rafmacalaba/data-use-mentions-extended huggingface dataset:
distribution within the sample (Taylor et al. 2003). The authors used a retrospective report of the village migration network size from seven years earlier to control for migrant selection at the household, but do not control for village fixed effects, such as proximity to cities (and markets) and other factors affecting the local economy, which will lead to biased estimates of the effect of migration on earnings. In another study examining earnings across the earnings distribution, the authors estimate models in first-differences and identify migration through the one-year lag in size of the village migrant network, finding that households with migrants have per capita incomes 8.5 to 13.1 percent higher than those without migrants (Du et al. 2005). First-differences control for many unobserved factors that might be introducing bias through migrant selection, though even lags in size of the village migrant network may be correlated with past shocks to the local economy. If there are persistent effects of shocks, or mean reversion after a negative shock driving earlier migration, there could yet be bias in these estimates. That said, these estimates are considerably more credible than those found by Taylor et al. (2003).<sup>15</sup>

# **4.2 Consumption and Investment**

Given the earning gains from migration, studying the consumption and investment patterns affected by migration would allow us to comprehend the specific budget constraints households can overcome with migration. de Brauw and Giles (2018) highlight the positive effect of migration on annual changes in consumption in a panel of households and villages between 1988 and 2002: out-migration contributes to a 2% to 2.9% annual increase in consumption per capita, or between 65% and 93% of the annual consumption growth recorded in the RCRE sample villages between 1988 and 2002.<sup>16</sup> During the period with rapid expansion in migration, from 1995 to 2002, a one-percentage point increase in out-migration from the villages is associated with consumption per capita increases of 10
to estimate the causal effects of childcare, analyzing data from a sample of 1,990 women participating in the sixmonth long public works program and their eligible children. The program’s implementation team identified 36 urban public works sites with the potential to host a childcare center. We then randomly selected 18 sites to receive the community-based childcare centers and the remaining 18 sites had no additional childcare provision. With this sample size, the study is powered to detect reasonable effect sizes (Section 3 provides detailed discussion). We conducted a baseline survey in the early stages of childcare implementation and a follow up survey 14 months later. The 2
to alternative estimation-based methods, which signal that the peso was overvalued by approximately 34–46%, as of October 2015. Premia on parallel exchange markets are indicative of the magnitude of overvaluation, and had been widening since 2011 and reached about 34–36 percent in December 2015, before the removal of capital and currency controls. Arbitrage conditions in goods markets also provide measures of currency overvaluation, but need to be interpreted with caution. Indicators relying on the law of one price (LOP), such as the Big Mac and iPad indices, equate goods prices across countries once converted in a common currency, assuming the absence of trade barriers and price controls, and price discrimination. Purchasing power parity (PPP), which implies the real effective exchange rate should be constant (normalized to a year in which it is assumed to be in equilibrium), suggests the peso is undervalued. Artificially low prices in recent years caused these indicators to be misleading. Upon correcting the REER for discrepancies in inflation statistics reported by official vis-à-vis alternative private sector sources, PPP signaled the peso to be overvalued in October 2015 by approximately 46 percent.

The remainder of the paper is structured as follows. Section 2 provides a literature review on the estimation of equilibrium exchange rates, its different approaches and empirical findings. Section 3 explains the methodology and data employed in the BEER analysis. Section 4 presents a range of estimates of the peso’s misalignment using the BEER approach, conducts a series of robustness checks, and contrasts findings to estimation-based approaches using parallel rate premia, LOP, and PPP. Finally, Section 5 summarizes the findings and concludes.

3
We finally investigate two alternative mechanisms that could explain our results: increasing labor market concentration and increasing product market concentration as consequences of takeovers. First, we find that virtually none of the takeovers in our sample has a meaningful impact on labor market concentration (see also Arnold, 2021). Therefore, we conclude that takeover-induced changes in labor market concentration cannot drive our job loss results. Second, we also find that takeovers that are more likely to increase product market concentration (e.g., horizontal takeovers between firms in the same narrowly defined industry) do not induce larger job loss.

Our work contributes to the recent literature on the consequences of mergers and acquisitions for workers (Furlan, 2015; Geurts and Van Biesebroeck, 2019; Arnold, 2021; Todd and Heining, 2020; Prager and Schmitt, 2021). We make several contributions to this literature. First, we are able to study administratively identified takeovers, rather than relying on worker flows between firms as some prior work did, potentially missing the mergers of smaller firms. Second, we have comprehensive administrative data on a variety of worker outcomes, including employment outcomes, earnings, and benefit uptake. These data allow us to comprehensively characterize the consequences of takeovers. Third, using detailed data on wages and skills, we are able to examine potentially important mechanisms, including labor market concentration and labor restructuring behind takeover-induced job loss. In this latter aspect, the most similar papers to ours are Lagaras (2017) which uses micro-data from Brazil to characterize how workers at target firms are affected differently based on their occupational relatedness with the acquirer and Lagaras (2023) which studies long-term earnings of workers separated from target firms. In another related paper, Lee, Mauer and Xu (2018) use aggregate data in the United States to study how post-consolidation job loss is increasing in the human capital relatedness of consolidating firms. Similar to Lagaras (2017) and Lee, Mauer and Xu (2018), we find that job loss is higher among target workers
<br><!-- End of picture text --> Source: Calculations from CDG–World Bank 2022 microdata.

