rafmacalaba/data-use-mentions-extended huggingface dataset:
|20544|20544|
|Firms|5226|5226|5226|5226|
|frm and year FE|Yes|Yes|Yes|Yes|
|log employment|Yes|Yes|Yes|Yes|
|industry-specifc trends|Yes|Yes|Yes|Yes|
|frm-specifc trends|Yes|Yes|Yes|Yes|
|imported inputs|Yes|Yes|Yes|Yes|

NOTES: IV-FE regressions of (log) employment on export intensity (exports/sales) and the average per capita GDP of a firm’ export destination. Columns (1): estimates of export intensity; column (2): estimates of average per capita GDP; column (3): estimates of an export dummy; column (4): estimates of a high-income export dummy. All regression include firm fixed-effects and year fixed-effects, log total employment (firm size), firm-specific trends, and the share of imported inputs. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

23
.896|0.870|0.870|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

# **_Connectivity_**

Table 3 focuses on the role of connectivity. Remarkably, all six variables are significant with the expected positive sign when we looked at GVC integration as a seller. Better internet coverage and a higher LPI with regard to customs procedures, logistics, and overall values as well as shorter export and import times have the capacity to magnify economic upgrading through forward GVC links.

Regarding GVC integration as a buyer, however, we observe that only higher LPI customs values show a positive sign (table 3, column 3). This speaks in favor of connectivity being an important factor for competitiveness of the export sector. Timely, reliable, and efficient customs and other administration procedures benefit, in particular, domestic value added. Thus, this result highlights a further positive impact of trade facilitation agreements.

20
remedies exist, including: stating in advance no additional aid will be reduced, land‐use planning that forbids development in the floodplain, and buying out of structures in the hazardous areas (the MMC study noted above found floodplain buyouts to have some of the highest benefit‐cost ratios).

Although businesses do not present as needy an image as a family that has been displaced from its home, businesses still seek and do not refuse disaster assistance, even if they are guilty of moral hazard. This ranges from the smallest businesses in developing countries to the largest corporation in industrialized ones, as evidenced in the aftermath of the financial meltdown leading to the Great Recession recently in the U.S.

Many well‐intended policies contribute to the moral hazard situation. For example, the Ministry of Finance and Public Credit in Mexico has implemented two major strategies: a) creation of a natural disaster fund and b) risk transfer through reinsurance and catastrophe bonds. The former sends a signal to potential disaster victims that they will be bailed out. The latter does not really reduce disaster risk, but simply spreads it out. This is not to say that the policies are not well‐intended or benefical, it is just that they have some features that do not reduce overall risk. At the same time, there are several ways to stem moral hazard, such as the deductibles in insurance policies or “index‐based” insurance, where protection is gauged in terms of factors that cause losses rather than the level of losses.

# **4.5. Distributional Considerations**

Often neglected in risk assessments is the distribution of costs and benefits. These are important for at least three reasons. First, numerous studies have determined that the least well‐off groups are those most vulnerable to disasters; moreover, their condition is exacerbated by disasters. Thus, disasters are a great concern from an equity, or fairness, standpoint. Second, lagging social economic groups or lagging regions have been found to represent a
sectors and type of product in which they are active, or tailored to those segments that merit closer scrutiny during the visit. For example, for borrowers whose credit quality has deteriorated, banks’ largest obligors and connected borrowers could merit a more thorough review. When PCRs register individualized credit operations, they can produce samples to evaluate how new credit policies or financial products are impacting the bank’s risk profile, and if this is consistent with their credit strategy.

For prudential purposes, in many countries regulatory frameworks set a criteria or mandatory rating system that banks must use to risk classify their borrowers and to compute loan loss provisions. Increasingly, though, they are allowing banks to use their own rating systems for those purposes. At times they require these particular banks to map their grades into a single and homogeneous scale that allows performance comparisons across banks. In either case, during on-site inspections bank supervisors examine the reliability and robustness of banks’ risk rating processes and systems and, in particular, whether they are underestimating default risk. Banks’ portfolio samples obtained from PCRs should look for inconsistencies or abnormalities in rating systems or rogue credit policies.<sup>22</sup> For example, they should flag and track refinanced/restructured credits to monitor their payment behavior. It is also helpful that samples cross check risk ratings assigned by different banks to the same borrower: significant and systematic differences should be deemed abnormal and should trigger the necessity for further revisions.

# _Off-site monitoring_

Bank on-site examinations can be highly demanding in time and resources for both the supervisor and the inspected institution, and consequently are conducted at periodically timed intervals. Off-site monitoring procedures are thus an efficient aid for an effective ongoing supervision, allowing supervisors to monitor banks and observe how their risk profile evolves between inspections, with the possibility to promptly identify changes in their financial condition.

