rafmacalaba/datause-encoder-data huggingface dataset:
# **Table S8b: Reduced forms for Table S8a**

||Base<br>estimation<br>(Column 2,<br>Table 3)|Excluding<br>nations with<br>fewer than<br>100 data<br>points|Excluding<br>nations with<br>fewer than<br>1000 data<br>points|<br>Excluding<br>nations with<br>fewer than<br>2000 data<br>points|<br>Examining<br>long-term<br>OECD<br>nations only|Excluding<br>exporters<br>with <100k<br>ethnic US<br>members|Excluding<br>largest<br>foreign<br>patenting<br>industry|Excluding 5<br>largest<br>foreign<br>patenting<br>industries|<br>Excluding<br>largest<br>foreign<br>productivity<br>outlier|<br>Excluding 5<br>largest<br>foreign<br>productivity<br>outliers|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|||The depend<br>Panel A|ent variable i<br>: Weighting|s Δ log bilate<br>bilateral route|ral exports on<br>s by the intera|the intensive<br>ction of expo|margin by e<br>rter and imp|xporter-impor<br>orter trade in|ter-industry<br>industry||
|Δ Log estimator for tech|0.938***|0.937***|0.936***|0.904***|0.622***|0.975***|0.898***|0.716***|0.948***|0.747***|
|flows from the United States|(0.298)|(0.298)|(0.308)|(0.341)|(0.239)|(0.352)|(0.175)|(0.249)|(0.302)|(0.274)|
|Observations|103,839|103,013|89,059|71,779|74,087|78,411|97,011|73,518|100,740|82,939|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|||||Pan|el B: Excludin|g sample wei|ghts||||
|Δ Log estimator for tech|0.648***|0.653***|0.668***|0.634***|0.580***|0.601***|0.657***|0.529***|0.655***|0.619***|
|flows from the United States|(0.112)|(0.113)|(0.120)|(0.135)|(0.124)|(0.124)|(0.115)|(0.124)|(0.116)|(0.126)|
|Observations|103,839|103,013|89,059|71,779|74,087|78,411|97,011|73,647|100,740|82,939|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 3.
**Figure 10:** Responses of the _CA_ to one standard deviation shocks for **Eurozone periphery [sample 1975-2011]**

<!-- Start of picture text --> R shock REER shock GR shock YGAP shock<br>0 .3<br>0.5<br>-0.2 -0 .3<br>-0.5 -0.7 -0.7 -0.8<br>1 5 1 5 1 5 1 5<br><!-- End of picture text --> _Note._ The y-axis measures deviations from the baseline, while the x-axis represents years after shock.

**Table 2:** Forecast error GVD **Eurozone core [1975-2011]** : percent of variation in _CA_ explained by column variable

||Ba|seline VA|R|||
|---|---|---|---|---|---|
|Years ahead|_R_|_REER_|_GR_|_YGAP_|_CA_|
|2|0.86|1.14|3.61|3.51|90.88|
|4|1.05|1.15|3.68|3.51|90.60|

# Alternative VAR _<u>(replacing REER by ULC)</u>_

|Years ahead|_R_|_ULC_|_GR_|_YGAP_|_CA_|
|---|---|---|---|---|---|
|2|0.64|4.10|3.25|2.55|89.45|
|4|0.81|4.18|3.51|2.59|88.92|

**Table 3:** Forecast error GVD for **Eurozone periphery [1975-2011]** : percent of variation in _CA_ explained by column variable

||B|aseline V|AR|||
|---|---|---|---|---|---|
|Years ahead|_R_|_REER_|_GR_|_YGAP_|_CA_|
|2|3.36|4.07|10.34|1.41|80.82|
|4|3.35|4.17|10.71|1.72|80.05|

# Alternative VAR _<u>(replacing REER by ULC)</u>_

|Years ahead|_R_|_ULC_|_GR_|_YGAP_|_CA_|
|---|---|---|---|---|---|
|2|3.55|0.03|8.79|2.68|84.94|
|4|3.55|0.05|9.11|3.28|84.00|

