rafmacalaba/data-use-mentions-extended huggingface dataset:
4 This aggregation method differs from Kaminsky's since the signals are extracted from the behavior of our composite index, while in her case each individual variable generates signals that are then aggregated into the composite index. Our aggregation procedure assumes that the leading variables drift more or less in the same direction, or have a common element in their behavior prior to the crisis. If this is not the case, it will not be a good indicator. For example, if the real exchange rate appreciates (increases) but there is a contraction in domestic credit growth, our IMV may not change and hence no signal will be issued.

Once the composite index of macroeconomic vulnerability (IMV) is built, its sinmple evolution is not informative enough about a potential risk situation. Thresholds have., to be used in assessing when the IMV has reached an "anomalous" level.

We applied four transformations or filters to the IMV to generate signals: detrending the variable with respect to its long-run level (defined as the Hodrick-Prescott trend), the variable in levels (no iFilter), detrending the variable with respect to a short-run (6 mos.) moving average and thLe residuals of an ARIMA model fitted to the IMV. The first three (or variants) have been used in other papers while the ARIMA residuals approach is novel, and produced the lowest Type I error (probability of not anticipating the crisis). Initially we focus on the IMV as described above, to make our sample period as comparable as possible to that of other papers. In a second stage stock market prices in real terms are included as an additional leading variable, but the sample period is shortened to 1986-199,8 due to availability of consistent information from the IFC on this variable for most of the sample countries.

# A. Deviations from trend model (DT model)

This model uses deviations from a long run trend to generate signals. To determine wriat the long run trend of a series is, the IMF used a 3-year moving average
. if at least one battle related deaths occurs. In the second specification, conflict is defined by a higher threshold, by 0.008 deaths per 1000 population.<sup>10</sup> We expect to get different results from the two specifications. From the analysis of Figure 1 we know that economic damage of civil war increases with the severity of conflict. The estimated impact from the second model should therefore be more acute.

Table 1, panel A and B, reports the results. Each column contains one of our measures for economic growth. In all specifications, conflict incidence correlates negatively with country-level economic performance. The estimated coefficients of conflict incidence are statistically significant and negative. We also find that the coefficients

> 7For a discussion see Henderson et al. (2012). In order to calculate light per capita we use population data that is provided by a World Bank dataset.

> 8The standard reference here is Miguel et al. (2004). Ciccone (2011) shows that high rainfall levels three years earlier seem to be best predictors of conflict in the reduced form. Miguel and Satyanath (2011) argue that lagged negative growth shocks are a predictor of conflict onset. In any case, there is no evidence from this literature that contemporaneous growth declines cause conflict. Bazzi and Blattman (2014) corroborate the view that the relationship between income shocks and conflict is not straightforward. They do not find evidence of an effect of price shocks on conflict onset and only weak evidence on incidence.

> 9Results from this are presented in the Appendix.

> 10We take the threshold from Mueller (2016) who shows that a threshold like this leads to a similar number of coded civil wars as the threshold of 1000 battle-related deaths often used in the conflict literature. In the context here, this is a conservative approach as it is not the threshold which yields the biggest difference between conflict and non-conflict countries.

10
surveys asking about incomes or wealth that oversample high-income or high-wealth households or areas. Oversampling rich households using tax or other forms of administrative data is not common, even in high income countries, probably because it requires coordination between various public agencies (for example national statistical offices and tax authorities). In addition, administrative tax data in LMICs may miss the rich whose incomes are outside the tax system, which may be a large proportion of those at the top of the income distribution (Haq, 1964, Ravallion, 2022). Nevertheless, it would be useful, perhaps as a pilot, for an LSMS to oversample high income households using tax data, where this can be obtained. A less ambitious method of oversampling would be to use population census data to oversample small areas with high incomes or with characteristics that are correlated with high incomes, if income data is not collected in the census. A version of this was attempted in the NIDS panel fifth wave in South Africa to increase the sample size of white and Indian South Africans, by drawing a top-up sample from enumeration areas with large proportions of these groups, using estimates from the most recent population census (Branson, 2019).