There is larger diversity in responses across countries regarding gender equality. In Vietnam, Mongolia and Lao PDR, more officials disagree with equalitarian statements (Figure 8a). Surveyed EAP officials on average support gender equity more strongly than officials from other regions. Surveyed officials from EAP are also much less likely to believe that girls face barriers to education in their countries (Figure 8b); this aligns with higher levels of female achievement than males in all subjects on the 2019 SEA-PLM, as can be seen in Table 2 (in the Annex). We find no correlation between the share of respondents agreeing to the three first statements and per capita GDP. There is however a small significant correlation between per capita GDP and the share of officials disagreeing with the fact that girls face additional challenges in accessing and completing their education. However, this correlation disappears once one takes into account the region: EAP countries appear as a group very different from the rest of the sample.

> 9 We speculate that officials in Vietnam and Lao PDR  may view inclusion as a burden, especially if student-teacher ratios are high and supports for special education are not available. More knowledge and awareness of options to make inclusion possible may be helpful in these country contexts.

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# **E. Evidence for Ecuador**

To examine the impact of foreign shocks on workers and firms in Ecuador we use the Instituto Ecuatoriano de Seguridad Social (IESS) database for the 2006-2017 period, an administrative longitudinal employer-employee database similar to Brazil’s RAIS covering all formal workers and their employing firms from Ecuador’s social security records.<sup>45</sup> IESS data includes job records for each worker with worker and firm unique identifiers and many demographic characteristics and it is merged with a census of higher education degrees from Ecuador’s higher education registry based on a common unique identifier for individuals to obtain education information. In order to construct the worker panel database needed for our analysis of the impact of foreign shocks on worker outcomes we follow the steps described for Brazil, that is, (i) to define a starting worker panel database with the cohort of individuals employed in the tradables sector in 2006 (and entrants after 2006) keeping their entire work histories and selecting for each the highest paid job in April of each year to identify her/his wage, employer firm, and sector, (ii) to construct an auxiliary database with workers in the 16-65 age range employed at least once by an exporting firm, and (iii) to expand the database such that each worker has observations for all consecutive years between the first and last IESS years and after the worker’s last IESS year until 2017 provided the worker’s age does not surpass 65.<sup>46</sup> While IESS contains information on each firm’s industry and sales, it does not contain information on location but we obtain it from the firm registry described below.

To compute GFC-induced foreign demand shocks for firms, we rely on customs data covering the universe of firm-level export and import transactions from Servicio Nacional de Aduana. We merge the customs data with the worker panel database based on a common firm identifier. We derive a firm panel database from the IESS worker panel database where
outsourcing. For this reason, publicly owned utilities—which outsource at lower levels than their private peers—were chosen as more appropriate comparators for utilities in SSA.

- Automation of network operations (such as smart meters and prepayment meters) and works in general is much less advanced in SSA than Latin America, with many processes still manually carried out, requiring larger complements of staff.

- Data limitations

- Plant-level data are not always available within utility annual reports, the primary source of data used for estimating capex in the main section of this paper. For the purposes of the overstaffing analysis, an industry database was used for plant-level data which may introduce inconsistencies between installed capacity data used in this paper.

- Actual capacity factors of generation plants are unknown. The approach for efficient levels of staff in generation assumes conservatively that all plants are running at full capacity.

- Staff numbers reported by utilities may not be consistent. Reported staff numbers are assumed to be core full-time equivalent staff. Where a breakdown of staff is not available, there could be part-time employees included or non-core utility staff such as secondees. Most utilities also report a breakdown of temporary staff and casual workers. Where these are not reported separately, they may not be included in the reported total, thereby underreporting full-time equivalent staff.

- Customer data reported by utilities may not be accurate, particularly for utilities with poor customer management systems.

# Generation assumptions used in the overstaffing analysis

Efficient levels of staffing for generation were estimated based on the type and size of each generation plant. The assumptions applied are outlined in Table A1.6 together with additional assumptions as follows:

- **Outsourcing maintenance:** Maintenance is outsourced, except in South Africa where it is known that maintenance is not outsourced. The study reduces maintenance staff assumed in Table A1.6 by 70 percent.

