rafmacalaba/datause-encoder-data huggingface dataset:
# <u>Table 7. Schooling, skills, migration, and earnings for men in Pakistan</u>

|||**A**|**ll**|||**Worki**|**ng men**||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||Monthly<br>earnings|Monthly<br>earnings|Monthly<br>earnings|Monthly<br>earnings|Monthly<br>earnings|Monthly<br>earnings|Monthly<br>earnings|Monthly<br>earnings|
|Years of schooling (a1)|4.12***|3.37***|1.10*|0.063|2.89***|2.76***|1.97***|2.09***|
||(0.50)|(0.46)|(0.60)|(0.84)|(0.62)|(0.60)|(0.57)|(0.61)|
|Cognitive skills (a2)|||8.31***|0.26|9.50***|3.47*|7.71***|3.31*|
||||(2.77)|(3.59)|(2.35)|(1.78)|(2.04)|(1.94)|
|SEM skills (a3)|||16.5***|14.3***|10.3***|6.00***|11.6***|7.88***|
||||(1.86)|(2.71)|(1.98)|(2.02)|(1.81)|(1.64)|
|Out Village (a4)||||32.9**||35.4***||33.5***|
|||||(13.2)||(13.4)||(12.2)|
|Interaction YrsSchooling and Out Village (b1)||||4.78***||2.36*||1.97|
|||||(1.44)||(1.42)||(1.33)|
|Interaction Cog and Out Village (b2)||||13.6**||11.6**||10.3*|
|||||(6.35)||(5.45)||(6.04)|
|Interaction SEM and Out Village (b3)||||-7.37||-4.49||-4.02|
|||||(5.17)||(4.69)||(4.16)|
|Brain index (job content)|||||||9.20***<br>(2.21)|<br>6.95***<br>(2.12)|
|Brawn index (job content)|||||||-7.32***<br>(1.81)|-5.45***<br>(1.86)|
|Team work (job content)|||||||23.1***|11.6***|
|Observations|2340|1978|1978|1978|1636|1636|1636|1636|
|Pseudo R-squared|0.034|0.030|0.043|0.092|0.055|0.090|0.068|0.096|
|Median Dependent|120.19|115.38<br>|115.38<br>|115.38<br>|134.62|134.62|134.62|134.62|
|||Has skills|Has skills|Has skills|||||
|Sample|All|measures|measures|measures|All|All|All|All|
|District FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|a1 + b1=0||||4.84***||5.12***||4.06***|
|||||(1.24)||(1.29)||(1.24)|
|a2 + b2=0||||<br>13.89***||<br>15.07***||<br>13.62**|
|||||(5.33)||(5.17)||(5.78)|
|a3 + b3=0||||6.89||1.51||3.86|
|||||(4.45)||(4.27)||(3.90)|

* p<.10 ** p<.05 *** p<.01

48
year of the Helen Keller survey and the IFPRI survey which were both collected in 2011, we observe large differences in the number of children classified as adequate in food. Based on the IFPRI survey 33% of the rural children in Bangladesh had access to adequate food but only 14% of both rural and urban children were found to be adequate in food in the Helen Keller survey. Besides sampling a different population, the two surveys lend themselves to different definitions of adequate food. In Helen Keller it is not possible to determine whether a child has met the minimum acceptable diet based on the composition and frequency of meals, but it possible to determine whether the household has experienced food insecurity. In the IFPRI sample it is possible to determine the minimum acceptable diet, but not whether the household experienced food insecurity.

