rafmacalaba/datause-encoder-data huggingface dataset:
The Gambia (from 77 to 113 percent of GDP).<sup>10</sup> Other notable countries with high public debt burdens are Mauritania (99 percent of GDP in 2015–16) and Ghana (72 percent of GDP in 2015– 16). The public debt burden increased in Ghana despite improvements in the primary surplus. This reflects the substantial size of interest payments. In contrast, Sudan, Guinea, and the Comoros experienced a decline in the general government gross debt that exceeded 10 percentage points of GDP—specifically, 14, 10, and 14 percentage points of GDP, respectively. However, their average levels of public debt in 2015–16 were very different—with the Comoros at 26 percent of GDP, Guinea at 55 percent, and Sudan at 69 percent.

**Figure 6. General Government Gross Debt Across SSA Countries, 2010-13 vs. 2015-16**

_(Percent of GDP)_ <!-- Start of picture text --> 140<br>CPV<br>120<br>GMB<br>100 MOZ MRT<br>80<br>COG TGO<br>GHA<br>AGO ZWE SYC SDN<br>60 MWI MUS<br>SEN<br>ZMB ETHKEN GIN<br>GAB<br>CAFBENTCD LSO ZAFSLEBDI CIVGNB<br>40 NAM NERTZALBRMDG<br>RWAUGA<br>BFA<br>CMR MLI<br>COM<br>SWZ<br>20 COD<br>GNQ<br>NGA BWA<br>0<br>0 20 40 60 80 100<br>General government gross debt (% GDP), 2010‐13<br>General government gross debt (% GDP), 2015‐16<br><!-- End of picture text --> _Notes: Red dots represent resource rich countries while blue dots represent non-resource rich countries. Calculations based on data from Kose et al. (2017)_ Fiscal space has shrunk in tandem with rising debt burdens. Figure 7 plots the fiscal space—as defined by the general government gross debt as a percentage of average tax revenues—of Sub10 The database does not reflect the recently available data on previously undisclosed external debt of the Republic of Congo.

17
using both SIPRI and COMTRADE data. The lower bound estimate suggests that in the five years after a giant discovery, arms imports are 25% larger (asinh(0.258)). The arms share of GDP increases by 30%. The arms share of imports also increases, by 17%, though the effect is not statistically significant here. This may be because in some countries total imports increase as well, as the demand for say manufacturing goods increases.<sup>4</sup> Yet the large coefficient suggests that the increase in arms imports is larger than that of total imports on average.

In Table 2 I show the results of estimating the same regressions but on a restricted sample of countries, i.e. those countries which have experienced at least one giant discovery over the period of study. The idea here is that discovery countries in non-post-discovery years provide a more conservative counterfactual than countries without giant discoveries or even without oil exploration, which may be different from discovery countries in terms of arms imports behavior in general. This robustness check confirms the baseline results and even suggests slightly larger effects. The lower bound estimate here suggests that arms imports increase by 31% in the five years following a giant discovery. The coefficient on the arms share of imports is now statistically significant, and suggests that the arms share of total imports increases by around 21%. The effect on arms imports as a share of GDP remains stable around 30%.

In Figure 6 I run the baseline regression of the odd columns in Table 1 but using different post-discovery treatment periods. More precisely, I estimate six different regressions where _Dit_ is a dummy equal to 1 in the year of the discovery only or also in the one, two, three, four or five subsequent years. While I find positive and significant effects across these different specifications, the 5-year period captures the largest effect. This is in line with arms imports occurring not necessarily right after but in any of the post-discovery years, before extraction happens. It is also in line with the event studies in Figure 5 which show arms imports surging mostly in the 3 to 5 years after the discoveries.

> 4Gollin et al. (2016) show that across 116 developing countries, imports of food and manufacturing, as a share of GDP, go up when natural resource exports increase.

13
**Figure 4 – Reduction in Medical Costs due to Tobacco Taxes**

The medical effects show a highly progressive effect, disproportionally benefiting lower-income groups in the population. This derives from two factors; (1) the higher price elasticity and (2) a lower income base that massively benefits from the reduction in medical costs.

