rafmacalaba/datause-encoder-data huggingface dataset:
risk would become more sensitive to these developments.

Future research could focus on the endogeneity of the share of foreign investors by using instrumental variables. This study used a lagged value of the foreign investor share to avoid simultaneity bias and it maintained the assumption of exogeneity. However, this assumption could be relaxed by allowing for endogenous regressors although that would lead to additional econometric challenges.

# **References**

- Adolfson, M., S. Laseen, J. Linde, and M. Villani. 2007. “Bayesian estimation of an open economy DSGE model with incomplete pass-through.” _Journal of International Economics_ 72:481–511.

- Adrian, T., and E. Crump, Richard Moench. 2013. “Pricing the term structure with linear regressions.” _Journal of Financial Economics_ 110:110–138.

- Agca, S., and O. Celasun. 2012. “Sovereign debt and corporate borrowing costs in emerging markets.” _Journal of International Economics_ 88:198–208.

- Amstad, M., F. Packer, and J. Shek. 2020. “Does sovereign risk in local and foreign currency differ?” _Journal of International Money and Finance_ 101:1–16.

- Arslanalp, S., and T. Poghosyan. 2016. “Foreign Investor Flows and Sovereign Bond Yields in Advanced Economies.” _Journal of Banking and Financial Economics_ 2016:45–67.

- Arslanalp, S., and T. Tsuda. 2014a. “Tracking Global Demand for Advanced Economy Sovereign Debt.” _IMF Economic Review_ 62:430–464.

- —. 2014b. “Tracking Global Demand for Emerging Market Sovereign Debt.” IMF Working Paper No. 14/39, International Monetary Fund, Washington, DC.

- Asonuma, T., S. Bakhache, and H. Hesse. 2015. “Is Banks’ Home Bias Good or Bad for Debt Sustainability?” IMF Working Paper No. 15/44, International Monetary Fund, Washington, DC.

- Azzimonti, M., E. de Francisco, and V. Quadrini. 2014. “Financial Globalization, Inequality, and the Rising Public Debt.” _American Economic Review_ 104:2267–2302.

20
# **6.  EDGAR Emissions versus The IFPRI Field Scale**

For comparison, we introduce the latest (2018) agricultural methane emissions estimates<sup>11</sup> from the Emissions Database for Global Atmospheric Research (EDGAR (2022)), the global standard for “bottom-up” emissions estimation that combines detailed sectoral activity data with emissions factors that are adjusted for broad regional differences that may incorporate differences in water supply conditions and field configurations.

Since sectoral activity data drive the EDGAR emissions estimates, we could expect them to exhibit a relatively close relationship. Table 2 presents OLS, Robust and Cluster results for a percentile regression model that relates EDGAR emissions to the IFPRI estimates of irrigated rice field extent. The fit is excellent; the regression R<sup>2</sup> is 0.5856, as compared with 0.2574 for the SP5 CH4 anomalies in Table 1.

# **Table 2:  EDGAR CH4 emissions estimates vs. irrigated rice field scale (IFPRI)**

Dependent variable: EDGAR Emissions Percentile

|OLS|Robust|Cluster|
|---|---|---|
|Field Extent<br>0.771***|0.771***|0.771***|
|Percentile<br>(167.06)|(182.20)|(31.57)|
|Const.<br>11.46***|11.46***|11.46***|
|(42.70)|(43.17)|(7.84)|
|R<sup>2</sup><br>0.5856|0.5856|0.5856|
|Obs.<br>19,755|19,755|19,755|
|t statistics in parentheses:<br>* p<0.05 ** p<0.01 *** p<0.001|||

Although the regression R<sup>2</sup> is relatively high, substantial unexplained variance remains because EDGAR’s regional adjustments prevent a near-perfect global fit between the EDGAR estimates and field scale alone. The EDGAR regional adjustments are intended to capture the broad effects of typical regional differences in water use and field configuration that affect methane emissions, given field scale. For the current exercise, a critical question follows: Do the SP5 CH4 measures

> 11 Available online at https://cidportal.jrc.ec.europa.eu/ftp/jrcopendata/EDGAR/datasets/v60_GHG/N2O/AGS/v6.0_N2O_2018_AGS.0.1x0.1.zip
The impacts of endemic framing are marginally larger in interventions conducted later than earlier ( _β_ = 0.046, _p_ = 0.057), but only when controlling for Wellcome indicators. Specifically, on average, for each month increase of the intervention start date, there is a marginally corresponding increase of 0.046 units in the impact of endemic messages on vaccination intentions.