- **Small plants:** There are 5 staff members for O&M of any plants smaller than 2 MW.

- **Limit on operational staff*
percent, and 1 percent levels, respectively. Standard errors are reported in parentheses. Note that outliers are removed for all regressions. Outliers are identified based on _dfbetas_ greater than 0.20. The robust regressions are estimated using the _rreg_ command in stata with default settings. For fixed effects regressions, standard errors are adjusted for country clusters. _Economic growth_ is the percent change in GDP per capita between the two years in which the GWP surveys were conducted deflated by the total CPI. _Total inflation_ is the percent change in the food CPI between the month of the GWP survey and the month of the previous GWP survey, where the food CPI in any given month is actually the average food CPI in the previous 12 months. _Relative food inflation_ is the percentage change in the ratio of the food CPI to the nonfood CPI, where the both CPIs in any given month are actually the average CPIs in the previous 12 months. Note that the robust regressor does calculate a pseudo R-squared, but it is generally regarded as inappropriate to report this value. Hence, the R-squared reported in this table is derived from an ordinary least squares regression that excludes outlying values.

25
fed to the ―Cash-Flow Engine‖ as underlying assumptions.

# **4.2 Financial Scenario Generation**

The cost and risk distributions of alternative strategies depend entirely on the scenario paths created for financial variables. As discussed in Section 2, the general approach in creating these scenarios is to use statistical or structural models. In either case, the scenario generators are calibrated by time series analysis, and therefore the process of creating financial scenarios, is heavily influenced by the quality of the historical data. In countries where the value of past data is questionable from a modeling perspective, one can consider adopting flexible approaches that allow using several models interchangeably, as model performance can vary 19
Consequently, very few plants are visited to check that they do comply with labor standards. In the unlikely event of one such visit, some employers add, inspectors can be easily bribed to avoid paying a fine.

Not surprisingly, employers do not feel unduly unconstrained by current labor regulations. A survey of some 200 manufacturing firms conducted in Cameroon in 1994 reports that only 2 percent of the interviewees considered labor regulations were a large or severe problem, compared to 85 percent who considered the problem was slight or non existent. The responses were 3 and 75 percent respectively for wage costs, 4 and 85 percent for rules regarding layoffs, and 5 and 63 percent for the cost of layoffs. Differences in these responses across plant sizes and sectors of activity are relatively minor, except for the cost of layoffs. More generally, labor issues appear as the less acute problem faced by firms, after taxes, corruption, price controls, government rules and difficulties in obtaining licenses, in order of decreasing importance (Gauthier, 1995).

This sanguine assessment of the labor market policies in force could be due to the fact that reforms were deeper in Cameroon than elsewhere in the CFA region. However, a similar picture emerges from studies done for other countries. In Senegal, before any deregulation of the labor market had actually taken place, most managers viewed labor market regulations more as a nuisance than a constraint (Terrell and Svejnar, 1989). In C6te d'Ivoire, the most stringent labor regulations were reportedly by-passed by firms using alternatives such as sub-contracting and apprenticeship (World Bank, 1993).

The fact that labor market regulations are not among the main concerns of employers does not imply that these regulations are irrelevant though. The Cameroon survey asks managers whether they would hire more workers if selected labor market regulations were removed. Some 6.5 percent of the interviewees declared they would if minimum wages were abolished, while 93.5 say they would not. In the case of firing restrictions, the answers were 5.
per capita of the bottom 40 percent of the population (Indicator 10.1.1) will most likely—if not always—be measured using data from a household consumption or income survey. However, the GDP growth rate (Indicator 8.1.1) is measured using the national account. Deaton (2005) observes that the estimated growth rate of consumption based on the national account tends to be larger than that based on the household survey, both across countries and over time for major countries.<sup>17</sup> We plot in Figure 4 the growth rates of mean consumption levels from the household survey and GDP per capita over past decades for two major middle-income countries, India and China. Indeed, our estimates using more recent data are consistent with Deaton’s findings: the growth rate of the survey mean (blue bars) is almost always less than the growth rate of GDP per capita (red bars). For both countries, over the whole study period, the average growth rate of the survey mean (dashed blue line) is also less than that of GDP per capita (dotted red line).<sup>18</sup> This result also holds on average for more than 150 countries for 1981-2014 in our sample that combines the PovCalNet database and the WDI database.