<!-- Start of picture text --> Figure 5b: The Evolution of Access to Adequate Food Security<br>Prevalence of Adequacy in Food<br>68<br>57<br>51<br>46<br>43<br>34 34<br>31<br>29 29 28<br>22<br>14 15<br>12 12<br>BGD, HK BOL IDN NPL ZWE<br>BGD, IFPRI ETH KHM PER<br>Year 1 Year 2<br>Source: Author estimates. Data for Bangladesh from 2010 (HK) and 2011 (HK, IFPRI); Bolivia 2003, 2008; Cambodia 2005, 2010;<br>Ethiopia 2000, 2011; Indonesia 2010; Nepal 2000, 2011; Peru 2005, 2012; Zimbabwe 2005, 2010.<br>Note: (1) Bangladesh (HK) includes food security but no information on meal frequencies.<br>(2) Bolivia (2003) does not include information on dietary diversity.<br>(3) Indonesia (RKD) only has information on household level dietery diversity, and no information on meal frequencies.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text --> Children in resource‐poor households are less likely to have access to adequate food security than children in resource‐rich households, but in general the differences are small. Although in all the countries analyzed children in resource‐poor households were less likely to have access to adequate food, there were differences in the degree of disparity (Figure 5c).<sup>13</sup> The largest disparity

> 13 We use the most recent year of data for the comparisons between resource‐poor and resource‐rich households.  The only exception is the Helen Keller data for Bangladesh where we use 2010 and not 2011, the reason being that no vaccination information was available for 2011.

24
<!-- Start of picture text --> Average Prices, Cigarette<br>(Deceml,er 1991 -- August 1992)<br>40.. .... ................................. ............................................... ..............<br>35. .. ............ ..................... ............... ......................... ... ................... ............ ..../<br>45~~~~~~44<br>.................. .. ............. .. .. . ....... ... >.:<br>.---, ........ ..;;;; .... ;..................................................<br>Dec9l 01/2 02J11 02125 03J31 04/14 04M2 OW/2 06102 0616' 0630 07/14 o7r2B owl moa2<br>Date<br>| Market Price ..... State Price<br><!-- End of picture text -->

_65_
place where he or she is surveyed. The DHS only has data on the date at which individuals _interviewed individually_ moved to their current place of residence. This information is not available for household members not eligible for an individual interview, and so it is not applicable to the sample used in the education analysis. In the next section, I explain how I can nonetheless check the robustness of my education findings based on DHS data to conflict-related migration.

The NLSS data on the individual’s district of origin before they last migrated is particularly useful in the marriage analysis, since in Nepal, as in most of South Asia, marriage is patrilocal. Marriage is therefore often accompanied by migration, not uncommonly to a different district, and so this may create both classical and non-classical measurement error in the district-level conflict measures used here. In particular, if when conflict was very intense, parents used marriage as a way of putting their daughters to safety by marrying them to men from more peaceful districts, this may create an artificially positive correlation between being married and being observed in a low-conflict intensity district. Therefore, when analyzing the impact of conflict on marriage, I present estimation results using data from both the DHS and the NLSS.<sup>1</sup> The data used to measure conflict intensity are taken from electronic files provided by the Informal Sector Service Center (INSEC), an independent, well-regarded, human rights NGO based in Kathmandu and with representatives in each of the 75 Nepalese districts, who monitor human rights violations. Data from INSEC has been extensively used in the media, international agencies and government reports, and in a number of academic studies, including Bohara et al. (2006) and Do and Iyer (2010). The INSEC data files contain the number of conflict-related deaths per month per district of Nepal between February 1996 and December 2006, as well as the total number of school destructions and abductions by Maoists at the district level, which are used to construct most measures of exposure to conflict used in this paper.

I also use two indicators of Maoist control over the district by 2003, based on classifications reported by Hattlebak (2007). I consider in turn two alternative definitions of Maoist control. I first categorize as under Maoist control any district that is categorized as such by both Maoists and the government (Definition 1). I then apply what Hattlebak (2007) considers the more reliable classification of the two, namely the government classification (Definition 2). The definition of all the conflict exposure variables used in this paper can be found in Table 1.

> 1 NLSS Data was provided by the Nepalese Central Bureau of Statistics (CBS).