Besides its usefulness for conducting on-site supervision, PCR data can support continuous off-site bank monitoring. PCRs databases can be programmed to produce regular, timely supervisory reports (for internal
errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

45
# **3 Research Design and Sample**

As discussed above, local cadres’ promotion incentives, the lack of portability, the lack of certainty in policies relating to the future for migrants and their families, and the complexity of social insurance schemes may all contribute to low participation rates among China’s migrant workers. This paper focuses on the extent to which information about schemes and enrollment processes affects participation in urban pension and health insurance programs. The data used in this study come from the Rural-Urban Migration in China (RUMiC) survey.

## **3.1 The RUMiC Survey**

The Rural-Urban Migration in China (RUMiC) survey is a longitudinal study with nine rounds: the initial wave was carried out in 2008, with additional rounds conducted annually during each of the last 8 years. Migrants are surveyed in 15 cities, including such coastal migrant destinations as Guangzhou, Shenzhen, Dongguan, Shanghai, Wuxi, Nanjiang, Hangzhou, and Ningbo, as well as major cities in interior regions, including Chengdu, Chongqing, Wuhan, Hefei, Bengbu, Zhengzhou and Luoyang. Unlike other surveys of migrant workers in China, in which migrants are sampled primarily by urban residential address, the RUMiC uses a workplace sampling strategy. In contrast with urban local residents, rural migrants frequently move to cities alone and often live in factory dormitories or other workplaces. Even in cases in which migrants bring their families to the city, high urban rents deter them from living in the type of urban residences that comprise standard sample frames (such as those maintained by the National Bureau of Statistics). More conventional urban household sampling frames tend to yield a biased sample of migrants, over-representing those who are more affluent, have longer tenure and more secure positions in the city than the “representative” migrant. By using a sampling frame based on a census of work-places, RUMiC avoids this bias.<sup>9</sup> Although RUMiC is designed as a longitudinal survey, the young and mobile nature of
scores across countries and over time are comparable because the tests are linked assessments and the scores are scaled.<sup>10</sup>

# **International health systems—OECD**

It is well known that countries vary considerably in their per capita spending on health care, with the US being among the largest spenders per capita. Inevitably, there has been and continues to be a lot of debate on the question of whether the high spenders (especially the US) achieve sufficiently better results to warrant the extra spending (cf. e.g. Anderson and Frogner 2008), and more generally on how to measure health sector results in international comparisons

> 7 See http://www.pisa.oecd.org/ for further details of the PISA program. For studies examining the cross-country differences in PISA, see Dobert, Klieme, and Sroka (2004), Ammermueller (2007), and Fuchs and Woessmann (2007).

> 8 Afonso and St Aubyn (2005) is an exception. They use DEA to analyze efficiency in education spending in OECD countries using the PISA data.

> 9 We have not included data for 2009 because most countries do not have expenditure data available in the WDI database. The secondary education expenditure per student is generated by multiplying purchasing power parity GDP per capita (2005 constant value) with expenditure per student in secondary education as a percentage of GDP per capita. 10 See PISA 2003 Technical Report (Adams 2005) for details.
2. New entrants are mainly drawn from the most productive set of _OR_ firms. The extensive margin of trade composition.

3. Product standards harmonization decreases export sales at existing exporters. The intensive margin of trade.

The model presented above is fairly flexible in terms of the assumption on the level of stringency of international norms in country _R_ . Note that hypotheses 1 and 3 remain unchanged if I assume that country _F_ and country _R_ have different but equally costly product standards to begin with. In this case, harmonization involves a different composition of the extensive margin of trade (hypothesis 2). It is represented now by entry from the most productive non-exporter firms because they can now access both markets upon the payment of a single fixed cost. Alternatively, I can relax the assumption that country _R_ uses international norms and get the same theoretical results. In this scenario the only required assumption is that country _R_ has lower fixed costs to export than country _F_ and that harmonization entails some reduction on the cost to export to country _F_ due to, for example, the elimination of standards aimed to protect domestic firms.

# **3 Data**

This analysis uses the U.S. linked/Longitudinal Firm Trade Transaction Database (LFTTD), which links individual U.S. trade transactions to U.S. firms in the Longitudinal Business Database (LBD)<sup>10</sup> , in conjunction with firm level information from the Censuses of Manufactures (CM) of the Longitudinal Research Database (LRD) of the U.S. Census Bureau. A key contribution of this study is the linking of firm level U.S. manufacturing data to industry level measures of EU product standards and their relationship with international norms. This section outlines the main features of the datasets.