# **Net effects: Total distributional impacts**

After calculating the effects of the price increase on consumption and medical expenditures, the study estimated both effects jointly. The aggregate effect of the increase in tobacco taxes is progressive, benefiting lower-income groups in the population more in all three scenarios (table 10; figure 5). In the lower-bound the net effect is negative among the all the income groups, affecting less the lower income groups. In the medium and upperbound scenario, the lower income groups benefited from the policy of raising the tobacco tax, but the higherincome groups see negative effects due to the increase in tobacco prices.

|||**Table**|**10 – Net**|**effect on**|**househo**|**ld expen**|**ditures (**|**%)**|||
|---|---|---|---|---|---|---|---|---|---|---|
|_Price shock under_|_Decile 1_|_Decile 2_|_Decile 3_|_Decile 4_|_Decile 5_|_Decile 6_|_Decile 7_|_Decile 8_|_Decile 9_|_Decile 10_|
|Lower-bound elasticity|-0.02|-0.07|-0.09|-0.12|-0.14|-0.16|-0.12|-0.16|-0.16|-0.12|
|Medium elasticity|0.08|-0.01|-0.02|-0.05|-0.08|-0.09|-0.07|-0.11|-0.11|-0.07|
|Upper-bound elasticity|0.18|0.06|0.05|0.02|-0.02|-0.03|-0.01|-0.05|-0.05|-0.03|

_Source:_ Based on data from the 2015 Household Budget Survey. _Note:_ The table shows the share of total consumption for each decile.

14
expenditure and 0.15 s.d. for labor income when pair-wise matching is used, and to 0.20 s.d. for both variables when stratifying or using the min-max re-randomization method.

Our other two outcomes variables, math test scores and height z-scores lie in the higher end of baseline predictive power, with the baseline outcome and six other variables predicting 43.6 percent and 35.3 percent of the variation in follow-up outcomes, respectively. Figures 5a to 6c illustrate that the choice of method makes more of a difference for these highly predictable follow-up outcomes than for the less predictable ones. Stratifying, matching, and the minmax t-state method consistently lead to narrower distributions in the differences at follow-up when test scores or height z-scores are the outcomes. Nevertheless, even with these more persistent variables, the gains from pursuing balance on baseline are relatively modest when the sample size is 300 – using pair-wise matching rather than a pure random draw reduces the 95<sup>th</sup> percentile of the difference in means from 0.23 to 0.17 in the case of math test scores.

# **4.2 What does balance on observables imply about balance on unobservables?**

In general, what does balancing on observables do in terms of balancing unobservables? Aickin (2001) notes that methods which balance on observables can do no worse than pure randomization with regard to balancing unobserved variables.<sup>17</sup> We illustrate this point empirically in the Sri Lanka and ENE datasets by defining a separate group of variables from the data to be “unobservable” in the sense that we do not balance, stratify or match on them. The idea here is that, although we have these variables in these particular data sets, they may not be available in other data sets (such as measures of entrepreneurial ability). Moreover, these “unobservables” are meant to capture what balancing does to variables that are thought to have an effect on the outcome variable, but

> 17 To see this, consider balancing on variable X, and the consequences of this for balance on an unobserved variable W. W can be written as the sum of the fitted value from regressing W on X, and the residual from this regression: _W_ = _PXW_ + ' ( _I_ −− 1 _PX_ ' ) _W_ (1) _PX_ = _X_ ( _X X_ ) _X_

> Balancing on X will therefore also balance the part of W which is correlated with X, PXW. Then, since the remaining part of W, (I-PX)W is orthogonal to X, it will tend to balance at the same rate as under pure randomization.

