rafmacalaba/datause-encoder-data huggingface dataset:
the bank, we subtract the disbursal amount from the discounted stream of repayments.<sup>12</sup> To achieve as representative a sample as possible, we also include 26 credit files from clients who applied, but were turned down by the Lender. For the incentive payouts in our study, these rejected loans are treated as non-performing<sup>13</sup> . Throughout the analysis, we report results disaggregated by non-performing and declined loans and show that our results are unaffected by the classification of loans declined ex-ante by the Lender. Importantly, the final columns of Table III show that loan files contain information that may be useful in distinguishing good loans and bad loans, suggesting there are returns to effort in this setting.

# **4.3 Loan Officers and Experimental Procedure**

Loan officers were recruited from the active staff of several leading private and public sector commercial banks. Participants first attended an introductory presentation, completed a practice session, and then participated in up to 15 sessions of the experiment, in which they evaluated six loan applications per session. Working conditions and presentation of information were designed to closely resemble the actual work environment of the representative loan officer.<sup>14</sup> Experimental sessions were scheduled to last one hour, although participants could finish early or late if they so chose. Incentive treatments, as described in Section 2, were randomly and individually assigned at the loan officer and session level, such that officers evaluated a batch of six loans under a given scheme.<sup>15</sup> The experiments took over a year to complete, and not all incentive schemes were eligible to be assigned in any given session.

> 12We estimate the Lender’s net profit per loan as the net present value of the disbursal plus repayments including interest, discounted by 8%, the approximate rate on Indian commercial paper between January 1 and December 31, 2009, and assuming a 10% recovery on defaulted loans.

> 13However, we do not include initially rejected loans in the profitability calculations. In non-reported results we also verified that our analysis is robust to the exclusion of rejected loans.

> 14Harrison, List, and Towe (2007) point out that laboratory behavior may not match field behavior when eliciting risk attitudes (“background risk”). In contrast to that study, we use within-subject variation, and the inclusion of loan officer fixed-effects may reduce the importance of heterogeneous perceptions of background risk from different subjects.

> 15The number of loan files was held constant to rule out multitasking concerns.

14
## Figure: E.10.8

# MEXICO LABOR INTENSIVE EXPORTS

<!-- Start of picture text --> LABOR INTENSIVE TOTAL EXPORTS<br>1200000<br>X 1000000<br>X 800000<br>o 600000<br>' 400000<br>20000_<br>° 200000__<br>0 1970 1980 1990<br><!-- End of picture text --> <!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1970<br>ROW (29.90%)<br>CA (5.57%) NA (64.53%)<br><!-- End of picture text --> <!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1980<br>ROW (34.70%)<br>NA (59.11%)<br>CA (6.19%)<br><!-- End of picture text --> <!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1990<br>ROW (23.16%)<br>CA (3.30%)<br>NA (73.54%)<br><!-- End of picture text -->
TABLE 11 Coverage for production function approach, filter-based, and expectations-based estimates: Advanced economies

|**Economy**|**Production**<br>**fti**|**h**<br>**Univariate and**<br>**ltiit filt**|**WEO expectations**|
|---|---|---|---|
||**uncon appro**|**c**<br>**muvarae ers**||
|**Advanced economies**||||
|Australia|1998-2032|1981-2024|1990-2022|
|Austria|1998-2032|1995-2024|1990-2022|
|Belgium|1998-2032|1995-2024|1990-2022|
|Canada|1998-2032|1981-2024|1990-2022|
|Croatia|1998-2032|2000-2024|1994-2022|
|Cyprus|1998-2032|1995-2024|1990-2022|
|Denmark|1998-2032|1991-2024|1990-2022|
|Estonia|1998-2032|1995-2024|1993-2022|
|Finland|1998-2032|1981-2024|1990-2022|
|France|1998-2032|1981-2024|1990-2022|
|Germany|1998-2032|1981-2024|1990-2022|
|Greece|1998-2032|1995-2024|1990-2022|
|Hong Kong SAR, China|1998-2032|1990-2024|1990-2022|
|Ireland|1998-2032|1995-2024|1990-2022|
|Israel|1998-2032|1995-2024|1990-2022|
|Italy|1998-2032|1981-2024|1990-2022|
|Japan|1998-2032|1981-2024|1990-2022|
|Korea, Rep.|1998-2032|1981-2024|1990-2022|
|Latvia|1998-2032|1995-2024|1993-2022|
|Lithuania|2000-2032|1995-2024|1993-2022|
|Netherlands|1998-2032|1981-2024|1990-2022|
|Norway|1998-2032|1981-2024|1990-2022|
|Portugal|1998-2032|1995-2024|1990-2022|
|Slovak Republic|1998-2032|1995-2024|1994-2022|
|Slovenia|1998-2032|1995-2024|1994-2022|
|Spain|1998-2032|1995-2024|1990-2022|
|Sweden|1998-2032|1981-2024|1990-2022|
|Switzerland|1998-2032|1981-2024|1990-2022|
|United Kingdom|1998-2032|1981-2024|1990-2022|
|United States|1998-2032|1981-2024|1990-2022|

Source: World Bank.