12
# **Table A10. Impact of living in the GMA and, once in the Luangwa Park System GMA, of -** **<u>participating in the VAG/CRB on the non poor households</u>**

||Luangwa|Treatment Regres<br>capita co|sion mod<br>nsumptio|els for the impact<br>nexpenditure of|on per|
|---|---|---|---|---|---|
|||||Participating|in|
|Variable|Variable description|LivinginGM|A<sup>_a_</sup>|<br>VAG/CRB|<br><sup>_b_</sup>|
|||(1)||(2)||
|Hage|Age of the household head|-0.003<br>||0.000<br>||
|||(0.002)||(0.004)||
|fhhh|Female-headed dummy|-0.134||-0.127||
|||(0.086)||(0.101)||
|maxedu|Education of the most educated member|0.060|***|0.063|***|
||(years)|(0.011)||(0.012)||
|c0to14|Number of children less than 15 years old|-0.133<br>(0.018)|***|-0.126<br>(0.023)|***|
|f15to60|Number of female members 15-60 years|-0.099<br>(0.043)|**|-0.161<br>(0.064)|**|
|m15to60|Number of male members 15-60 years|-0.051<br>(0.040)||-0.039<br>(0.053)||
|m61plus|Number of members older than 60 years|-0.157<br>(0.069)|**|-0.181<br>(0.105)|*|
|kroad|Distance to the nearest main road in km|-0.007<br>(0.002)|***|-0.008<br>(0.002)|***|
|kbsch|Distance to the nearest basic school in km|0.024<br>(0.010)|**|0.027<br>(0.008)|***|
|kheal|Distance to the nearest health centre in km|-0.001<br>(0.004)||-0.004<br>(0.003)||
|vacast|Value of consumer durable assets in ZMK|0.085<br>(0.012)|***|0.086<br>(0.021)|***|
|dcoop|Participation in cooperatives dummy<br>|0.283<br>(0.068)|***|0.212<br>(0.110)|*|
|gma or|Participation dummy variable<sup>_a,b_</sup>|0.744|***|0.530|***|
|dvcrb||(0.164)||(0.197)||
|cons|Intercept|13.282|***|13.738|***|
|||(0.174)||(0.162)||
|Number of|observations|544||336||
|Log-likelih|ood value|-777||-432||
|Goodness|of fit Chi-Square|.||.||
|Rho||-0.39|***|-0.42|**|

> _a_ Participation dummy variable refers to the GMA dummy, equal to 1 if the household is located in a GMA

> _b_ The participation dummy variable refers to the CRB/VAG dummy, equal to 1 if the household participates in the VAG/CRB. This model uses a sub-sample of households that are located in the GMA Significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

25
Table 5: Cumulative effects after 15 years following an initial 10% increase per variable.

||PM2_._5|_>_6|||PM2_._5 _>_10||
|---|---|---|---|---|---|---|
||25%|50%|75%|25%|50%|75%|
|Impulse:|Pollution||||||
|Pollution|_−_28_._203|_−_14_._470|_−_6_._403|_−_50_._507|_−_17_._071|0_._334|
|Bottom|_−_3_._066|_−_2_._227|_−_1_._534|_−_8_._312|_−_5_._742|_−_3_._876|
|Average|_−_1_._399|_−_0_._854|_−_0_._409|_−_4_._162|_−_2_._645|_−_1_._520|
|Impulse:|Bottom expenditures||||||
|Pollution|_−_3_._143|_−_2_._416|_−_1_._794|_−_5_._966|_−_4_._171|_−_2_._761|
|Bottom|6_._504|7_._192|7_._940|4_._389|5_._132|5_._928|
|Average|1_._435|2_._089|2_._773|0_._668|1_._221|1_._747|
|Impulse:|Average expenditures||||||
|Pollution|_−_0_._043|_−_0_._003|0_._031|_−_0_._407|_−_0_._235|_−_0_._106|
|Bottom|_−_0_._329|_−_0_._027|0_._268|_−_0_._919|_−_0_._634|_−_0_._363|
|Average|2_._522|2_._954|3_._330|1_._525|2_._146|2_._772|