# **_Country-Level Trust in Healthcare Workers_**

Trust in healthcare workers (e.g., doctors and nurses) was not a significant moderator for any of the framings.

# **_Country-Level Perceived General Vaccine Safety_**

General perceived vaccine safety (i.e., not COVID-specific) was not a significant moderator for any of the framings.

# **_Country-Level Perceived General Vaccine Effectiveness_**

The impacts of variants framing ( _β_ = 1.21, _p_ = 0.096) are marginally larger in countries with higher general perceived vaccine effectiveness. Specifically, on average, for each unit increase in level of agreement with the statement “Vaccines are effective”, there is a marginally corresponding increase of 1.21 units in the impact of variants framing on vaccination intentions.

_Table 3. Study-level moderation meta-regression results._

|Moderators|Exp|erts<sup>a</sup>|Safety|Efficacy<br>and Pro-<br>Social|End|emic<sup>a</sup>|Variants|
|---|---|---|---|---|---|---|---|
||(1)|(2)|||(1)|(2)||
|COVID-19 vacc. rate in sample||||0.003|-0.000|-0.004||
|||||(0.002)|(0.005)|(0.004)||
|Start month of experiment|0.031**|0.025||0.005|0.021|0.046*||
||(0.013)|(0.016)||(0.011)|(0.021)|(0.024)||
|Trust in healthcare workers||-0.368|-0.421|||||
|||(0.405)|(0.340)|||||
|General perceived vaccine safety|||-0.248<br>(0.207)|||||
|General perceived vaccine||||||-0.774|1.213*|
|effectiveness||||||(1.363)|(0.729)|
|Observations|16|13|10|14|7|5|4|
|Wellcome Global Monitor<sup>a</sup>|No|Yes|Yes|No|No|Yes|Yes|
|_I_<sup>2</sup>|86.0|85.6|37.4|67.7|78.3|71.2|72.0|

_Note:_ * _p < .10, ** p < .05. a_ Because of missing data with trust in doctors, perceived vaccine safety, and perceived vaccine effectiveness, for which data from the Wellcome Global Monitor 2018 were not available for Belize, Djibouti, Jamaica, Papua New Guinea, and West Bank and Gaza, we report the results of two study-level moderator meta-regressions for experts and endemic framings: (1) without the Wellcome Global Monitor indicators and all countries and (2) with the Wellcome Global Monitor indicators, dropping countries with missing indicators.

20
|ln VA per capita primary sector|||-83.43<br>(64.48)|||
|---|---|---|---|---|---|
|ln VA per capita primary sector squared|||-5.643|||
||||(4.372)|||
|ln VA per capita primary sector||||25.90||
|||||(230.9)||
|ln VA per capita primary sector squared||||1.842||
|||||(16.74)||
|ln VA per capita primary sector|||||-102.9|
||||||(110.5)|
|ln VA per capita primary sector squared|||||-8.282|
||||||(8.948)|
|Constant|14.60|19.22**|-291.3|108.6|-303.1|
||(9.781)|(9.082)|(237.9)|(796.3)|(341.3)|
|Observations|742|742|742|742|742|
|Number of region|14|14|14|14|14|
|Sargan test|614.0|617.2|612.2|612.6|613.7|
|Source LFSs 2007-2011. Conditional logit fix|ed effect: dep|endant variable|: FLP (1: in th|e labor force;|0: otherwise).|
|Standard errors in parentheses (*** p<0.01, **|p<0.05, * p<0|.1).||||

# **Individual panel short-term model**

In this section, female participation rates regressions are carried out on the pooled sample 20072011 separately for urban and rural areas using a panel logit model with fixed effects and random effects. Table 2 shows the results. The panel observations are of only one year in length from quarter to quarter, i.e. each individual is observed only two times in two consecutive years and in the same quarter of the year. Only 13.7 percent of urban residents and 15.2 percent of rural residents change participation status during the one year they are observed, which means that all other individuals are dropped from the fixed effects model. This is evidently a constraint because many variables change little over a one year period and because we lose information for up to 85% of the sample, which explains why we retained the random effects model. The set of independent variables were tested for multicollinearity and all variables passed VIF and tolerance tests. Hausman tests are provided in the annex.