18
# **Table A-1 (continued)**

# **<u>(iii) Guatemala</u>**

|Variable label|White Men|Indigenous|Men<br>White Women|Indigenous Women|
|---|---|---|---|---|
|Primary|0.40**|0.26**|0.31|0.22|
||(0.07)|(0.07)|(0.17)|(0.13)|
|Secondary|0.53**|0.58**|0.39|0.45|
||(0.10)|(0.12)|(0.20)|(0.23)|
|Tertiary|1.58**|0.96**|0.39|1.62*|
||(0.26)|(0.20)|(0.46)|(0.82)|
|Mother’s years of schooling|0.06**|0.05|0.02|0.05|
||(0.01)|(0.03)|(0.02)|(0.03)|
|Age|0.04**|0.04**|0.08**|0.05**|
||(0.01)|(0.01)|(0.03)|(0.02)|
|Age^2|-0.001**|-0.001**|-0.001**|-0.001**|
||(0.00)|(0.00)|(0.00)|(0.00)|
|Formal sector wage employee|1.23**|1.66**|1.93**|1.86**|
||(0.08)|(0.08)|(0.14)|(0.15)|
|Informal sector wage employee|0.98**|1.07**|1.18**|0.76**|
||(0.07)|(0.07)|(0.13)|(0.10)|
|Public employee|1.36**|1.97**|2.24**|1.41**|
||(0.10)|(0.15)|(0.19)|(0.41)|
|Guatemala City|-0.19*|-0.08|0.43*|0.28|
||(0.10)|(0.33)|(0.17)|(0.20)|
|Rural|-0.45**|-0.36**|-0.29|-0.54**|
||(0.06)|(0.08)|(0.17)|(0.12)|
|Constant|4.82**|4.60**|3.40**|4.18**|
||(0.21)|(0.30)|(0.65)|(0.40)|
|Number of Observations|2795|1990|1363|906|
|Adjusted R-squared|0.41|0.35|0.30|0.21|

Standard errors in parentheses; ** significant at 99% level; * significant at 95% level 35
# **Supplementary References**

1. BGS and DPHE. Arsenic Contamination of Groundwater in Bangladesh, Vol. 2. Final Report  WC/00/19. Keyworth: British Geological Survey (BGS) and Bangladesh Department of Public Health Engineering (DPHE), 2001.

2. Gaus I, Kinniburgh DG, Talbot JC, Webster R. Geostatistical analysis of arsenic concentration in groundwater in Bangladesh using disjunctive kriging. _Environ Geol_ 2003; **44** : 939-48.

3. van Geen A, Zheng Y, Versteeg R, et al. Spatial variability of arsenic in 6000 tube wells in a 25 km<sup>2</sup> area of Bangladesh. _Wat Resour Res_ 2003; **39** (5): 1140-55.

4. Shamsudduha M. Spatial Variability and Prediction Modeling of Groundwater Arsenic Distributions in the Shallowest Alluvial Aquifers in Bangladesh. _J Spat Hydro_ 2007; **7** (2): 33-46.

5. Yu WH, Harvey CM, Harvey CF. Arsenic in groundwater in Bangladesh: A geostatistical and epidemiological framework for evaluating health effects and potential remedies. _Wat Resour Res_ 2003; **39** : 1146.

6. Dhar RK, Zheng Y, Stute M, et al. Temporal variability of groundwater chemistry in shallow and deep aquifers of Araihazar, Bangladesh. _J Contam Hydrol_ 2008; **99** (1-4): 97-111.

7. R Core Team. _R_ : A language and environment for statistical computing (R version 3.4.3). R Foundation for Statistical Computing, Vienna, Austria. URL <u>http://www.R project.org/. 2017.</u> 8. Zahid A, Jahan K, Ali MH, Ahmed N, Islam MK, Rahman A. Distribution of Groundwater Salinity and Its Seasonal Variability in the Coastal Aquifers of Bengal Delta. In: Zahid A, Hassan MQ, Islam R, Samad QA, Khan MS, Haque R, eds. Impact of Climate Change on Socio-economic Conditions of Bangladesh. Dhaka: Alumni Association of German Universities in Bangladesh, German Academic Exchange Service (DAAD); 2013: 170-93.