Note: Forecasts for filter-based estimates for 2022Q2-2024Q4 are based on the lag structure of the estimation. Forecasts for production function-based estimates are derived as described in Kilic Celik, Kose, and Ohnsorge (2023). Univariate filters: Hodrick-Prescott, Baxter and King, Christiano and Fitzgerald, Butterworth, and unobserved component model.

58
_of Environmental Research and Public Health_ , _17_ (2), 522. <u>https://doi.org/10.3390/ijerph17020522</u> Haas, J., Moreno-Leiva, S., Junne, T., Chen, P.-J., Pamparana, G., Nowak, W., Kracht, W., & Ortiz, J. M. (2020). Copper mining: 100% solar electricity by 2030? _Applied Energy_ , _262_ , 114506. https://doi.org/10.1016/j.apenergy.2020.114506 Haider, S., & Bhat, J. A. (2020). Does total factor productivity affect the energy efficiency: Evidence from the Indian paper industry. _International Journal of Energy Sector Management_ , _14_ (1), 108-125. <u>https://doi.org/10.1108/IJESM-11-2018-0010</u> Hamamoto, M. (2006). Environmental regulation and the productivity of Japanese manufacturing industries. _Resource and Energy Economics_ , 28(4), 299-312. <u>https://doi.org/10.1016/j.reseneeco.2005.11.001</u> Hamza, K., Laberteaux, K. P., & Chu, K.-C. (2021). On modeling the cost of ownership of plugin vehicles. _World Electric Vehicle Journal_ , 12(1). https://doi.org/10.3390/wevj12010039 Harchaoui, S., & Chatzimpiros, P. (2017). Reconstructing production efficiency, land use and trade for livestock systems in historical perspective. The case of France, 1961–2010. _Land Use Policy_ , 67, 378-386. https://doi.org/https://doi.org/10.1016/j.landusepol.2017.05.028 Hasan, M. A., Frame, D. J., Chapman, R., & Archie, K. M. (2021). Costs and emissions: Comparing electric and petrol-powered cars in New Zealand. _Transportation Research PartD: Transport and Environment_ , 90, 102671. <u>https://doi.org/10.1016/j.trd.2020.102671</u> Hasanbeigi, A., Morrow, W. R., Masanet, E., Sathaye, J., & Xu, T. (2012). _Assessment of Energy Efficiency Improvement and CO2 Emission Reduction Potentials in the Cement Industry in China_ . Berkeley: Lawrence Berkeley National Laboratory Hassan, A. (2019). Do renewable energy incentive policies improve the performance of energy firms? Evidence from OECD countries. _OPEC Energy Review_ , 43(2), 168-192. <u>https://doi.org/10.1111/opec.12146</u> He, X., Wang, F., Wallington, T. J., Shen, W., Melaina, M. W., Kim, H. C., De Kleine, R., Lin, T., Zhang, S., Keoleian, G. A., Lu, X., & Wu, Y. (2021). Well-to-wheels emissions, costs, and feedstock potentials for light-duty hydrogen fuel cell vehicles in China in 2017 and 2030. _Renewable and Sustainable Energy Reviews_ , 137, 110477. <u>https://doi.org/10.1016/j.rser.2020.110477</u> 46
# **7. References**

- Alfaro, L., and A. Charlton, 2009. “Intra-Industry Foreign Direct Investment.” _American Economic Review_ 99 (5), 2096-2119.

- Antras, P., and R. Caballero, 2009. “Trade and Capital Flows: A Financial Frictions Perspective.” _Journal of Political Economy_ 117(4), 701-744.

- Aviat, A., and N. Coeurdacier, 2007. “The Geography of Trade in Goods and Asset Holdings.” _Journal of International Economics_ 71(1), 22-51.

- Aykut, D. and D. Ratha, 2003. “South-South FDI flows: How Big Are They?” _Transactional Corporations_ 13(1), 149-176.