- 19 -
# Appendix 5: Conditional logit models:

## Table 32

|Dependent variable: N|eed of fis|cal adjustme|nt based|on Escolano|et al.(201|4)|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|0.361***|0.400***|0.386***|0.398***|0.589***|0.672***|0.125|0.336***|
|(% of GDP)|(0.036)|(0.044)|(0.040)|(0.046)|(0.093)|(0.139)|(0.091)|(0.076)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-26.752***<br>(3.563)||-26.493***<br>(3.836)|-24.799***<br>(3.859)|-26.180***<br>(3.923)|-25.308***<br>(3.824)|-26.841***<br>(3.850)|
|Inflation (t-1)||-12.167***||-11.215***|-9.861***|-11.526***|-9.756***|-10.992***|
|||(3.716)||(3.829)|(3.658)|(3.843)|(3.513)|(3.810)|
|U.S. Interest rate||-0.064||-0.076|-0.159|-0.153|-0.155|-0.090|
|||(0.087)||(0.101)|(0.110)|(0.109)|(0.108)|(0.101)|
|**Political factors**|||||||||
|Gini|||0.098***|0.077**|0.068*|0.238***|0.072*|0.073*|
||||(0.035)|(0.038)|(0.037)|(0.085)|(0.038)|(0.038)|
|Rule of law|||0.326|0.169|0.095|0.168|-1.825***|0.195|
||||(0.269)|(0.339)|(0.341)|(0.343)|(0.697)|(0.340)|
|Margin of Majority|||1.539|1.919*|1.914*|2.125*|1.895*|-0.810|
||||(1.076)|(1.155)|(1.148)|(1.174)|(1.135)|(2.936)|
|Election's year|||0.239|0.441*|0.452*|0.427*|0.462*|0.441*|
||||(0.214)|(0.237)|(0.237)|(0.236)|(0.237)|(0.236)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||-0.304***<br>(0.112)||||
|Gini * Rigidity||||||-0.007**<br>(0.003)|||
|Rule of law * Rigidity|||||||0.073***<br>(0.022)||
|Majority * Rigidity||||||||0.109<br>(0.108)|
|Observations|1,080|1,065|960|948|948|948|948|948|
|Number of ifscode|60|60|53|53|53|53|53|53|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|r2_p<br>Standard errors in par<br>*** p<0.01, ** p<0.05|0.203<br>entheses<br>, * p<0.1|0.344|0.233|0.352|0.366|0.360|0.370|0.354|

- 61 -
India and Bangladesh (Table 4) and the estimated IIT indices for major products for both partners (Appendix 1), brings the following to light:

||**Table 5: Vert**<br>|**ical Tra**<br>|**de Potential between India and Ban**<br>|**gladesh***<br>|||
|---|---|---|---|---|---|---|
|**Reporter**|**Partner**|**HS2**|**Commodity (HS2)**|**IIT**<br>**(HS2)**|**IIT****<br>**(HS6)**|**Potential**<br>**(HS2 ****HS6)**|
|India|Bangladesh|03|Fish, crustacean, mollusc, and<br>others|0.970|0.787|0.183|
|India|Bangladesh|09|Coffee, tea, MATN, and spices|0.801|0.560|0.241|
|Bangladesh|India|03|Fish, crustacean, mollusc, and<br>others|0.200|0.003|0.197|
|Bangladesh|India|08|Edible fruits and nuts|0.180|0.001|0.179|
|Bangladesh|India|14|Vegetable plaiting materials|0.770|0.012|0.758|
|Bangladesh|India|19|PREP. of cereal, flour, starch, and<br>milk|0.160|0.012|0.149|
|Bangladesh|India|25|Salt, sulphur, earth, stone, and<br>plastering materials|0.830|0.140|0.691|
|Bangladesh|India|31|Fertilizers|0.950|0.194|0.756|
|Bangladesh|India|33|Essential oils, resinoids,<br>perfumery, and cosmetics|0.800|0.476|0.324|
|Bangladesh|India|39|Plastics and articles thereof|0.440|0.326|0.114|
|Bangladesh|India|53|Other vegetable textile fibres|0.020|0.000|0.020|
|Bangladesh|India|54|Man-made filaments|0.330|0.019|0.311|
|Bangladesh|India|55|Man-made staple fibres|0.530|0.288|0.242|
|Bangladesh|India|56|Wadding, felt, and nonwoven<br>yarns|0.690|0.041|0.650|
|Bangladesh|India|63|Other made-up textile articles|0.310|0.235|0.075|
|Bangladesh|India|84|Nuclear reactors, boilers, parts|0.980|0.277|0.703|
|Bangladesh|India|87|Vehicles of railway, tram, roll-<br>stock|0.080|0.051|0.029|

*IIT indices are calculated for bilateral trade between India and Bangladesh at H2 nomenclature. **Average of multiple products at HS6. _Source_ : Calculations based on UN COMTRADE.