The first interesting finding is that GDP growth reduces the probability of participation for women in both urban and rural areas and according to both FE and RE estimations. This may comply with the U-shape hypothesis if Morocco found itself on the downward part of the curve, a fact we said is at odds with the overall performance of the country and the trends in the variables that are usually associated with the U-shape hypothesis (structural transformation, female education, fertility rates). This apparently odd result is explained by the fact that growth has been jobless and that employment generation, where it occurred, occurred in male dominated sectors. As already reported, Verme et al. (2014) showed that male participation increased during the period 2007-2011.

Age is clearly hump-shaped in FLP. We also know that the turning point of the hump when FLP starts to decline is around the age of 25, which largely coincides with marriage age. This result is consistent with Verme (2014), which found the female peak of participation in MENA countries 13
control units of analysis as treated households is a conservative way to bound the direction of the main regression results because some of the households that moved in the post-shock period may have in fact lived in non-affected areas. Table 9 summarizes the effects on food and total household consumption per capita and poverty status estimated on this modified sampled.<sup>11</sup> All the findings hold (qualitatively and quantitatively) after the regressions are re-estimated on a sample where migrant households are reclassified as non-migrant units of analysis.

Finally, we exploited a different natural experiment as an additional robustness check. This natural experiment is generated by the severe floods that affected Zambezia, Nampula and Niassa provinces in central and northern Mozambique in early 2015. Weeks of heavy rainfall in December 2014 and January 2015 caused several rivers to swell, flooding several parts of districts in these provinces. The floods inflicted significant damage to crops as well as to public and private infrastructure, including roads, bridges, schools, health centers, telecommunication networks and housing. On January 12, 2015, the Government of Mozambique declared an institutional red alert for the affected areas of the country.

We run the triple difference model specified in Equation 1 to investigate if the negative effects identified for the floods in 2007, Cyclone Jokwe 2008 and the droughts in 2005 are observed again among the population exposed to a natural disaster that occurred a few years after. The empirical model for this event is implemented using data from the IOF survey 2014/15. Given the timing of the 2015 floods, we use as baseline the first wave of the survey (collected between August and October 2014) whereas the third wave (collected between May and July 2015) provides the post shock data point. Given the spatial part of the source of variation used for identification ( _Shock_ x _Cycle_ ), this analysis uses the cross-sectional rather than panel nature of the data.<sup>12</sup> The results, summarized in Table 10, show that the floods in 2015 also led to short-term negative impacts that resemble the direction of the effects identified in the base models. The only exception is a positive point estimate for the effect on consumption per capita although this happened along with an increase in the overall poverty rate. A closer look at the data reveals that the average increase in consumption in the treated districts was largely driven by a large increase in consumption for

> 11 Results from the other outcomes are available from the authors upon request.

> 12 Household fixed effects estimation requires within-household variation of shock intensity over quarters. There is almost no variation of this short variation in the sample used for this analysis since there are only a few households that migrated between affected and non-affected districts in the period from Q1 to Q4.

19
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One of the results we find out of the matching estimation method is that almost all of the estimated ATET are not statistically significantly different from zero. The only exception is the effect of government support on the percentage change of full-time employees estimated by kernel matching based on mahalanobis distance. While this estimation is significant at the 10% level, we note that balancing of covariates in El Salvador’s matched sample was not successful compared to that of the unmatched sample.

Another result of the matching estimation is the sign of the ATET. Although almost all of them are not statistically significantly different from zero, all of them have positive signs except for two: the effect of government support on the total number of temporary workers in matching with mahalanobis distance in El Salvador and on anticipating falling in arrears on outstanding liabilities within next six months in matching with propensity score in Georgia.

To sum up, while the balancing of covariates in Georgia’s matched samples seem better than that of its raw sample, the ATET is not significantly different from zero, neither in Georgia nor in El Salvador. One possible reason for the El Salvador case is the long gap between ES and COV-ES. The latest El Salvador ES was conducting in 2016, referencing the 2015 fiscal year. This may also partly explain the poor performance of balancing of covariates in El Salvador. Another possible reason why we have insignificant coefficients varying is a small sample size. While ES and COV-ES contain 719 and 581 observations in El Salvador and Georgia, respectively, the samples we use for matching estimation consist of only 169 and 235 observations in El Salvador and Georgia, respectively, because of the restrictions mentioned at the beginning of this section.