Broner, F., T. Didier, A. Erce, and S. Schmukler, 2013. “Gross Capital Flows: Dynamics and Crises.” _Journal of Monetary Economics_ 60(1), 113-133.

- Dailami, M., S. Kurlat and J. Lim, 2012. “Bilateral M&A Activity from the Global South.” World Bank Policy Research Working Paper Series 5953.

- Daude, C., and M. Fratzscher, 2008. “The Pecking Order of Cross-border Investment.” _Journal of International Economics_ 74(1), 94-119.

- de la Torre, A., C. Calderon, T. Didier, E. Levy Yeyati, and S. Schmukler, 2010. “Globalized, Resilient, Dynamic: The New Face of Latin America and the Caribbean.” World Bank LAC SemiAnnual Report, October.

- de la Torre, A., T. Didier, C. Hevia, S. Pienknagura, and S. Schmukler, 2012. “Latin America Copes with Volatility, The Dark Side of Globalization.” World Bank LAC Semi-Annual Report, April.

- de la Torre, A., T. Didier, A. Ize, D. Lederman, and S. Schmukler, 2015. “The Rise of the South: Challenges for Latin America and the Caribbean.” World Bank Flagship Report, forthcoming.

- Didier, T., R. Rigobon, and S. Schmukler, 2013. “Unexploited Gains from International Diversification: Patterns of Portfolio Holdings around the World.” _Review of Economics and Statistics_ 95(5), 1562-1583.

- Greenaway, D., and R. Kneller, 2007. “Firm Heterogeneity, Exporting, and Foreign Direct Investment.” _Economic Journal_ 117(517), 134-161.

- IMF (International Monetary Fund), 1993. “Balance of Payments Manual.” IMF, Washington DC. <u>https://www.imf.org/external/pubs/ft/bopman/bopman.pdf.</u>

- ———, 2002. “Coordinated Portfolio Investment Survey Guide.” IMF, Washington DC.

22
sunflower, sorghum, maize, mung bean, soyabeans, tobacco, chillies, capsicum, okra, tomatoes, cabbages and cauliflowers. Since these are all crops grown in the local area and within the migratory potential of the pest, it is capable of causing continuing losses in agriculture.

b) Guntur (and Krishna and Prakasam also) forms a local "hot spot" of resistant pests. This has arisen largely due to the indiscriminate use of huge amounts of pesticides on cotton in the area. Resistance monitoring studies have shown that Heliothis populations in Guntur have RFs close to or exceeding the values consistent with field failure levels.

c) There is widespread migration of the moths into the inland areas of the state, especially in November, when the they are assisted by the prevailing wind patterns. In other words, the relatively more resistant Guntur strains are "contaminating" the other areas and raising the RFs in those areas towards the field failure levels. The fact that there have been only sporadic incidents of field failure reported in other areas (despite a continuing selection pressure) is because population pressures have not been high enough to precipitate a crisis situation. Nonetheless, it must be stated (Pedgley et al., 1987) that if resistant moths continue to migrate from Guntur, it is very likely that populations will build up to field failure levels in a short period of time.

60
is a dummy variable indicating recorded natural disasters in The Chronicles in DDFFPPFFPprefectureFFDDii,tt _i_ in year _t_ , which includes droughts, flooding, locusts, and earthquakes. P P and are defined as before. represents the prefecture fixed effects; , year fixed effectstt . In columns 1 and 2 of Panel A in Table 3, neither XXii μμii P P nor P θθPtt × R R P is tt tt ii significant, suggesting that natural conditions did not deteriorate in the warlord period, and that FFRRF prefectural border ruggedness was not associated with obvious changes in local natural conditions.

We also conduct three other robustness checks in Table 3. First, a small number of prefectures (i.e., six) did not directly neighbor with prefectures controlled by other warlords— then their border ruggedness could have little military implication for them. In general, given the frequent changes of territories during that era, our prior is that even inner prefectures’ border ruggedness matters since they could become outer prefectures some months or years later. This is why in the base scenario we include the border ruggedness of all prefectures. After excluding all six of such prefectures in column 3, we obtain similar results as in our baseline regression.

Second, wars could interrupt agricultural and business activities and lead to famines. To exclude the impact of wars, we delete the observations with wars happening in the observation prefecture-year. The coefficient of P P × R R P is -0.096, similar as before. When tt ii the prefectures were surrounded by less rugged borders, warlords, war or not, still did less to FFRRF prevent famines or provide famine relief for their subjects.

