stage of IV regressions shown in models 1 to 5 of Table 11. .

71
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Challenges**

Senegal’s power supply is insufficient to meet the existing and rapidly growing demand. Between 2005 and 2009, generation capacity grew from 365 MW to 510 MW,<sup>10</sup> but demand kept growing at a rate of 25–30 MW per year. An equilibrium between supply and demand has never been reached. As an example, in August–September 2009, an interruption of fuel supply linked to SENELEC’s liquidity issues put about 10 units out of operation and caused major outages over several days. Frequent disruptions highlighted the structural problems and capacity shortages.

The country’s installed generation capacity was only about 25 MW per million people (table 12), making it an unreliable system—a problem widespread throughout the African countries. The number of outages in a typical month in Senegal is almost 12, which is aligned with the averages found in other SubSaharan countries. By comparison, a typical power security standard in the United States is 1 day in 10 years. The percentage of firms relying on their own generator in Senegal is at 55.4 percent, above the Sub-Saharan African average of 43.1 percent. Not surprisingly, one-fourth of the electricity consumed in Senegal comes from private generation (table 13).

**Table 13. Performance of the electricity sector in West African countries**

||Burkina<br>Faso<br>(2009)|Cape<br>Verde<br>(2006)|Côte<br>d’Ivoire<br>(2009)|Ghana<br>(2007)|Mali<br>(2007)<br>|Mauritania<br>(2006)|Niger<br>(2006)|Nigeria<br>(2007)|Senegal<br>(2007)|Simple<br>average|
|---|---|---|---|---|---|---|---|---|---|---|
|Number of power outages in a typical month|10.8|12.5|4.5|9.7|4.4|3.7|20.7|26.8|<br>**11.8**|<br>14.4|
|Average duration of outages (hours)|3.3|5.3|4.6|12.6|3.9|2.9|0.5|8.2|<br>**6.2**|<br>6.6|
|Loss due to outages (% of sales)|5.8|8.9|5|6|1.8|1.6|2.5|8.9|<br>**5**|<br>6.4|
|% of firms owning/sharing generator|11.6|39.8|6.5|26.6|23.8|28.6|24.8|85.7|<br>**55.4**|<br>43.1|
|Electricity from own generator (%)|10.9|4.6|15.1|29.5|16|9.2|10.5|60.9|<br>**24.7**|<br>30.6|
|_Source_: World Bank 2011b.<br>_Note_: Year of the survey in brackets.|||||||||||

SENELEC, Senegal’s power utility, faces major financial problems. Power tariffs are admittedly relatively high when compared to other countries in the region, reflecting the fact that the cost of generation is high due to the percentage of thermal power (371 MW out of the 489 MW from SENELEC’s plants are thermal, and the rest 66 MW are hydropower) (figure 9a). While tariffs somehow allow for recovering operational costs, they still are below the long-run marginal cost of the power sector, implying that a capital subsidy of close to 25 percent of total costs is tacitly in place (figure 15).

> 10 The installed capacity is 549.1 MW but, given the age of some of the equipment, the real generation capacity accounted for only 510 MW as of 2009. Sixty-five percent of this corresponds to SENELEC’s production and the remaining 35 percent to private production.

28
38

# Notes:

- a. Contributions to the old system will cease on June 30, 1997. Transition workers can choose at retirement the higher of the benefits available under the old PAYG scheme or the new defined-contribution plan.

- b. Except those who meet the requirements for retirement under rules of the old system by the date AFPs start operating.

- c. Only workers who have contributed for at least 48 months in the last 10 years and have at least 6 months prior to entering the new system.

- d. "Compensatory pension" is paid upon retirement, not as a bond. The value is based on years of contribution and last ten years' earnings.

- e. Workers with fewer than 150 weeks of contributions are not eligible for a recognition bond.

- f. Disclosure of expected current and future fiscal costs would be made on an annual basis.

- g. The value is based on years of contribution and the last monthly salary earned.

- h. MPG introduced with second round of reforms in 1995, but regulations have not been issued.

- **i.** This is paid by the employer.