# **6. Panel Estimation**

This section uses both rounds of the COV-ES implemented in El Salvador and Georgia, along with the baseline ES. Consequently, the sample within each country is different from the samples we used in Section 5. We use the fixed effect panel regression method to help identify the causal link between the government support policies and the outcome variables of interest.

Recall that, in Round-2 data, the firms that received any government support since the onset of the pandemic, whether before Round-1 or after, are counted as having received government support. This effectively divides the firms that enter in our analysis into three sub-groups: (i) those that did not receive any support in either round of the survey, (ii) those that received support 22
Table 9: Sources of Procyclicality

||**Total Expe**|**nditures**|||**Curren**|**t Expenditur**|**e**|
|---|---|---|---|---|---|---|---|
||(1)<br>(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|State GDP / Country GDP|-1.771<br>*<br>-1.697<br>*|<br>-1.488|*<br>-1.876<br>*|<br>-1.387|*<br>-1.492|**<br>-1.103|**<br>-1.845<br>**|
||_[1.059]_<br>_[0.897]_|_[0.872]_|_[0.982]_|_[0.829]_|_[0.654]_|_[0.502]_|_[0.773]_|
|State Debt / State GDP|-1.312<br>**<br>-1.095|-0.687|-1.170<br>*|<br>0.307|0.562|1.087|0.417|
||_[0.535]_<br>_[0.664]_|_[0.703]_|_[0.627]_|_[0.719]_|_[0.856]_|_[0.764]_|_[0.774]_|
|State Current Revenues Procyclicality|0.384<br>**|||0.619|**|||
||_[0.165]_|||_[0.130]_||||
|State Output Volatility|0.283||||0.031|||
||_[0.673]_||||_[0.595]_|||
|State Current Revenue Volatiltiy||3.013||||3.403||
|||_[2.342]_||||_[2.719]_||
|Intergov. Transfers / State Current<br>Rev.|||0.072<br>_[1.395]_||||0.827<br>_[1.374]_|
|Observations|27<br>27|27|27|27|27|27|27|
|R-Squared|0.27<br>0.17|0.22|0.16|0.49|0.10|0.12|0.12|
|**_Addendum: Using State Tax Revenues_**||||||||
|State Tax Revenues Procyclicality|0.263<br>_[0.249_|||0.494<br>_0.214_|***|||
|State Tax Revenue Volatiltiy||3.895|***|||3.387|*|
|||_2.049_||||_1.751_||
|Intergov. Transfers / State Tax Revenues|||0.708||||1.307|
||||_1.304_||||_1.078_|