Table 6: Economic pollution costs based on a conversion rate from IDR to dollars of 100,000 IDR to 7.410 USD – Pulled from Google Finance on 15 October, 2017.

||Annual expenditures in|Average annual loss in expenditures|
|---|---|---|
||USD per capita|for 10% PM2_._5 increase|
|Bottom household PM2_._5<sup>6+</sup>|397.132|8.844|
|Average household PM2_._5<sup>6+</sup>|1074.54|9.177|
|Bottom household PM2_._5<sup>10+</sup>|420.50|24.145|
|Average household PM2_._5<sup>10+</sup>|1183.96|31.316|

xix
||||(0.094)|(0.098)|||(0.055)|(0.059)|
|---|---|---|---|---|---|---|---|---|
|Observations|13,857|13,857|13,857|13,857|36,337|36,337|36,337|36,337|
|R-squared|0.928|0.928|0.929|0.929|0.904|0.905|0.905|0.905|
|Treated|208|315|537|537|475|709|1171|1171|
|Total country-products|1034|1034|1034|1034|2937|2937|2937|2937|
|Intermediate-Final >15|-0.225*|||-0.030|||||
|Intermediate-Final >10||-0.286***||-0.207|||||
|Intermediate-Final >5|||-0.162|-0.063|||||

Note: Robust standard errors clustered at the cluster importer-product level are in parentheses. All specifications include importer-year, importer-product, and product-year fixed effects. The samples include the top 15 producers of automobile or top 15 exporters of final electronics other than Japan. The last three rows of each panel report the difference between coefficients for auto and electronics in Panel A and intermediate and final in Panel B and C.

*** p<0.01, ** p<0.05, * p<0.1

Table 1 presents the estimates of Equation (1) for the impact of the 2011 earthquake on imports from Japan. The results show that importers who are more dependent on Japan are more likely to reduce imports. The coefficient in columns 1-3 and 5-7 consider highly dependent imports versus less dependent as defined by the indicated threshold (5, 10, or 15). For example, column 1 shows that importers who relied on Japan for 15 percent or more of the product reduced exports of autos by 56 percent (exp(.814)=.44), as compared with those that relied on them for 15 percent or less. When the variables for all of the thresholds are included, in columns 4 and 8, results show that the declines in imports for both auto and electronics were stronger for higher thresholds of dependence on Japan.<sup>10</sup> The estimated negative impact on imports from Japan varies between around 32 percent for electronics and 56 percent for auto— the coefficients for auto are statistically different from those for electronics when using the 15 percent threshold. The stronger effects for the auto industry may reflect the higher fixed costs of building a factory in this sector relative to electronics. As shown in Section 2, sectors with higher fixed costs would experience larger relocation of production after a natural disaster that destroyed the factory. Alternatively, the industry may be more sensitive to cost savings--a flatter benefits curve or face more elevated insurance costs.

10 For instance, to gauge the impact for country-products with a Japan share above 15 percent, we would need to sum the coefficients of all the dummy variables (i.e., 5 percent, 10 percent, and 15 percent) as these variables are not mutually exclusive. Note that the coefficients in columns 1-3 and 5-7 cannot be directly compared since the control groups are different. In columns 4 and 8 they are additive and relative to less than 5 percent.

11
# **1. Introduction**

In the years leading up to the current financial crisis, banks around the world expanded their balance sheets to increase profitability in an environment of cheap funding. Access to international funding made it possible for individual banks and overall banking systems to reach enormous size relative to their countries’ GDP. The prime example is Iceland where the liabilities of the overall banking system reached around 9 times GDP at the end of 2007, before a spectacular collapse of the banking system in 2008.<sup>2</sup> By the end of 2008, the liabilities of publicly-listed banks in Switzerland and the United Kingdom had reached 6.3 and 5.5 times their GDP. Liabilities of banks in Belgium, Denmark, France, Ireland, and the Netherlands similarly exceeded two times their GDP. At the end of 2008, we identified 30 publicly-listed banks worldwide with liabilities exceeding half of their country’s GDP. Twelve of these had total liabilities exceeding 1 trillion US dollars.