9. MPO. The groundwater resources and its availability for development, Technical report no. 5, Master Plan Organization (MPO), Ministry of Water Resources, GoB. Dhaka: Harza Engineering USA in association with Sir MacDonald and Partners, UK, Met Consultant, USA and EPC Ltd., 1987.

10. Rahman AA, Ravenscroft P, editors. Groundwater resources and development in Bangladesh. Dhaka: The University Press Limited; 2003.

11. BWDB. Hydrogeological study and mathematical modelling to identify sites for installation of observation well nest. Dhaka: Bangladesh Water Development Board (BWDB) and Institute of Water Modelling (IWM), 2013.

12. Baumann E. Handpump technologies: Low cost hand pumps, Field Note No 2011-3. Gallen, Switzerland: The Rural Water supply Network (RWSN), Skat Foundation, 2011.

30
- 24 -

**Table 5:  Fixed Effects Estimation**

**Dependent Variable:  AR(EARLY) Sample:  1993Q1 – 2003Q3; Cross-Sections Included:  24 Total Panel Observations (Unbalanced):  693** **<u>R</u>**<sup>**2**</sup> **= 0.7968; Adj. R**<sup>**2**</sup> **= 0.7854; F-Statistic = 69.4348 ; P-Value(F-Statistic) = 0.0000**

|**Variable**|**Coefficient**|**Std. Error**|**t-Statistic**|**p-value**|
|---|---|---|---|---|
|C|3.0257|0.1803|16.7840|0.0000<sup>***</sup>|
|RF|0.3652|0.0166|21.9809|0.0000<sup>***</sup>|
|SOFI|0.0029|0.0010|2.9696|0.0031<sup>***</sup>|
|SOA|0.0083|0.0031|2.6801|0.0075<sup>***</sup>|
|LEV|0.0115|0.0046|2.5309|0.0116<sup>**</sup>|
|MIS|-0.0208|0.0548|-0.3800|0.7041|
|MSHARE|-3.7609|0.7187|-5.2331|0.0000<sup>***</sup>|
|AP(EARLY)|0.0002|2.46 *<br>5<br>−<br>_e_|7.2057|0.0000<sup>***</sup>|
|CR|-0.0437|0.0074|-5.9007|0.0000<sup>***</sup>|
|HER(EARLY)|-4.6728|0.9512|-4.9128|0.0000<sup>***</sup>|
|TREND|0.0002|0.0019|0.0997|0.9206|
|D|-1.2189|0.2011|-6.0609|0.0000<sup>***</sup>|
|RF*D|0.4163|0.0382|10.8874|0.0000<sup>***</sup>|
|AP(EARLY)*D|-0.0001|3.39 *<br>5<br>−<br>_e_|-4.0003|0.0001<sup>***</sup>|
|CR*D|-0.0884|0.0264|-3.3444|0.0009<sup>***</sup>|