29
financial inclusion and fostering financial stability. In contrast, low tax rates are estimated to have a significant positive effect on the covariance between inclusion and stability—that is, generate or increase synergies between the two outcomes. Greater formalization of the economy—increase in the percentage of firms that formally registered when they started operations in the country—is estimated to introduce or significantly increase the trade-off (this variable is more significant than in the univariate conditioning exercise). Education—enrollment rates in secondary education—is estimated to generate synergies between financial inclusion and stability (although this effect was insignificant in the univariate conditioning). Perhaps the transmission from education could work through improved financial literacy that ensures responsible financial inclusion, with positive implications for financial stability (Klapper, Lusardi, and Panos 2013).

The results are somewhat different when the maximum number of countries are included (Table 6, Column 2), which includes many high-income countries that were excluded in the first exercise immediately above. Here financial openness is insignificant, probably because more high income countries with greater capacity to manage capital flows are included. Low tax rates retain their positive effect, at the 5 percent significance level (a stronger result than the 10 percent level in Column 1). Population density is estimated to have a significant, positive effect on the covariance, and can thus introduce or increase synergies between inclusion and stability (at the 10 percent level)—even though population density was not significant in our univariate conditioning. More densely populated countries may benefit from more intensive social networks that ease screening and monitoring of financial customers, as well as ease the emergence of self-monitoring groups—for instance, credit cooperatives and private guarantee schemes. Other things equal, finance could thus be more responsible in densely populated countries and generate synergies with financial stability by increasing the scale of the market and diversifying exposures of banks. Finally, the depth of the credit information index (the coverage and richness of credit reporting systems) is significant in generating or enhancing synergies between inclusion and
Labor Office's Yearbook of Labor Statistics 1995 and refer to 1994.

# **Belgium**

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1992. Data on Central Government, Non-central Government, Education, Health and State-owned enterprise employment are taken from IMF Report No. SM/94/289 of December 7, 1994 and relate to 1/1/ 94. In Belgium, education is exclusively the right of non-central government. Health services responsibilities are shared between the central government and the communities.

Consolidated Central Government wages and salaries are for 1993 and are taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing (monthly basis) are taken from the Intemational Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993.

# **Canada**

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1994.

Data on Central and Non Central Government employment are taken from IMF Report No. SM/95/62 of April 7, 1995 and relate to 1994. In our data, federal employees correspond to central government, whereas non-central government employees include employees of both the county governments and of municipalities. Data on Education and Health employment are from Public Management: OECD Country profiles, OECD 1992 and relate to 1990. Responsibility for education is essentially with the county government. The federal government contributes financially, but salaries are paid by the provincial government. Health services are also mainly the responsibility of non-central government. Data on State-owned enterprises are also from Public Manaaement: OECD Country profiles, OECD 1992 and relate to 1990. Military employment data exclude personnel of paramilitary units, i.e., the Coast Guard (5,200).

Consolidated Central Government wages and salaries are for 1991 and are taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing (monthly basis) is taken from the International Labor Office's Yearbook of
understanding the actual determinants, drivers, and mechanisms of fragmentation in production networks—and especially under what circumstances firms decide to outsource—whether motivated by strategic or cost-saving decisions (for a complete review of the literature on outsourcing, see Mankiw and Swagel, 2006; and Escaith et al., 2010).

A substantial portion of empirical work devoted to trade almost invariably suggests that reductions in logistics costs have a positive impact on aggregated trade flows (e.g., Hoekman and Nicita, 2010). Similarly, recent work on the determinants of trade costs has found that logistics performance plays an important role (Arvis et al., Forthcoming). However, the link between reduction of logistics costs and GPNs has been mostly neglected in applied research, except for a few cases discussed above. Logistics costs have not been captured in a straightforward manner in those cases, leaving open the question for further examination in the remainder of this paper.