1. The IIT index levels are higher in manufactured products than in primary products, reflecting the greater role of economies of scale in the production of manufactures.

2. IIT index scores suggest there are production-sharing opportunities, in a static sense, in 11 products, with varying potential. The range of this potential varies from textile and clothing (highest concentration) to iron and steel (lowest concentration). Electrical machinery and equipment and mechanical appliances occupy the middle portion of the value chain, with midlevel concentrations. The estimated results in Table 5 support this.

3. Only two sectors for India and Bangladesh had IIT shares in the moderate range: textiles and clothing, and electrical machinery and mechanical appliances. All other sectors had low or negligible IIT shares.

How, then, to intensify vertical IIT between the two countries? Sectors with increasing shares of IIT and high economies of scale are most likely to have bilateral trade growth potential. To realize that potential, should check on a case-by-case basis whether the different products are differentiated as final products or as parts and components versus final products.

10
China’s labor markets: over the past 3 decades, a huge amount of migrants have moved to cities, with rural males tend to work in brawn-intensive jobs such as construction, laborers, and so on. The framework of relative comparative advantage in brawn-intensive jobs by males thus explains why young men may choose to work more and enjoy lesser gains in education in response to health interventions.

To test the hypothesis of gender differences in the level of brawn and responsiveness of brawn to nutrition, we replicate Zhang (2012) and run regressions that relate boys’ and girls’ health status to Water Plant and other control variables. Here we are trying to understand the health effects of treated plant water for those young males and females, so we track their nutritional status recorded in CHNS before 25 and explore whether access to plant water improves their health and whether this improvement differs by gender. The sample size used for this analysis is 11,169 observations.<sup>46</sup> The results are in Table 10. Both boys and girls gain improvements in their height and body mass (i.e., weight/height) after they have been exposed to plant water. The key finding is that boys’ body mass has increased by 0.55 kg/m, which is much larger than such effect on girls (0.39 kg/m). Moreover, there is evidence that the same increase in body mass translate into more gain in strength for male than for female (Pitt et al. 2011). Thus, boys gain more brawn than girls after treated plant water becomes accessible. Our findings of gender-specific brawn-responsiveness to plant water thus also offer support to the brawn story in Pitt et al. (2012).

The brawn theory of gender division of labor has interesting implications about how an elder sibling’s gender identity affects education of younger siblings within a family through the income channel. When treated plant water improves health of young people in a household, this theory implies that the boys are relatively more likely to choose to work and girls more likely to continue schooling. As a result, male youth are more able to contribute to their household financial resources and provide better support to the schooling pursuit of their younger siblings. We thus expect that an individual with an elder brother gains more in education from

> 46 To construct the sample to examine the health effects of plant water between boys and girls, we track the measures of nutrition of our baseline individuals from age 0 to 25 recorded in CHNS data. Thus, the final sample is panel data of the individuals showing up in the sample for analyzing the effects on education, which is larger than the crosssectional data we use in the baseline analysis.