*** = significant at the 1% level;** = significant at the 5% level;* = significant at the 10% level

|**Table 6:**<br>**S**<br>**R**<sup>**2**</sup> **= 0.7968; A**<br>|**Fixed Effects E**<br>**Depende**<br>**ample: 1993Q1 –**<br>**Total Panel O**<br>**dj. R**<sup>**2**</sup> **= 0.7854; F**<br>|**stimation, with**<br>**nt Variable: AR(**<br>**2003Q3; Cross-Se**<br>**bservations (Unba**<br>**-Statistic= 69.434**<br>|**Robust Standard Er**<br>**EARLY)**<br>**ctions Included: 24**<br>**lanced): 693**<br>**8 ; P-Value(F-Statisti**<br>|**rors**<br>**c)= 0.0000**<br>|
|---|---|---|---|---|
|**Variable**|**Coefficient**|**Std. Error**|**t-Statistic**|**p-value**|
|C|3.0257|0.2270|13.3270|0.0000<sup>***</sup>|
|RF|0.3652|0.0217|16.8099|0.0000<sup>***</sup>|
|SOFI|0.0029|0.0009|3.1631|0.0016<sup>***</sup>|
|SOA|0.0083|0.0046|1.7966|0.0729<sup>*</sup>|
|LEV|0.0115|0.0040|2.9086|0.0038<sup>***</sup>|
|MIS|-0.0208|0.0489|-0.4260|0.6703|
|MSHARE|-3.7609|0.6535|-5.7550|0.0000<sup>***</sup>|
|AP(EARLY)|0.0002|2.79 *<br>5<br>−<br>_e_|6.3559|0.0000<sup>***</sup>|
|CR|-0.0437|0.0180|-2.4237|0.0156<sup>**</sup>|
|HER(EARLY)|-4.6728|0.7214|-6.4778|0.0000<sup>***</sup>|
|TREND|0.0002|0.0021|0.0900|0.9283|
|D|-1.2189|0.2703|-4.5093|0.0000<sup>***</sup>|
|RF*D|0.4163|0.0491|8.4727|0.0000<sup>***</sup>|
|AP(EARLY)*D|-0.0001|5.53 *<br>5<br>−<br>_e_|-2.4490|0.0146<sup>**</sup>|
|CR*D|-0.0884|0.0410|-2.1565|0.0314<sup>**</sup>|

*** = significant at the 1% level;** = significant at the 5% level;* = significant at the 10% level
the conditions which specifically favor insurgency. Since insurgents are usually numerically weak compared to the governments they are fighting against, they must be able to hide from government forces and be able to garner local support for their activities. This suggests a number of testable hypotheses: first, the presence of mountainous or forested terrain, poorly served by roads, should increase insurgency. Second, insurgency should be more common in rural areas with low migration, where the rebels can easily punish people who do not support them. Third, the need to form a cohesive fighting force implies that insurgencies should be less common in areas of greater linguistic diversity and fewer means of communication.

Several cross-country studies have suggested that diverse societies are more prone to conflict (Easterly and Levine, 1997; Horowitz, 1985). Collier and Hoeffler (2004) also lay out a “grievance” framework, where the presence of different ethnic groups is likely to lead to the presence of significant animosity between groups; this is especially likely when one ethnic group tends to dominate over the others. Lee et al. (2004) find that the impact of democratization on human rights is limited by the presence of ethnic minorities. Several studies including Alesina, Baqir and Easterly (1999) and Miguel and Gugerty (2005) have also documented the inability of diverse societies to work together to obtain public goods.

Nepal has a very diverse society in several dimensions. Although the majority of the population is Hindu, there are deep caste divisions in the Nepalese society. Discrimination and human rights abuses against the lower castes are not uncommon (Human Rights Watch, 2004). Hence, in the Nepalese context, the grievance hypothesis suggests that areas with greater caste diversity or a greater dominance of high castes should have more conflict. Nepal also has considerable linguistic diversity, with only 60% of the population speaking Nepali. While other empirical analyses of the Nepal conflict (Bohara et al, 2006; Acharya, 2007) focus on the proportion of people who speak Nepali as a measure of social diversity, they do not construct polarization or fractionalization measures similar to the cross-country studies of civil war.<sup>8</sup> These prior studies also do not examine the role of caste divisions at all.

8Duclos, Esteban and Ray (2004) discuss the concept, measurement and estimation of polarization in detail.