# **3 Data and Methodology**

## **3.1 Data**

For full details of our data and sources, see Table 1. In this section, we discuss two novel aspects of our dataset. First, we rely on a parts and components product list based on Ando and Kimura (2005) and Obashi and Kimura (2010), which allows us isolate trade in parts and components from trade in final goods. According to the Harmonized System, manufactured goods range from Chapters HS28 to HS92. Hence, we include all relevant goods classified as part of general machinery (HS84), electric machinery (HS85), transport equipment (HS86-89), and precision machinery sectors (HS90-92). According to these authors, the distinction between intermediate goods and finished products is far from obvious, not only because of the level of aggregation in HS 6-digit information, “but because the HS classification is not designed on the basis of the functionality of goods”. Hence components are only incorporated into this 7
Although the level of the minimum wage relative to average wages is an important piece of information, one must keep in mind that minimum wages are designed to increase earnings of workers at the lowest end of the earnings distribution. These workers are typically those with lower levels of education. Thus, what ultimately matters is the ratio of minimum wages to average wages for low educated and low paid workers. Table 18 below shows that even workers with no diploma earn on average more than 3 times the minimum wage. This reflects the small earnings differentials by levels of education in Vietnam. As can be seen in table 18, workers with upper secondary education only earn 20% more than workers with no diploma.

**Table 18: the minimum wage is low even for workers with little education**

||Monthly<br>Average wage<br>(in Dongs)|Ratio of minimum to<br>average wage|
|---|---|---|
|No diploma|486,230|0.30|
|Primary diploma|509,430|0.28|
|Lower secondary diploma|497,050|0.29|
|Upper secondary diploma|567,620|0.25|
|College and university|747,840|0.19|

_Source:_ VLSS 2 (GSO, 1999> Given this paper's focus on Vietnam's export potential, the question as to whether the minimum wage is binding in the labor-intensive export-oriented sector is of particular interest. A visit to Haiphong's export-oriented garments, footwear and food processing enterprises shows that here again market wages are above minimum levels. In Haiphong, fulltime workers earn on average 300,000 to 550,000 VND per month, depending on productivity. This is two to four times more than the mandatory minimum. The lowest level of pay that was observed in Haiphong was 10,000 VND a day, which still yields a monthly income of more than 50
difference out the time-varying, unobserved heterogeneity. We could control for the bias due to time-varying heterogeneity in two possible ways. One is to apply an FE method, such as model (3), which controls for initial household- and village-level characteristics using predetermined variables; that is, this version of the FE model controls for initial characteristics, assuming that these initial factors control for the time-varying heterogeneity that correlates the errors of the borrowing and outcome equations. More specifically, following Jalan and Ravallion (1998), we rewrite model (3) as follows: where _X ij_ 0 is a vector of household- and village-level characteristics for the initial survey year (i.e., 1991/92).

A second way to control for the bias due to time-varying heterogeneity is to apply a propensity score–weighted FE method. Following Hirano, Imbens and Ridder (2003), we first calculate the weight variable on the basis of the propensity score obtained through the participation equation, using the 1991/92 survey data. More specifically, the weight variable is given a value of 1 for participants and _p_ /(1- _p_ ) for non-participants, where _p_ is the propensity score (probability of receiving microcredit). In the second stage, the impact of microcredit is estimated using householdlevel FE as before, but this time as a weighted regression incorporating the weight variable.<sup>19</sup>

> 19 Another estimation technique commonly used to control for time-variant heterogeneity for many outcomes is the Dynamic Panel Generalized Method of Moments (GMM) estimator.  Unfortunately, with just 3 time periods in our data set, some data transformations necessary to implement the GMM cannot be achieved.  For example, the endogenous program participation must be instrumented with its own second or deeper lagged variable because, unlike the first lagged variable, it is not correlated with the error term.  With respect to our data, the second lagged variable is invalid for both the 1991/92 and 1998/99 observations.