Third, we consider the sensitivity of our results when taking into account famine severity. In our baseline regressions we consider famine incidence. But famines differ in severity, and more severe famines might reflect more serious governance failure. Do our key results still hold if famine severity is considered? Because the data set does not provide a precise scale of famines, we construct our new famine measure relying on qualitative descriptions, as described in Su, Fang and Yin (2014). We create an ordinal variable that is 2 if the record mentions human casualties, 1 if the record mentions hunger or malnutrition, and 0 otherwise. Among the 167 famines, 78 are classified as severe (i.e., with a scale of 2), and 89 as mild (i.e., with a scale 17
**Figure 1: One month vs Four Month recall of Monthly Sales for the Month of March 2005.**

<!-- Start of picture text --> 4 6 8 10 12<br>Log Sales Reported in Round 1<br>12<br>10<br>8<br>6<br>Log Sales Reported in Round 2<br>4<br><!-- End of picture text -->

**Figure 2: Quarterly vs Annual recall of 12 month sales for the year 1 April 2005 to 31 March 2006**

<!-- Start of picture text --> 8 10 12 14 16<br>Log Yearly Sales (quarterly recall)<br>16<br>14<br>12<br>10<br>Log Yearly Sales (annual recall)<br>8<br><!-- End of picture text --> 29
Our findings are similar when we use an alternative classification (Table A.8). There is an increase in the value of non-export crops that are traded, with no difference between male- and female-headed households. We also find evidence of decrease in non-export crop production among female-headed households.

Why is it that households do not switch towards more commercial crop production in response to the availability of more options for commerce, as <mark>Shamdasani (2016)</mark> finds in India? There are a few possibilities. First, it is worth remembering that the response of a farmer depends on the types of markets that are now newly accessible. If rural roads offer connectivity to local markets rather than export markets, there may not be as strong an incentive to switch to export crops. Emerging evidence from the trade literature on the high costs of intranational trade (Atkin et al., 2015) suggests that this may be the case: domestic markets may be better thought of as a collection of small fragmented markets rather than a homogenous single market. In that case, better roads improve the links between these fragmented local markets where the demand for export crops may not be very high.

Second, switching may take more time than our quantitative data allow us to capture. Several respondents in our qualitative data collection exercise told us that they did start to cultivate certain types of trees. However, since our study spans a period of six years, we would not expect to see impacts on crop production or trade associated with increased cultivation of acacia, orange, or cinnamon in the VARHS data: the cropping cycle for these trees is seven to eight or 10 years. The decrease in production of nonexport crops among female-headed households may reflect this gap in production immediately after the switch to a more profitable crop in the long run.

“ _I started planting acacia in 2012. First, I planted on 2 ha, and planted rubber on the remaining 1 ha. In 2016, I planted acacia on all 3 ha since rubber was not profitable._ ” — Male, 63, An Bac, Quảng Nam Province When analyzing production and trade of livestock, we find that access to roads is associated with reduction in both livestock production and trade for female-headed households only (Table A.6). Analysis of changes in household income patterns, combined with qualitative work, provide insights into the potential reasons why.

# _Changes in household income_

Table A.9 presents estimates of impacts on household income. While we do not find significant impacts on total income, our results suggest that the composition of income has changed. Notably, these patterns differ for female- and male-headed households. We observe an increase in income from agriculture for male-headed households and a decrease, albeit smaller, in income from the sale of assets. For femaleheaded households, we observe the opposite pattern: a decrease in income from agriculture and an increase in income from the sale of assets.

16
# **E. Ebola**

## **E.1 Specialized Ebola Facilities and Training**

The UN Mission for Emergency Ebola Response (UNMEER) compiled information on three types of treatment facilities:

1. Ebola Treatment Unit (ETU): 17 facilities with an average of 32 beds;

2. Ebola Holding Center (EHC): 49 facilities with an average of 18 beds; and 3. Community Care Center (CCC): 41 facilities with an average of 10 beds

**Figure E.1:** Location of Ebola Treatment Facilities

<!-- Start of picture text --> 10 ° N 10 ° N<br>9.5 ° N 9.5 ° N G<br>G G<br>G G G<br>9 ° N 9 ° N GG G G G G<br>GG G G G G<br>G G G G<br>8.5 ° N GG GG G G 8.5 ° N G G G G<br>G<br>G<br>8 ° N G G 8 ° N<br>7.5 ° N 7.5 ° N<br>7 ° N 7 ° N<br>13 ° W 12.5 ° W 12 ° W 11.5 ° W 11 ° W 10.5 ° W 13 ° W 12.5 ° W 12 ° W 11.5 ° W 11 ° W 10.5 ° W<br>Type G ETU CCC EHC Type G CCC EHC ETU<br>(a) UNMEER (b) National Ebola Response Center (NERC)<br><!-- End of picture text --> Maps of three types of treatment facilities: Ebola Treatment Units (ETUs), Ebola Holding Centers (EHCs), and Community Care Centers (CCCs). The plots differ in the source of the information: data on the left come from the National Ebola Response Center (NERC); the right, from UNMEER. These sources largely overlap, though the NERC data contains fewer CCCs and more missing geo-coordinates than the UNMEER data. Both datasets were accessed through the Humanitarian Data Exchange.