28
Table 5: Intrahousehold Bargaining and Vitamin C, D, and Iron Allocations

||(9)|(10)|(11)|(12)|(13)|(14)|(15)|(16)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Lvitc|Lvitc|Lvitd|Lvitd|Liron|Liron|Lanimiron|Lanimiron|
||_SURLS_|_SURHF E_|_SURLS_|_SURHF E_|_SURLS_|_SURHF E_|_SURLS_|_SURHF E_|
|L hast|0.010||-0.010||-0.006||0.021*||
||(0.009)||(0.018)||(0.006)||(0.012)||
|L wast|-0.038*||-0.027||-0.009||0.006||
||(0.020)||(0.039)||(0.013)||(0.024)||
|_κ_|-0.003|-0.013***|-0.019*|-0.019***|-0.023***|-0.027***|-0.015**|-0.016**|
||(0.005)|(0.004)|(0.010)|(0.007)|(0.004)|(0.004)|(0.008)|(0.006)|
|L mcapx|0.878**|0.620|2.797***|2.512**|0.492*|0.150|2.060***|0.917|
||(0.397)|(0.485)|(0.939)|(1.169)|(0.254)|(0.262)|(0.586)|(0.721)|
|(L macapx)<sup>2</sup>|-0.056*|-0.040|-0.186***|-0.178**|-0.029|-0.006|-0.122***|-0.052|
||(0.030)|(0.036)|(0.071)|(0.088)|(0.019)|(0.019)|(0.044)|(0.054)|
|L landpc|-0.028|0.605*|-0.022|0.204|0.022|0.005|-0.023|-0.172|
||(0.046)|(0.337)|(0.083)|(0.584)|(0.026)|(0.173)|(0.060)|(0.421)|
|L hhsz|0.042|0.999***|0.054|0.406|0.068**|0.085|0.062|-0.308|
||(0.049)|(0.369)|(0.099)|(0.651)|(0.028)|(0.181)|(0.067)|(0.476)|
|round1|-0.249***|-0.236***|-0.083|-0.160*|0.039*|0.022|0.146**|0.092|
||(0.044)|(0.047)|(0.086)|(0.090)|(0.023)|(0.024)|(0.058)|(0.061)|
|round2|-0.423***|-0.375***|0.025|-0.123|-0.013|0.008|0.207**|0.061|
||(0.066)|(0.071)|(0.124)|(0.139)|(0.035)|(0.037)|(0.084)|(0.092)|
|round3|-0.130**|-0.096*|-0.296***|-0.390***|0.014|-0.009|0.004|-0.094|
||(0.051)|(0.058)|(0.110)|(0.121)|(0.030)|(0.031)|(0.066)|(0.076)|
|Constant|-7.229***|-6.942***|1.876|0.430|-3.344***|-3.220***|-9.033***|-7.312**|
||(1.698)|(2.515)|(3.828)|(5.076)|(1.017)|(1.201)|(2.535)|(3.393)|
|Observations|10,555|10,515|10,555|10,515|10,555|10,515|10,555|10,515|
|P-value of the te|sts of equatio|n 13 for L ha|st=L wast,|for :|||||
|boy|0.076|0.286|0.811|0.801|0.574|0.852|0.053|0.085|
|girl|0.404|0.130|0.494|0.968|0.679|0.530|0.385|0.170|
|madol|0.067|0.189|0.636|0.554|0.028|0.131|0.194|0.034|
|fadol|0.189|0.703|0.156|0.126|0.991|0.267|0.008|0.005|
|fadlt|0.121|0.117|0.961|0.880|0.596|0.775|0.407|0.495|
|pregnant, fadol|0.711|0.487|0.476|0.950|0.905|0.253|0.701|0.945|
|pregnant, fadlt|0.204|0.337|0.773|0.729|0.788|0.585|0.351|0.581|
|lactating, fadol|0.021|0.437|0.863|0.706|0.880|0.583|0.084|0.029|
|lactating, fadlt|0.221|0.704|0.399|0.656|0.930|0.725|0.476|0.367|

Note: Robust standard errors clustered at the household level are in brackets. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1; vitc, vitamin C adequacy ratio; vitd, vitamin D adequacy ratio; iron, iron adequacy ratio; animiron, animal iron intake/total iron requirement; L, natural log; madol, male adolescent; fadol, female adolescent; fadlt, female adult; preg, pregnant; lact, lactating; hast, 1+husband’s assets; wast, 1+wife’s assets; mcapx, monthly per capita expenditure landpc, per capita land; hhsz, household size; _κ_ , individual health endowment; additional controls (not shown) are discussed in section 4.