8
Note that the commonly available household surveys in developing countries include a random sample of parents (household head and spouse), and thus the estimate of the standard deviation of parental schooling is likely to be unbiased. We can put together the relations in inequality (4), and equations (8) and (9) to derive the following approximate relation: Now observe that _√δ > δ_ , because _δ ∈_ (0 _,_ 1), and as a result, the bias represented by the right hand side of approximation (10) is much smaller than the bias in approximation (4). To give a sense of the magnitudes, _δ_ = 0 _._ 9 implies a value of _√δ_ = 0 _._ 949 , and _δ_ = 0 _._ 8 implies _√δ_ = 0 _._ 90. Thus the IGC estimates from coresident sample suffer from much less bias when compared to the most widely used measure of intergenerational persistence: IGRC. If the bias in IGRC is 10 percent, the corresponding bias in IGC is half of that (5 percent), and when the IGRC estimate is biased downward by 20 percent, the corresponding bias in IGC is about 10 percent. An important implication of the above results is that, at high levels of coresidency rates ( _δ_ closer to 1), the difference between the biases in IGRC and IGC will be smaller, which is consistent with the evidence that in India the differences in biases between IGRC and IGC is much smaller when compared to that in Bangladesh. The actual biases estimated in the data, however, also reflect sampling variability.<sup>25</sup> From relations (4) and (10) above, we get the following approximate results on the slope of the bias in IGRC and IGC estimate with respect to the coresidency rate: The results in equation (11) above are important for cross-country and over time (for 25Our evidence that in many cases, the bias in IGC is less than a third of the corresponding bias in IGRC implies that these approximate relations underestimate the advantages of IGC as a measure of mobility. However, we believe that this is useful in providing an intuitive understanding of the empirical results.

26
|4.|Modernization of existing workshop and|7.00|
|---|---|---|
||laboratory of BMD||
|5.|Development of existing Training Institute|3.00|
||facilities of BMD||
|**Total**||**30.00**|

# **Operational & Maintenance cost:**

||**Items**|**Estimated Cost in Million**<br>**US$ per year**|
|---|---|---|
|1.|Operational cost of existing 3+additional 5 RS<br>observatories for per year|2.00|
|2.|Operational cost and maintenance cost of|3.00|
||existing 35+additional 30 observatories for per||
||year||
||**Total**|**5.00**|

The current analysis has identified 19 coastal districts as vulnerable to accentuated storm surge related inundation. The Red Crescent Society has recommended an annual awareness promotion program of Tk 10 million (= US$ 142, 857) per district along with a Tk 20 million (= US $ 285,714) overhead cost for the Center, summing to a total cost of US$ 3 million.

# _On further Infrastructural Investment:_

As strong structural protection measures, such as polders, are being suggested in this analysis, it reduces the need for making infrastructure (roads, bridges etc.) resilient to storm surge related inundation, except in regions where there will not be any full‐scale structural protection, for example in islands which are not and will not be protected by polders. However, it should be noted that in these unprotected islands, there is not much infrastructure to protect.

To sum up, Table 7 presents the itemized estimated cost of adaptation for storm surge induced inundation in Bangladesh. The estimates indicate that an adaptation investment up to $2.462 billion by 2050 would be needed to implement the measures discussed, even without climate change. The estimate of the additional investment necessary to cope with
45 performance standards must be made conditional on serving the needs of a complete spectrum of displaced workers. F Not all disolaced workers require adult training services. The program evaluation studies summarized In Section E indicate that relatively expensive skill training programs are typically not cost effective. Indeed, more favorable benefit-cost evidence Is reported for much less costly job search assistance services. As noted earlier, these studles have primarily been carried out for U.S. displaced worker programs which are typically less permanent and less well funded than those found in many other Industrialized natlons. In additlon, the authors of the U.S. studies raise a number of caveats about their lindings that make them very cautious In drawing policy recommendations regarding skill training. At the same time, however, the limited evidence summarized for skill training In Canada, Sweden, and Australia also fails to provide clear evidence that these programs substantially enhance employability and earnings.