## **3.1 U.S. Manufacturing firms Across Industries and Time**

The CM is conducted every five years and the empirical part of this paper makes use of CM information from 1992,
of firms are in the AMA, and they employ 60 percent of the formal private sector workforce; 14 percent of firms are in Tema; 22 percent are in Tamale (Northern region); and 10 percent are in Takoradi (Western region). The location of establishments is related to agglomeration effects (urban centers). Thus the employment density is highest in Accra and decreases with the size of the firm locality (Table 1).

# **_Employment Growth: Which Firms Are Adding Jobs?_**

Figure 5 shows the aggregate net growth employment rate, which was an estimated 8 percent per annum — that is the stock of employment was estimated to grow at nearly ten percent a year over the period 2010 to 2012. Underlying this net rate was a job creation rate of nearly twelve percent and a contraction rate of four percent. Put differently, then, the growing firms over this period added jobs at nearly a fourfold rate compared to the firms that shed jobs over the same timeframe.

> 18 Based on the World Bank’s Entrepreneurship database, which includes information on the density of new businesses registered per 1,000 people. By this metric, in 2004, the first available data point, this rate was 0.49; by 2012 the rate was 0.90 (and was 1.09 in 2011).

12
and Bergstrand (2007) and BBF. Third, whereas BBF examine the impact of agreement depth on trade flows, we use a binary EIA variable in order to keep the discussion focused around the effects of geographic proximity. We do follow the Baier and Bergstrand (2007) and BBF convention of using panel data over five-year intervals (1965, 1970, 1975, ..., 2010) to isolate our results from year to year variability.

In our first pass at better understanding the interplay between closeness and EIAs we augment the baseline gravity specification with an interaction term between the CEPII capital city to capital city distance measure<sup>14</sup> and our EIA dummy from the DESTA dataset, as shown in equation (7): For robustness, we also add a vector of controls to account for trade agreement depth. This is pertinent, as distance and depth of agreement are correlated (see section 3.2). We thus estimate: where Dijt is a vector comprising the seven dummy variables for each provision category in the DESTA dataset, as described in section 2.2.

> 13 This specification is equivalent to that used in BBF for aggregate trade flows only.

> 14 CEPII provides various measures of bilateral distance. The most frequently used are simple distance, which measures distance from capital city to capital city, and population-weighted distance (both detailed in Mayer and Zignago (2011)). Regression results when using the two different measures were very similar (as was also found by Johnson and Noguera (2012)). Therefore, we report results using the simple distance measure since it is more commonly used in the gravity literature.

7
have argued that the period after weaning from breast milk until age 24 to 36 months may also be “critical,” as protection from the mother during pregnancy and breastfeeding ends (Maccini and Yang, 2009). Second, this approach helps to check the validity of the research design by testing for differential trends between program receiving and non-receiving communities.

The results of the health index, cognition index, and economic index are shown in Figure 2 to Figure 4 for males and females separately. The coefficients for males are strictly positive for age of exposure less than or equal to 0 years, while it is slightly positive until age 2 years (but statistically insignificant) and zero for age greater than 5 years. The graphs thus indicate that prenatal exposure (especially around conception) to midwives is the most beneficial in terms of adult outcomes, while the impacts are minimal if exposed 4 years after birth. The graphs for females show flat stretches around zero, indicating a negligible impact of midwives in adult outcomes for females. Thus, the evidence from the event study is consistent with the results presented in Table 2, Table 3, and Table 5.

The results also help in validating the research design. The flat stretches of the diagram for any exposure in late childhood lessen the concern that the estimates are confounded by differential cohort trends between program receiving and non-receiving communities. The graphs are also consistent with the fact that the results presented in Table 2 to Table 5, are not driven by the disproportionate impact of the 1997 financial crisis on older cohorts living in the program areas.

# **6 Discussion and Additional Results**

This section extends the previous analysis to examine the robustness and sensitivity of the results to alternative specifications. Moreover, it analyzes plausible mechanisms behind the results and explores the results with different sub-samples.<sup>33</sup>

> 33Additional regression analyses to provide suggestive evidence on the likely pathways through which early-life midwife exposure affects adult economic well-being are shown in the Appendix S7. Moreover, while
deregulatory product market reforms dependent on the labor market regulations in place. They proxy workers’ bargaining power with the share of workers whose wages are determined by union agreements, regardless of whether they belong to a union (“bargaining coverage”). When workers’ bargaining power is high, increased competition reduces unemployment more than when it is low. For example, when firm profitability drops by 3 percentage points, unemployment is estimated to decrease by only 0.6 percentage points in economies with low bargaining power (a coverage of 53%, for example), while it decreases by 1.1 percentage points in high bargaining power (97% coverage) settings.<sup>31</sup> This is in line with Nicoletti and Scarpetta (2005), who find that the positive employment effects of deregulating product markets are larger when labor markets are more restrictive. Interestingly, real wages increase less after the increased competition when bargaining power is high than when it is low.