It can be argued that Deaton’s finding, supplemented by our more recent estimates, applies to the whole distribution of consumption, rather than just a chunk of it, such as the bottom 40 percent. Still, this may be a cause for concern if the discussed inconsistencies extend to all parts of the consumption distribution. Consequently, at least for the purpose of comparison with estimates on shared prosperity (Indicator 10.1.1), estimates of the growth rate of consumption using the

> 17 Deaton (2005) argues that when rich households are less likely to cooperate with the survey than poor people, survey-based estimates of consumption will understate mean consumption. On the other
estimated to equal the as raw sugar price (c.i.f. Europe) plus 90% of the excess of the price of fully refined sugar (fob Europe) over the raw sugar price.

Under the importable hypothesis, the reference price for sugar is derived as the world free market price of sugar ( fob European ports) plus freight to India, plus port (unloading etc) costs to give an ldf price at the three Indian port cities, minus domestic transport costs from the market centers in the producing areas to the port markets. Freight (European ports to Indian ports) and port costs were estimated to be USD 38 per MT for the year 1990 and Rs 8.39 per quintal for the year 1981, respectively. These costs have been used to project costs for the other years using the tramp and port cost index respectively ( see methodology notes ). The transport cost for each state is calculated by projecting the 1981 cost using a weighted average of the rail and road indices. The 1981 cost of transport was Rs 15.03, 4.74 and 4.74 per quintal for UP to Calcutta, Maharashtra to Bombay port and TN to Madras port, respectively. The weights are based on the study of transport costs by Sharma (1991) in which he estimated the shares of rail and road transport as a function of distance ( see methodology notes) According to his study, nearly all the sugar would be carried by road, even for as long a distance as the 1441 km between Kanpur in 29 The shares shipped be rail would be: Kanpur in UP to Cal (1441 km), UP and Calcutta. Th hrssipdb alwudb apri Pt acutta(14ki) 13.1 percent; producing areas in Maharashtra to Bombay (220 kms), 1.5 percent; producing areas in Tamil Nadu to Madras (280 kms), 2.8 percent. See Annex 8 for details.

Under the exportable hypothesis, to compute the f.o.b.
.6|23.7|22.2|25.0|
|Other Europe|59.3|68.3|70.0|68.8|
|North America|3.2|3.1|2.0|1.0|
|Asia|2.0|3.1|5.3|5.1|
|Other|0.0|1.8|0.4|0.1|
|Total|100.0|100.0|100.0|100.0|
|Banks|20.3|16.3|38.7|34.0|
|Central Banks|4.5|2.4|14.4|33.3|
|Fund Managers|55.1|78.9*|25.9|4.1|
|Hedge Funds|0.0|2.3|7.5|8.2|
|Pension Funds|12.7|0.0|6.6|12.5|
|Insurance Companies|5.8|0.0|5.9|6.9|
|Other|1.6|0.1|0.9|1.0|
|Total|100.0|100.0|100.0|100.0|

* Includes pension funds and insurance companies. Source: Public Debt Management Agency

The strong interest of foreign institutional investors is shown by data on the allocation of new issues. Despite the establishment of a central securities depository and automated trading system since 1995, available data do not allow a complete analysis of public debt holdings by type of investor. This is mainly because data are recorded in nominee accounts. Plans are under way to require banks to indicate the ultimate beneficiaries of nominee accounts, at least for statistical purposes. Data from the Public Debt Management Agency show that foreign institutional investors from other European countries play
through two stress tests, described below. These stress tests do not include any feedback effect on growth and the primary deficit. Their outcome can be considered a lower bound of possible realizations, which would be amplified by lower real growth and a higher primary balance, resulting, for example, from lower revenue.

A scenario analysis which increases the real cost of financing by two percentage points for all countries results in two countries increasing their debt-to GDP ratio above 50 percent by 2030. Figure 17 introduces an external permanent increase to the real interest rates by 2 percentage points compared to their historic average. In this scenario, all but three countries in our sample still maintain a negative real interest-growth differential. While higher interest rates would lead to a deterioration in debt dynamics in all countries, Cambodia and Bangladesh, among LDICs, and Albania, Armenia and Georgia, among EMs are projected to face an increase of public debt to GDP ratio, which would increase above 50 percent of GDP in Albania and Armenia and would remain narrowly below the 50 percent level for Georgia.

Figure 17. Scenario 1: Interest Rates are Two Percentage Points Higher (Change in debt-to- GDP ratio, 2016-30) <!-- Start of picture text --> LIDCs EMs<br>3% 36% 40% 120%<br>2% 35%<br>30% 100%<br>1% 34%<br>0%<br>33% 20% 80%<br>-1%<br>32%<br>-2% 10% 60%<br>31%<br>-3%<br>30% 0% 40%<br>-4%<br>-5% 29% 50% line<br>-10% 20%<br>-6% 28%<br>-7% 27% -20% 0%<br>KHM BGD ALB ARM GEO<br>Baseline BRI Debt