It seems pruden **t** o conclude fom the available empirical evidence that, particularly for skill training provided in a classroom setting, retraining should be limited to carefully screened displaced workers whose specitic needs can be adequately matched wkh local training resources. Indeed, the one criticism that has been levied against the comprehensive Swedish training system is that it enrolls unemployed Individuals for whom retraining Is unlikely to improve their labor market opportunitles. A germanent. instutionalized trainina is to be Dreferred to short-term Intervention. A final lesson closely related to the aforementioned lesson concems the evidence that short-term intervention as typified by the U.S. displaced worker demonstration projects has seldom been found to be very effective in improving the employability and long-run eamings prospects of displaced workers. The more hopeful approach is a training system that is permanent and institutionalized. As noted, an example of such a training system is the Swedish AMU. Training programs in Australia, Britain, Canada, and the U.S. have undergone considerable recent change designed to make them flexible in responding to shifts In labor dernand and to the needs of displaced workers. But unlike the Swedish model, programs In all four countries make use of extemal training providers to supply retraining serices. The effectiveness ofthese programs remains to be established.
Equation (1) includes interaction between −50) and P P P to allow the jj jj different slopes of the outcome equation of the treatment and control group. The control (RRPPR PP variables, which are denoted by , include both individual-level and household-level ii,jj,tt variables. We include the district fixed effects XX and the year fixed effects to capture jj tt the unobservables that commonly affect districts and years as a whole. The household-level uu YYRRP (or individual-level) error terms are denoted by . ii,jj,tt εε We examine the impact of the program on a comprehensive set of outcome variables encompassing both individual-level and household-level variables in different areas ranging from employment, education, health and poverty to household access to credits and services. The lists of these outcome variables are shown in Table 1 and Table A.2 in the Appendix. Regarding control variables, they should not be influenced by the treatment variable under investigation (Heckman and Vytlacil, 2007; Angrist and Pischke, 2008). As the program may impact various outcomes of individuals, we control for a limited set of exogenous variables including age, gender, and ethnicity. Additionally, we conduct robustness checks by including additional control variables such as households’ educational levels and demographic characteristics. As will be shown in the empirical results section, the inclusion of additional variables does not alter the results, thereby affirming the robustness of our findings. For interpretation, we primarily rely on the results derived from the parsimonious model specifications.

The RDD model relies on the crucial assumption of continuity in the running variable around the cutoff point. The 30A Program, as per Resolution 30a/2008/NQ-CP by the Government of Viet Nam, was approved in December 2008, with the 2006 poverty rate serving as the cutoff variable. Thus, districts were unlikely to manipulate the poverty rate to be selected into the program. Nonetheless, it is useful to conduct a density test on the data 13
are, the proportions rise to 96.35% for high-income countries and to 78.11% for lowincome countries (Table 3). The proportions based on GDP per capita are very similar to the ones based on the number of countries (Table 3).

Among the middle and low-income countries, the occurrence of EDIS seems to be higher in Europe and Central Asia (74.07%) and Latin America and Caribbean (66.67%), whereas it is the lowest in Sub-Saharan Africa (10.87%) (Table 3). The occurrence rates go up to approximately 98% for both European and Central Asian, and Latin American and Caribbean countries when proportions are based on GDP.

The United States is the first in history to adopt an EDIS which dates back to 1934 – a year marked by a banking crisis.<sup>5</sup> As shown in Figure 4, this was followed in 1960s by nine other countries and the trend has been dramatically upward especially since 1980s reaching a total of 88 countries in 2003 which is a quadruple of the 1984 figure. In 1994, deposit insurance became the standard for the newly created single banking market of the European Union (EU). Until 1990s the EDISs mostly prevailed and kept building in high income countries but since 1995 we have observed a surge to EDISs in especially lower middle income countries (Figure 4). This is partly driven by the Eastern and Central European transition economies which eventually became or are expected to become EU members although EDISs remain quite prevalent in Latin America and Caribbean as well, thanks to the generally accepted best practice advice given to the developing countries (Folkerts-Landau and Lindgren 1998, and Garcia 1999).

# **3. The database**

The database builds on Demirgüç-Kunt and Sobaci (2001) as mentioned in the introduction. A large section of their database was constructed by the survey results of an International Monetary Fund working paper (Garcia 1999) and earlier sources such as Kyei (1995) and Talley and Mas (1990) augmented by some other country sources. We further complement and improve the database through various other country and online

> 4 There is no data available for Andorra, Monaco, San Marino, and Vatican City so they are not included in the dataset.

> 5 In Norway there was a guarantee fund for savings banks with voluntary membership in 1921 which became obligatory in 1924, whereas a guarantee fund for commercial banks was first introduced in 1938 (Gerdrup 2003). However, Norway’s guarantee fund is not considered a pure deposit insurance scheme so they had no official explicit deposit insurance until 1961.

4