42
_18 Railway Reforim In the Central and Eastern European (CEE) Economies_

# SOURCES

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- Galenson, Alice, 1989, "Labor Redundancy in the Transport Sector." World Bank, Working Paper Series No. 158, Washington, D.C.

- Galenson, Alice and Louis Thompson, 1993, "The Bank's Experience with Railway Lending: An Evolving Approach." World Bank, Infrastructure and Urban Department, Transport Division, Washington, D.C.

- Halicek, Dieter, 1988, "Experience with Labor Redundancy Schemes in the Transport Sector in Western Europe, the United States and Japan." World Bank Discussion Paper INU 9, Washington, D.C.

- Huff, Lee, and Louis Thompson, 1990, "Techniques for Railway Restructuring." World Bank, Working Paper Series No. 380, Washington, D.C.

- Moyer, Neil, and Louis Thompson, 1992, "Options for Reshaping the Railway." World Bank, Working Paper Series, No. 926, Washington, D.C.

- ", Poland: Decentralization and Reform of the State." World Bank Country Study, Europe,

- Middle East and North Africa Technical Departnent, Washington, D.C.

- , 1992, "New Opportunities for the Railways: The Privatization of British Rail.' Department of Transport, U.K. Government Cm2012, London, England. (cited as British Rail White Paper).

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was driven by factors external to Mexico, the credit risk of Mexican firms operating in energyrelated sectors ramped up as a result.<sup>3</sup> Importantly, the degree of Mexican banks’ exposure to the energy sector varied substantially before the shock. We exploit this cross-bank variation to identify how banks reallocated their credit depending on their ex-ante exposure to the struggling sector. We show that banks with large exposures to the energy sector had an incentive to maintain borrowers afloat even as their creditworthiness deteriorated, causing a decline in lending to other sectors (Caballero, Hoshi, and Kashyap 2008; Peek and Rosengren 2005).

How banks cope with shocks and transmit them to other sectors of the economy remains an important issue in finance. However, there are several challenges to overcome to answer these questions rigorously. One challenge is having a credible counterfactual, since aggregate shocks may affect the entire banking sector simultaneously. To overcome this hurdle, we exploit the late 2014 oil price shock and adopt a difference-in-differences approach with ex-ante similar banks differing in their exposure to energy-related sectors prior to the shock. We define ex-ante bank exposure as the ratio of loans to firms in energy-related sectors over the bank’s tier 1 capital in the month prior to the unanticipated shock.<sup>4</sup> A second challenge in identifying banks’ strategies is to isolate changes in the supply of credit from changes in the demand for credit, as aggregate shocks might impact firms’ credit needs. To control for time-varying credit demand, we rely on loan-level data obtained from the Mexican credit registry on the universe of commercial bank loans from January 2013 to June 2016. Loanlevel data allow us to saturate our specifications with bank*firm and firm*month fixed effects, exploiting variation in the credit conditions of a firm-bank pair over time, as well as by the same firm across different banks with varying exposures to the shock (Khwaja and Mian 2008; Morais et al. 2019).

> 3 The global energy price drop was related to the advent of new oil suppliers, such as shale producers in the United States (World Bank 2018). Figure 1 shows the rapid drop of West Texas Intermediate prices from almost $100 per barrel in mid-2014 to about $50 per barrel over just a few months. This drop was associated with a weakening of the creditworthiness of energy companies in Mexico and other emerging markets, as shown by the increase in their credit default swap premiums.

> 4 Our measure of exposure follows the definition of credit exposure outlined by the Basel Committee on Banking Supervision (2014). As a robustness check, we use four alternative bank-level measures of ex-ante exposure and confirm that our findings remain unchanged. These measures are (i) August 2014 ratio of energy sector loans to total loans, (ii) August 2014 ratio of energy sector loans to total assets, (iii) August 2014 ratio of energy sector loans to total bank equity, and (iv) December 2012 ratio of energy sector loans to tier 1 capital (Table A4 in the appendix).

3