***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.  OLS estimation. Robust standard errors in brackets.

19
**Table 9: Impact on Business Inputs**

||Owner's<br>Labor<br>Hours|Hours of<br>Consulting <br>Services|Value of<br> Inventories|Made<br>large<br>K purchase|Value of<br>Capital<br> <br>Purchases|Aggregate<br>Outcome<br>Index|
|---|---|---|---|---|---|---|
|**Panel A: New Firms**|||||||
|Unconditional Experimental Impa|cts:||||||
|First‐Follow‐up|13.538***|1.874|349.447***|0.289***|1062.394***|0.380***|
||(1.961)|(2.897)|(122.713)|(0.031)|(127.841)|(0.042)|
|Second Follow‐up|21.699***|14.146***|1868.767***|0.404***|1542.570***|0.672***|
||(1.704)|(2.259)|(349.657)|(0.029)|(142.963)|(0.041)|
|Third Follow‐up|19.526***<br>(1.748)|‐0.016<br>(3.178)|745.583***<br>(182.426)|0.113***<br>(0.030)|183.804*<br>(109.557)|0.303***<br>(0.042)|
|Impacts Conditional on Business i|n Operation||||||
|First‐Follow‐up|5.499***|‐1.326|75.781|0.270***|1236.484***|0.295***|
||(1.960)|(4.368)|(174.286)|(0.040)|(170.615)|(0.048)|
|Second Follow‐up|4.649***|12.694***|1464.506***|0.299***|1586.015***|0.477***|
||(1.697)|(2.632)|(426.952)|(0.036)|(164.168)|(0.044)|
|Third Follow‐up|2.089|‐22.658***|353.372|‐0.050|‐5.410|‐0.069|
||(1.843)|(4.895)|(240.923)|(0.040)|(160.420)|(0.050)|
|Control Mean: First follow‐up|24.949|7.460|721.444|0.211|345.219|‐0.139|
|Control Mean: Second follow‐up|23.928|3.636|925.474|0.206|252.411|‐0.240|
|Control Mean: Third follow‐up|19.471|17.927|601.747|0.187|246.931|‐0.099|
|Obs: First follow‐up|993|994|991|995|991|995|
|Obs: Second follow‐up|1071|1071|1013|1071|1013|1071|
|Obs: Third follow‐up|927|818|872|927|872|927|
|**Panel B: Existing Firms**|||||||
|Unconditional Experimental Impa|cts:||||||
|First‐Follow‐up|9.058***|7.564|729.308***|0.369***|1356.056***|0.425***|
||(2.653)|(5.930)|(267.946)|(0.046)|(184.612)|(0.054)|
|Second Follow‐up|8.643***|6.117**|1319.844**|0.242***|1018.391***|0.340***|
||(2.552)|(2.901)|(578.671)|(0.045)|(202.343)|(0.051)|
|Third Follow‐up|11.687***|1.767|1029.584**|0.152***|306.290|0.236***|
||(2.492)|(4.449)|(440.225)|(0.048)|(304.020)|(0.058)|
|Impacts Conditional on Business i|n Operation||||||
|First‐Follow‐up|4.819**|6.649|639.108**|0.349***|1350.441***|0.376***|
||(2.428)|(6.346)|(288.353)|(0.048)|(195.908)|(0.052)|
|Second Follow‐up|1.717|4.928|982.279|0.175***|961.396***|0.234***|
||(2.438)|(3.265)|(665.083)|(0.048)|(222.787)|(0.051)|
|Third Follow‐up|2.012|‐4.411|695.914|0.060|145.969|0.060|
||(2.310)|(5.612)|(516.818)|(0.055)|(374.417)|(0.063)|
|Control Mean: First follow‐up|43.263|10.521|1222.716|0.358|536.734|‐0.234|
|Control Mean: Second follow‐up|40.869|7.941|2226.204|0.434|595.510|‐0.183|
|Control Mean: Third follow‐up|31.589|15.457|1360.244|0.307|552.070|‐0.125|
|Obs: First follow‐up|423|422|422|423|423|423|
|Obs: Second follow‐up|458|458|453|458|453|458|
|Obs: Third follow‐up|409|335|400|409|400|409|

Notes: Control means shown are unconditional means Aggregate outcome index is the average of standardized z‐scores of the other columns. Robust standard errors in parentheses, *, **, *** indicate significance at the 10, 5, and 1 percent levels. Experimental estimates are ITT estimates and control for randomization strata.

46
WPS4203

# **Latin Americans of Japanese Origin (** **_Nikkeijin_ )**

# **Working in Japan – A Survey**

## **Junichi Goto**

## **Kobe University**

**Research Institute for Economics and Business Administration Kobe University Rokkodai-cho Nada-ku, Kobe 657-8501, Japan (phone) 81-70-803-7000 (fax) 81-78-803-7059 - (e-mail) jgoto@rieb.kobe** **<u>u.ac.jp</u>**

### World Bank Policy Research Working Paper 4203, April 2007

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._
- 53 -

Table 8: Health Benefits of Redui Particute ater to California Standard

|Healti Effect|Low|Medium|High|
|---|---|---|---|
|Premartre Mortality|1,100|1,700|2.40D|
|Hospital Admissions|1,700|3,100|4.100|
|Emergency Room Visits|33.600|61.700|89.800|
|Restricted Activity Days|6.762,000|9.571,000|14,949,000|
|Lower Respirawory IllneSS (children)|74,800|158,000|222,600|
|Asdhma Attacks|353,000|_705.000_|5,900.000|
|Respiratory Symnptos|23,844,000|47.165,000|71.533,000|
|Chronic Bronchitis|8,000|16,000|24.000-|

Table 9: Health Benefits of Reducing Ambient Lead

||Standar|d||
|---|---|---|---|
|Effect|Indonesian|WHO|90 Percent<br>ReducEion|
|Hypertension|62,350|135,660|241,000|
|Non-faal Heart Atacks|68|190|559|
|Premanre Mortality|57|158|461|
|IQ Loss (points)|1.091,860|2,073,205|3.130,918|

Table 10: Health Benefits of Reducing Ambient Nitroen Dioidde to Indonesian Standard <!-- Start of picture text --> Low Medium High<br>Respiratory Symptoms (millions) 1.04 1.77 2.50<br><!-- End of picture text -->