21
# **Skill Use, Skill Deficits, and Firm Performance in Formal Sector Enterprises: Evidence from the Tanzania Enterprise Skills Survey, 2015**

By Hong Tan, Sajitha Bashir and Nobuyuki Tanaka<sup>**1**</sup> JEL codes: I2, J2, L25 Keywords: skills, education, training, demand and supply of labor, firm performance

> 1 The authors gratefully acknowledge the assistance provided by Yevgeniya Savchenko, Mariam Adil and Martin Moreno in analyzing the survey data, and of the DEC Enterprise Survey Team (Jorge Luis Rodriguez Meza, Silvia Muzi, Gemechu A. Aga, and Arvind Jain) in conducting the Tanzania Enterprise Skills Survey, 2015.
Higher level inspections and<br>audits|Client feedback and redress,<br>comparison of baseline and post-<br>grant data on quality and access|
|Penalties|Audit observations on financial<br>compliance|Public censure, competitive<br>pressures, voice and exit options<br>for clients|
|Managerial flexibility|Little or none. No tolerance for<br>risk and no accountability for<br>failure.|Rewards for risks but penalties<br>only for persistent failure.|
|Local government autonomy and<br>budgetary flexibility|Little|Absolute|
|Transparency|Little|Absolute|
|Focus|Internal|External, competition, innovation<br>and benchmarking|
|Accountability|Hierarchical to higher-level<br>government, controls on inputs<br>and process with little or no<br>concern for results|<br>Results-based, bottom-up, client-<br>driven; empowers citizens to<br>hold their governments to<br>account.|

_Source_ : Shah (2007).

23
*Result 2.** Signaling a socioemotional skill decreases the probability of being invited for an interview if the skill is not solicited in the vacancy, and it increases the probability of being invited for an interview if the skill is asked for in the vacancy text.

We finally investigate whether the probabilities of callback for our treatment CVs are different according to the applicant’s gender. Table 14 provides the results from OLS regressions conditional on the applicant’s CV clicked, and with cluster-robust standard errors at the vacancy level. Models 1 to 3 show that female candidates with socioemotional skill signals in their CVs are around 5 percentage points less likely to be invited for an interview when the vacancy text does not solicit the signaled socioemotional skill. On the other hand, Models 4 to 6 show that the same effect is close to zero for the male candidates.

**Result 3.** Firms that do not ask for the signaled socioemotional skills in the vacancy text evaluate these skill signals negatively only for female applicants.

> 23 Separate balance tables for each sector are available upon request. Balance tables look very similar across sectors. 24 Unconditional versions of these regressions are available upon request. Results do not vary.

> 25 Using Model 4, a joint significance test for SE skills * Signaled SE skill required in vacancy and SE skills gives an F- statistic of 3.60, with a p-value of 0.0579.

> 26 Separate regressions for each cluster are in line with these findings and are available upon request from the authors.
An alternative hypothesis suggested by the theoretical results of (Diamond, 1984, 1991,1993) and the empirical results of Demirguc-Kunt and Maksimovic (1999) and Gianetti (2003), among others, suggests: _Hypothesis 1A: Poor legal quality implies that external capital will be concentrated in debt (via intermediaries) and in particular short-term debt thus there should exist:_

- _i. a positive relation between legal effectiveness and equity, and_

- _ii. a negative relation between legal effectiveness and total debt, and_

- _iii. a positive relation between legal effectiveness and debt maturity._

These hypotheses are predicated on the binding nature of the local legal system. If creditors and borrowers can avoid a poor local legal system by writing debt contracts that are enforced in countries with better legal systems, as we described earlier, then these predictions should only apply to local contracts. Since poor legal effectiveness is likely to be a binding constraint that results in less than optimal lending, we expect firms that can use foreign currency to loosen this constraint, will do so. Therefore, we suggest the following hypothesis.

_Hypothesis 2: When possible, firms will avoid the constraints of a poor legal system by contracting for debt outside their home country (i.e., by using foreign currency debt), thus there should exist:_

- _i. no relation between legal effectiveness and total debt or debt maturity for firms with sufficient access to foreign debt, and_

- _ii. a positive (negative) relation between legal effectiveness and the use of both shortterm and long-term local (foreign) currency debt._

In summary, observed empirical relations that appear contradictory can be explained by separately examining local and foreign currency debt.

# **III.   Data**

We rely on two primary data sources for our study. The first is firm-level accounting data from the WorldScope Database. We examine all non-financial firms with sufficient data for fiscal year 1996 in nine East Asian countries: China, Hong Kong, Indonesia, Malaysia, The Philippines
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Meyersson,