- j. Including contributions to the individual AFORE account, INFONAVIT and the value of Social Quota contribution by the government. The Social Quota, initially New Peso I per day, is indexed to the CPI and estimated at I and 5.5 percent depending on worker's income, with an average of 2.2% of wages for all workers.

- k. The rate shown is for both private and public sector and for 1996 and the following year. Contribution rate increased gradually during 1994-96.

1. Mandatory contributions based on employee's salary include: Employee 2.5%, employer 5%, and the State 1.5%; plus variable contributions to complementary funds

**_Source:_** Cerda, L and Grandolini, G., "Mexico: The 1997 Pension Reform," 1996; World Bank internal document.
Placzek, 2010; Farber, 2017; Goldschmidt and Schmieder, 2017; Schmieder, von Wachter and Heining, 2022). This literature typically compares workers who lost their jobs to workers who did not lose their jobs and uses mass layoff events for identification. Our work uses another source of exogenous variation in job loss, namely firm takeovers. Firm takeovers substantially increase the probability of job loss and job loss has longer-term negative consequences for workers.

Our findings have important implications for assessing welfare effects of firm consolidations. First, they suggest that takeovers can have negative, long-lasting impacts on target workers’ labor market outcomes. Second, we show evidence that labor restructuring is an important determinant of job loss: workers who are over-placed or workers in acquisitions where the acquirer is more likely to have workers already serving a given role experience higher rates of separation. These findings suggest that even consolidations that are typically viewed favorably from a consumer welfare perspective, those that increase efficiency without likely increasing product market concentration, can still have large, long-term welfare impacts for target workers through labor restructuring.

The remainder of this paper proceeds as follows. In Section 2, we describe the data and our empirical approach. In Section 3, we show that takeovers have long-lasting, negative impacts on incumbent worker outcomes. In Section 4, we demonstrate that labor restructuring is the primary mechanism driving these results through a series of heterogeneity analyses. Finally, in Section 5, we conclude.

# **2 Data and Methods**

## **2.1 Data Sources**

We study the impact of takeovers on labor demand and the labor market outcomes of workers of acquirer and acquired firms using several administrative datasets from Statistics Netherlands (CBS). We begin with an administrative dataset which identifies various “firm events”, such as when firms are established, closed down, or taken over. A takeover is defined as an event where one business takes over one or more other businesses in the sense that the production factors of the acquirer continue in the same manner. The business which is taken over will cease to exist in its previous form. In this dataset, we can observe the date of the takeover and the different firms involved. A unique advantage of this dataset is that, in contrast to previous papers which relied on worker flows 4
definitions stunting “reflects a process of failure to reach linear growth potential as a result of suboptimal health and/or nutritional conditions.” Stunting is an indicator of persistent, longer term chronic undernutrition and has long-term negative effects on the child’s development (Brown, 2017). We also calculate weight-for-height (wasting). A z-score of less than -2 for wasting suggests recent rapid and severe weight loss which is often associated with acute starvation and/or severe disease.

We use market prices for three crops – teff, wheat and maize, collected through the community questionnaire in the LSMS.<sup>10</sup> These are key crops in Ethiopia and account for the largest share of total cereal area cultivated (Taffesse, Dorosh, and Asrat, 2011). The data are collected at item level from two different vendors in each enumeration area across Ethiopia. An average price is computed from the two different vendors, which forms the price in that enumeration area. Table 2 presents summary statistics of the three crop prices in wave 1, wave 2 and wave 3.<sup>11</sup>

**Table 2: Summary statistics of market prices in ETB/kg**

||**Teff**<br>Observations|Mean|Std Dev|Min|Max|
|---|---|---|---|---|---|
|2011/12|258|9.64|2.3|3.5|15.67|
|2013/14|347|13.1|2.68|4|18|
|2015/16|365|16.42|3.52|4|22|
||**Wheat**|||||
||Observations|Mean|Std Dev|Min|Max|
|2011/12|248|8.2|2.58|4.46|15.67|
|2013/14|339|8.96|2.66|4.55|18|
|2015/16|365|10.48|3.43|4|22|
||**Maize**|||||
||Observations|Mean|Std Dev|Min|Max|
|2011/12|268|5.33|2.11|3.5|15.69|
|2013/14|381|6.39|2.4|4|18|
|2015/16|389|6.4|3.18|4|22|