These huge banks have assets well over $100 billion, far exceeding the technologically optimal size of around $25 billion found by Berger et al. (1997) on the basis of US data. Huge banks are no doubt difficult to manage effectively, and huge size may yield few additional risk diversification benefits. Banks may have grown this large in part because bank managers see their stature and pay increase with bank size. Alternatively, bank growth has been motivated by a desire to reach too-big-to-fail status, implying lower funding cost. Banks perhaps can increase their implicit claim on the financial safety net by ever increasing their size under normal business cycle conditions and in the absence of a major financial crisis. The financial and economic crisis that started in 2008, however, has been unexpectedly deep with a severe deterioration of the public finances so far and projected in the years to come. This raises doubts about countries’ ability and determination to save their largest banks. At the very least, financially strapped governments may be forced to resolve any future large-bank failures in a relatively cheap way, implying large losses to bank creditors.<sup>3</sup>

> 2 The liabilities of Glitner Bank, Kaupthing Bank and Landsbanki together were 9.0 times Icelandic GDP in 2007.

> 3 At a time of severe financial crisis, countries’ ability to guarantee bank liabilities may be at least as important as their formal guarantees. In the recent crisis, many countries provided explicit guarantees of many non-deposit liabilities and they have extended deposit insurance. Explicit deposit insurance of some kind already was almost universal prior to the current crisis (see Demirgüç-Kunt, Kane, and Laeven (eds.), 2008).

2
**Table 3. Component of the change in public net debt to GDP ratio for ORC**

|Component|Formula|Sign|Line in<br>“Table”|
|---|---|---|---|
|1) Non-oilprimarybalance:|)<br>(<br>_t_<br>_t_<br>_t_<br>_g_ −|+|4|
|2)Oil revenues at referenceprice|oilrt|-|7|
|3) Seigniorage:|_t_<br>σ|-|8|
|4) Domestic real interest rate cost:|)<br>1(<br>)<br>1(<br>)<br>(<br>1<br>_t_<br>_t_<br>_t_<br>_t_<br>_t_<br>_d_<br>_g_<br>_b_<br>_i_<br>+<br>⋅<br>+<br>⋅<br>−<br>−<br>π<br>π|+|12|
|5)Forein real interest rate cost|)<br>(<br>1<br>*<br>*<br>_t_<br>_t_<br>_t_<br>_f_<br>_b_<br>_i_<br>⋅<br>−<br>−<br>π|+|13|
|g|)<br>1(<br>)<br>1(<br>*<br>_t_<br>_t_<br>_g_<br>+<br>⋅<br>+<br>π|||
|6) Real GDP growth:|1<br>)<br>1(<br>−<br>⋅<br>+<br>_t_<br>_t_<br>_t_<br>_d_<br>_g_<br>_g_|-|14|
|7) Capital loss due to real exchange<br>rate depreciation|[<br>]<br>)<br>1(<br>)<br>1(<br>)<br>(<br>)<br>1(<br>)<br>1(<br>*<br>*<br>1<br>1<br>*<br>1<br>*<br>^<br>_t_<br>_t_<br>_t_<br>_t_<br>_t_<br>_l_<br>_t_<br>_t_<br>_f_<br>_t_<br>_g_<br>_oa_<br>_nfa_<br>_i_<br>_b_<br>_i_<br>_e_<br>+<br>⋅<br>+<br>+<br>⋅<br>+<br>−<br>⋅<br>+<br>⋅<br>−<br>−<br>−<br>π|+|15|
|8) Net changes in the assets of the oil<br>stabilization fund|*<br>*<br>1<br>*<br>1<br>*<br>*<br>)<br>1(<br>)<br>1(<br>)<br>(<br>)<br>(<br>_t_<br>_t_<br>_t_<br>_t_<br>_t_<br>_t_<br>_t_<br>_l_<br>_oa_<br>_g_<br>_oa_<br>_nfa_<br>_i_<br>+<br>+<br>⋅<br>+<br>+<br>⋅<br>−<br>−<br>−<br>π<br>π|-|16|