A31
There continue to be a number of different risk factors for infant mortality. However, these risk factors have remained relatively constant over time, with male children more likely to die early than female children, a common pattern throughout the world due to genetic factors (Hill & Upchurch, 1995). Children from poorer and uneducated households and living in rural areas are also more likely to die early in life and there are substantial regional disparities (annex, Table 1).

# 4.2.2 Nutrition

In addition to facing different chances of early health care and even early death, children face unequal chances of healthy growth and nutrition depending on their backgrounds. Table 2, in the annex, presents the full set of relationships between nutrition and children’s background. Notably, there are substantial disparities in nutrition by wealth (Figure 6). As of 2011, 28 percent of the poorest children were stunted compared to 7 percent of the richest. The poorest children have also made the least progress over time; they have had relatively lower reductions in their chances of being stunted. In addition to differences by wealth, there are substantial disparities— depending on where children live and their parents’ education—for stunting and other nutrition outcomes.

**Figure 6. Stunting by Wealth Quintile and Year (Percentages)**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50 2003/4<br>40 35 2011<br>28<br>30 25<br>20<br>20 16 16 15<br>10<br>7 7<br>10<br>0<br>Poorest Poorer Middle Richer Richest<br>Percentage<br><!-- End of picture text --> _Source:_ Authors’ calculations based on DHS 2003/04 and ENPSF 2011.

Location acutely affects children’s chances of living in a household that uses adequately iodized salt and, therefore, being protected against cognitive deficits. As Figure 7 shows, rates range from 3 percent in Tensfit to 25 percent in North Central. Additionally, there are large differences by rural-urban residence, wealth, and parents’ education. These factors are interrelated. For instance, families that are less wealthy are also less educated.

13
15 _-_

-

Table **8.** Average Axle Loads by Vehicle Type (tons)

|Vehicle<br>type|Sample<br>size|Axle 1|Axle 2|Axle 3|Axle 4|Axle 5|GVW|Equiv.<br>Std.<br>Axles|
|---|---|---|---|---|---|---|---|---|
|Pick-up|2|1.6|3.2||||4.7|0.03|
|Light bus|47|2.3|3.6||||5.9|0.16|
|Heavy bus|196|4.0|6.6||||_10.5_|0.79|
|M-wagon|123|2.7|4.4||||7.0|0.21|
|Light truck 490|3.7|5.2||||8.9|0.85||
|Medium truck|132|4.1|4.8|4.1|||13.0|1.13|
|Heavy truck|192|_5.1_|7.6|5.6|5.7||24.0|3.98|
|Heavy truck|34|5.2|5.7|5.4|5.6|5.8|27.6|4.12|
|Other|2||||||26.0|7.88|

Source: Bhandari, 1990.

Current transport taxes have been surveyed in a TecnEcon report for the World Bank (1988b) and will not be replicated here. The main conclusions of the report were as follows:

- (a) The duty and purchase taxes on imported vehicles at the rate of 25-35 percent -

- (depending on age and engine size) do not exceed in fact, are less than the rates for other imported consumption goods.

- (b) Commercial vehicles (trucks, vans, buses) are tax exempt, as are motorized vehicles whose engine capacity does not exceed 1,600cc (if petrol driven) or 1,800cc (if diesel driven). Furthermore, returning expatriates who were away for over two years are exempt, as are lumber companies, foreign construction companies and companies with government con.racts. The effective rates are consequently much lower than the official ones, even in the case of high-capacity saloon cars (or cars that are over 5 years old).

- (c) Vehicle inspections (and licensing fees) are performed once every 6 months (in conjunction with the roadworthiness tests). The fees are based on a progressive scale: US$4-36 for private motorcars, US$13-36 for rigid trucks, and US$71-133 for road tractors. The enforcement of the rules is, however, lax, and government vehicles are exempt.

- (d) Fuel taxes include a 'road fund' component (about 3 percent of the retail price of gasoline and 5 percent of the price of diesel oil), which is the main source of the fund (other sources aie licensing fees and tolls).