# _Source: LSMS Survey dataset_

The mean price for all three crops increased considerably between the three waves. For example, the mean price for teff increased from 9.64/kg in wave 1 to 16.42/kg in wave 3; the

> 10 We explored using monthly data collected by the Ethiopian Central Statistical Agency on crop prices at the district level, as used by Woldemichael et al _.,_ (2017). However, in our case we faced the challenge of many missing observations and that results were sensitive to the approach adopted to dealing with these. 11 Table A.2 in the appendix presents the average price for each region.

7
Table 6. Estimating the impact of PEC on <u>School performance</u>

||PEC<br>Dummy|Treatment<br>Dummy|
|---|---|---|
|PEC|2.81|-2.00|
||(2.06)|(7.33)|
|2006 PEC|-2.39|-0.25|
||(10.25)|(4.32)|
|2007 PEC|4.56|0.76|
||(9.54)|(4.67)|
|2008 PEC|-5.27|-0.63|
||(10.73)|(4.95)|
|2009 PEC|-11.33|-2.30|
||(10.29)|(5.49)|
|2010 PEC|15.15|-3.95|
||(8.43)*|(5.71)|
|2011 PEC|-16.39|-3.88|
||(15.06)|(6.37)|
|2012 PEC|-3.12|-10.60|
||(8.61)|(8.00)|
|Constant|445.33***|447.36***|
||(19.12)|(17.01)|
|Controls|Yes|Yes|
|R2|0.06|0.06|
|Clusters|90|92|
|Observations|110,11|111,618|

School-Level clustered standard errors in parentheses. *p<0.10, **p<0.05, ***p<0.01 Previous insignificant results overall could hide important heterogeneous effects of the intervention. A SBM program may have a differentiated effect within the schools in the evaluation. Table 7 shows specifications using the intensity of the treatment in the experiment sample in 2006. The intensity of the treatment among very selected schools in Colima shows positive effects on 20
because it illustrates the most plausible mechanism connecting the currency devaluation to individuals’ evaluations of well-being.

As dependent variable, we use respondents’ answers to the following question: “In general, how would you describe: Your own present living conditions?” Answers are given on a scale consisting of the categories “very bad,” “fairly bad,” “neither,” “fairly good,” and “very good,” where high values denote good living conditions. While the literature often uses questions concerning “life satisfaction” (Deaton 2008, 2012; Bjørnskov et al. 2010; Kahneman and Deaton 2010; Asadullah and Chaudhury 2012), the question we use asks respondents to evaluate their present living conditions on a scale from “very bad” to “very good,” which is clearly a constitutive feature of subjective well-being.<sup>6</sup> We therefore use this question to measure subjective well-being.<sup>7</sup> 6 We note that although the wording is not identical to most surveys asking about the satisfaction with life as a whole, the two questions tend to produce quite similar results. Using the 2011 wave of the World Values Survey in Ghana, we note the similarity between the regular life satisfaction question and a question specifically on satisfaction with one’s financial situation. Less than 5 percent of respondents who declare themselves satisfied with their financial situation (rating it 8–10 on a 1–10 scale) declare themselves unsatisfied with their life as a whole.

> 7 The _Afrobarometer_ also contains a related question, asking respondents to evaluate their living conditions relative to other people. Replications using this variable—evaluations of relative living conditions—does not change our findings substantially or statistically. Detailed results are available upon request. We do not think the two variables are sufficiently distinct to treat them as alternative measures, partly because questions asking people to rate their situation relative to others may pick up absolute and not relative differences (Karadja et al. 2014), and partly because the two living conditions questions are asked immediately after each other, which may make responses quite similar.