27
be noted that these subsidies can quickly become expensive. Early adopters tend to be individuals who are enthusiastic about new technologies and likely would have made the switch to EVs regardless of subsidies. In economics terms, their demand tends to be relatively inelastic with respect to price. Each $1,000 subsidy between 2010-15 has been estimated to increase EV uptake by 2.6% (Jenn et al., 2018) and 7.5% (Wee et al., 2018) in the US. In other words, each additional EV sale cost the federal government $38,462 and $13,333 by the two estimates, respectively. Beyond merely the cost, it is once again important to keep in mind the equity considerations. For example, a study of a suite of policies to encourage EV adoption in Colombia showed that is the interventions largely benefit high-income households, making them regressive (Callejas et al., 2022).

Alternatively, policy makers can also opt to encourage EV adoption indirectly by subsidizing charging infrastructure. This has been shown to be an effective strategy (Li et al, 2022). For example, results of a survey conducted in China in 2017 showed consumers valued having a charging station every 5 km higher than a 50% discount off charging fees (Ma et al., 2019). Additionally, increasing the ubiquity of charging infrastructure lowers the demand on range. With batteries making up a considerable portion of the weight of EVs, lowering battery capacity can save non-negligible weight. All else equal, this lowers the severity of injuries in the event of road collisions, damage to road infrastructure, and importantly, the energy required to move the vehicle. Finally, there is a positive feedback effect between charging infrastructure investment and consumer EV adoption (Li et al., 2017). That is, more charging stations encourage more EVs, and vice versa. This feedback makes subsidizing the two sides of the market complementary. Governments can kickstart the feedback loop by upgrading the fleet of public vehicles away from traditional fuels (Corts, 2010).

# 4. Elasticities

Quantifying the response to price instruments is fundamental for designing taxation schemes for demand management. In this section, we present a compilation of response elasticities to price instruments found in the literature. Table 4 shows inverse-variance weighted average elasticities of new vehicles, fuel economy, fuel demand, and traffic volume with respect to purchase, ownership, and use taxes. Appendix B provides a description of the summarization methods and the complete list of elasticities and their geographic and temporal scopes.

24
years after) yields similar patterns (4.35 vs. 2.33 total projects, and 0.86 vs. 0.28 HD, and both differences are statistically significant).

Total lending per year also declines by 60.4 percent in the 5 years after graduation compared to the 5 years before graduation. Analysis of 10-year periods shows that the decline is similar at 59.7 percent, thus signaling that the declines are not only transitory. The decline in HD lending is even larger and statistically significant. Comparing the 5-year periods before and after graduation, average HD GP lending per country per year declines from $87.91m to $17.9m, representing a 79.6 percent change. The pattern for 10-year periods is similar – average HD lending declines from $84.7m to $12.5m, or 72 percent.

In contrast, results for the countries graduating in the pre-1988 period are quite the opposite (Table 3B). These countries increased borrowing in the years after IDA graduation although significantly less so for the HD areas (107 percent overall vs. 77 percent for HD before and after 5-year periods). Similarly, while the overall number of projects increased, the number of HD projects did not change. These patterns are consistent with the notion that countries borrow proportionately less for the HD when they graduate from IDA.

These differences, however, do not account for other explanatory variables, including country size, and could be driven by the behavior of a few countries. For instance, if big countries like India and China stop borrowing for HD, the difference in average borrowing could be large even if the average country did not change borrowing behavior after graduation. To check for this, we look at per capita borrowing before and after graduation. Looking at the 5-year before and after periods, we see that per capita total lending and per capita HD lending also decreased, although the results are not statistically significant. These results suggest that country size is an important variable to control for; however, the results are also consistent with a reallocation away from HD.

12