Again, based on OECD data, Bassanini and Duval (2009) estimate reduced-form unemployment equations consistent with standard job-search and wage-setting models. In the average sample country, less stringent PMR is consistently associated with decreases in aggregate unemployment. However, the results are not consistent across countries when analyzing interaction between individual pairs of policies and accounting for their endogeneity. The authors argue the entire regulatory framework (not always just a pair of two policies) is the more relevant dimension to consider when evaluating the effectiveness of a policy given its context.

They proceed to construct a measure capturing how ‘employment-friendly’ the overall regulatory framework is within individual countries. The measure consists of the sum of the linear unemployment effects of individual regulations. In subsequent regressions, this measure is interacted with each individual regulation. The results show consistently that there are reinforcing effects between the individual policies and the overall regulatory framework: any reform that reduces regulations which are detrimental to employment (for example, anti-competitive PMR), would have a larger impact the more employment-friendly the overall
(PPML) version of the lasso and show how to choose the tuning parameter of this estimator using either cross-validation or the “plug-in”(or “theory-driven”) approach of Belloni, Chernozhukov, Hansen, and Kozbur (2016), which accounts for heteroskedasticity and clustered errors.<sup>1</sup> We apply our PPML-lasso estimators to a comprehensive data set on PTA provisions recently made available by the World Bank (Mattoo, Rocha and Ruta, 2020). Importantly, this database is very detailed, to the point where the number of provision variables we consider is larger than the number of PTAs we observe in our data. In addition, due to template effects and possible synergies between groups of provisions, the 305 provision variables in our data can be highly correlated with one another. We find that the number of provisions selected when using the PPML-lasso with the tuning parameter chosen by cross-validation is too large for the model to have a meaningful interpretation and that, in contrast, the number of provisions identified when using the plug-in penalty is too small to allow us to be confident that it includes the majority of relevant provisions.<sup>2</sup> To address these issues, we introduce two additional methods that seek to identify potentially important variables that may have been missed in an initial lasso step based on the plug-in penalty. One of the methods, that we call “iceberg lasso”, involves regressing each of the provisions selected by the plug-in lasso on all other provisions, with the purpose of identifying relevant variables that were initially missed due to their collinearity with the provisions selected in the initial step. The other method, termed “bootstrap lasso”, augments the set of variables selected by the plug-in lasso with the variables selected when the plug-in lasso is bootstrapped. As we show using simulations, these new methods present a favorable balance between the parsimony of the plug-in lasso and the lenience of cross-validation methods in small-to-moderate data sets where the true causal variables may be highly correlated with an
Rethinking North American Integration Survey that replicated six questions from the 2000 EWCS for Canada and the United States, to compare these two countries with 15 European countries in five dimensions of job quality.

There have been notable efforts to measure multidimensional job quality at the regional level in developing countries, especially in Latin America, where relatively high-quality survey data are available (Brummund, Mann, and Rodríguez-Castelán 2018; González et al. 2021; Sehnbruch et al. 2020). Brummund, Mann, and Rodríguez-Castelán (2018) use harmonized data on 15 Latin American countries to focus on four dimensions that can be consistently identified in the literature: income, stability, benefits, and job satisfaction. This study includes both wage employment and self-employed workers. Sehnbruch et al. (2020) include nine Latin American countries and focus on the three dimensions proposed by the OECD (Cazes, Hijzen, and Saint-Martin 2016): income, stability, and employment conditions. González et al. (2021) focus on six Central American countries and follow a similar approach to Sehnbruch et al. (2020), but include job security as a fourth dimension. All three studies include similar indicators (contract work and tenure) in their stability or security dimension.<sup>8</sup> Benefits indicators (social security receipts, health insurance, or retirement benefits) are included as indicators in the benefits dimension by Brummund, Mann, and Rodríguez-Castelán (2018), in the security dimension in González et al. (2021), and in the employment conditions dimension of Sehnbruch et al. (2020). Bocquier, Nordman, and Vescovo (2010) have conducted the only other study that looks at developing countries (West Africa region), but, because of the data limitations, they only include capital cities in seven countries.

The limited attempts to measure multidimensional job quality in developing countries reflect the lack of comprehensive quality data in these countries. This paper contributes to the literature by developing a multidimensional JQM that is global in scope