13
The optimal contract offers the L-type seller the same compensation and payment structure (i.e., full payment contingent on the conservation of forest) that she would receive if the buyer could distinguish types. But the H-type seller must receive a higher total payment by including the information rents corresponding to the first-best allocation of land in forest for an L-type, ∆<sup>_∗_</sup> _L_<sup>.Furthermore,theoptimalcontractprescribesacontractiblepay-</sup> ment equivalent to the present value of the information rents while the contingent payment is smaller than when there is symmetric information. Nevertheless, if the expected surplus is sufficiently high, the first-best level of conservation is implemented by self-enforcing contracts.

If the discount factor is small, the future gains from the forest-conservation relationship become too small to support any level of forest conservation. In this case, no schedule may satisfy the constraints, and forest conservation is not possible under a relational contract.

However, even if the expected gains from the relationships are small, relational contracts may still implement conservation, depending on how restrictive the self-enforcement constraint is. In this case, inequality (19) binds with _θ_ = _θH_ for _lH_ = _l_<sup>_∗_</sup> _H_<sup>.Iftheself-</sup> enforcement constraint is very restrictive, it is better to reduce the quantity of land in forest for both types below the first-best level and request some levels of conservation from both types instead of having only the H-type providing the first-best level and the L-type not participating. Requesting additional land in forest from the L-type implies an increase in the slope of the H-type payment schedule (due to information rents). Because the total payment is limited by the expected gains from the relationship, giving additional incentives for the H-type seller means decreasing incentives for the L-type seller. This is sub-optimal because a marginal reduction in forest conserved by the H-type reduces the surplus generated but allows for more area in forest from the L-type. As the L-type conservation is substantially below the first-best, _l_<sup>_∗_</sup> _L_<sup>,increasing</sup><sup>_lL_raisestheoverallsurplus.Asaresult,therequested</sup> quantity of forest for each type is given by _lLR_ and _lHR_ , for which the marginal gains of 23
Figure 7: Synchronization of business and financial cycles 12

- (A) Cyclical component of GDP (B) Growth during U.S. business cycles, 1960-2015

<!-- Start of picture text --> Percent US Other AEs EMDEs BRICS Percent United States Other AEs EMDEs<br>4<br>4<br>3 3<br>2<br>2<br>1<br>0 1<br>-1<br>0<br>-2<br>-3 -1<br>-4<br>-2<br>US expansions US recessions<br>(C) Correlations with U.S. business (D) Concordance with U.S. business<br>cycles and financial cycles<br>Correlation 2001Q1-2007Q4 Percent of total<br>0.8 2007Q4-latest 100<br>80<br>0.6<br>60<br>0.4 40<br>20<br>0.2<br>0<br>Business Credit House Equity<br>0.0 cycles cycles price price<br>AEs EMDEs BRICS cycles cycles<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

- (E) Activity around the U.S. recession (F) Activity around the U.S. recession of 2001 of 2009

<!-- Start of picture text --> Percent deviation from trend GDP Percent deviation from trend GDP<br>3 3<br>AEs EMDEs AEs EMDEs<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br>-4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4<br>Quarters Quarters<br><!-- End of picture text --> Sources: Haver Analytics, World Bank, Kose and Terrones (2015), International Monetary Fund. A. Cyclical component is defined as deviation from Hodrick-Prescott-filtered trend. B. Annual average per capita growth rates in purchasing power parity during years of expansions and recessions in the United States. Years of expansions and recessions are defined as those with annual positive and negative GDP per capita purchasing power parity growth in the United States, respectively. Other AEs exclude the United States.

C. Contemporaneous correlations between cyclical component of U.S. real GDP and cyclical component of real GDP of advanced economies (AEs) and EMDEs.

D. Average share of years in which business cycles in the United States and all economies were in the same phase. A higher share suggests more synchronization between two countries.

E.F. The graph shows cyclical component of GDP measured as the deviation from trend GDP computed using a Hodrick-Prescott filter on seasonally adjusted quarterly GDP around a trough in U.S. business cycle (t = 0) indicated by the solid bar. Troughs are 2001Q4 and 2009 Q2, defined by the National Bureau of Economic Research. The line refers to median of 35 advanced economies and 51 EMDEs.