rafmacalaba/datause-encoder-data huggingface dataset:
As noted in Section 3.4, in cases where there is evidence of a significant treatment effect, a test on how the distribution of percentile rank statistics based on mean-adjusted treatment effects (below referred to as _mean-adjusted percentile rank statistics_ ) compares to a uniform distribution is useful for understanding whether or not the impacts are likely to be common across treated firms. When there is no evidence of a significant treatment effect, a test on how the distribution of the unadjusted (original) percentile rank statistics compares to a uniform distribution is useful for understanding whether or not there might be offsetting effects on either side of the mean treatment effect.

Figure 1 presents results from the Kolmogorov-Smirnov and the Anderson-Darling heterogeneity tests for four of our five outcome variables using the estimates from our benchmark specification (described in Section 5).<sup>50</sup> For each outcome variable, we provide two graphs: 1) in the left-hand column a representation of the distribution of the unadjusted percentile rank statistics generated by SCM, and 2) in the right-hand column a representation of the distribution of the mean-adjusted percentile rank statistics. The first graph offers additional evidence on the null hypothesis of no impact. The second graph is most useful for testing the commonality of impact, given that an impact exists.

The tests for heterogeneity in the unadjusted results are largely consistent with the results of the earlier tests. Unadjusted percentile rank statistics for the 75<sup>th</sup> percentile of the clearance time distribution are not distributed uniformly, as they would be under the null hypothesis of no impact. There is some evidence of departures from uniformity for unadjusted percentile rank statistics for the 90<sup>th</sup> percentile of the clearance time distribution and for import value, but these are not statistically significant. The primary lesson from the left-hand column is that our earlier tests did not appear to miss any statistically significant effects that arose because the mean percentile rank statistic obscured heterogeneous treatment effects across firms.

The right-hand column is more interesting, because it provides evidence on the question of whether the treatment effect identified by previous tests on the 75<sup>th</sup> percentile of clearance time distribution was heterogeneous across firms or common to all firms. If mean-adjusted percentile rank statistics look to be uniformly distributed, we should accept the null hypothesis that the treatment impact is common across all firms. This appears to be the case. We cannot reject the

> 50 For purposes of visual clarity we exclude graphs for the inspection rate because there is little evidence in any of our treatments for a causal effect of the IHC program on the inspection rate.  There is no evidence of firm heterogeneity in the causal effects of inspection rates, nor is there evidence of heterogeneity remaining when we adjust by the quite modest mean estimated treatment effect.

42
**Table 2. Proportions of courses at the undergraduate and masters level covering different development economics topics by regions**

||||||Undergrad|uate level||||Masters<br>level|
|---|---|---|---|---|---|---|---|---|---|---|
||LAC|SSA|MENA|EAP|ECA|SA|All<br>developing<br>countries|Top 20 US<br>economics<br>departments|Non-Top US<br>economics<br>departments|All<br>developing<br>countries|
|Theories and models of<br>economic growth and<br>development|0.852***|0.771**|1.000***|0.889**|0.917***|0.688|0.834***|0.438|0.765*|0.728**|
|(Measuring) Poverty and<br>inequality|0.607|0.543|0.667|1.000**|0.750|0.750|0.648|0.563|0.824|0.580|
|The role of institutions,<br>incl. corruption|0.492***|0.371***|0.889|0.889|0.833|0.563*|0.559**|0.875|0.882|0.593**|
|Human capital, incl.<br>health and education|0.541**|0.457**|0.556|0.778|0.583|0.625|0.545**|0.813|0.765|0.519**|
|Population growth,<br>fertility, demographic<br>transition|0.410|0.400|0.556|0.778|0.583|0.438|0.448|0.625|0.647|0.444|
|The role of the State|0.230|0.429**|0.556**|0.778***|0.417*|0.375|0.372**|0.125|0.471**|0.259|
|International trade and<br>trade policy|0.246|0.457|0.556|0.556|0.500|0.375|0.366|0.250|0.118|0.383|
|Agricultural<br>transformation, rural<br>development|0.197|0.457|0.333|0.778**|0.333|0.375|0.338|0.313|0.294|0.321|
|Urbanization, rural-<br>urban migration|0.213|0.371*|0.333|0.667***|0.250|0.500**|0.324|0.125|0.353|0.321|
|Dependency theory1|0.380***|0.278**|0.500***|0.000|0.167|0.182*|0.304**|0.000|0.083|0.196*|
|Labor markets|0.213|0.229|0.222|0.556**|0.083|0.625***|0.276|0.125|0.059|0.309|
|Environment and<br>development, incl.<br>climate change|0.230|0.229|0.111|0.667***|0.417**|0.250|0.269*|0.063|0.412**|0.235|
|The role of aid|0.082|0.457*|0.556*|0.444|0.333|0.188|0.262|0.188|0.412|0.235|

27
|Environmental Support Program Project|Yves Andre Prevost|
|---|---|
|Environmental Support Program Project|Yves Andre Prevost|
|Environmental Support Program Project|Yves Andre Prevost|
|Environmental Support Program Project|Yves Andre Prevost|
|Financial and Legal Management UpgradingProject(FILMUP)|Denyse E. Morin|
|Financial and Legal Management UpgradingProject(FILMUP)|Denyse E. Morin|
|Financial and Legal Management UpgradingProject(FILMUP)|Denyse E. Morin|
|Financial and Legal Management UpgradingProject(FILMUP)|Denyse E. Morin|
|Financial and Legal Management UpgradingProject(FILMUP)|Denyse E. Morin|
|Financial and Legal Management UpgradingProject(FILMUP)|Denyse E. Morin|
|Financial and Legal Management UpgradingProject(FILMUP)|Denyse E. Morin|
|ZW-GEF BiodiversityConserv SIL(FY98)|Watson C. Chidawanyika|
|ZW-GEF BiodiversityConserv SIL(FY98)|Watson C. Chidawanyika|
|ZW-GEF BiodiversityConserv SIL(FY98)|Watson C. Chidawanyika|
|ZW-GEF BiodiversityConserv SIL(FY98)|Watson C. Chidawanyika|
|ZW-GEF BiodiversityConserv SIL(FY98)|Watson C. Chidawanyika|
|ZW-GEF BiodiversityConserv SIL(FY98)|Watson C. Chidawanyika|
|Transport Rehabilitation Imports Program Project||
|Transport Rehabilitation Imports Program Project||
|Rural Afforestation Project||
|Rural Afforestation Project||
|Rural Afforestation Project||
|Rural Afforestation Project||
|Rural Afforestation Project||
|Small Farm Credit Project||
|Small Farm Credit Project||
|Small Farm Credit Project||
|Small Farm Credit Project||
|ManufacturingExport Promotion Project||
|ManufacturingExport Promotion Project||
|ManufacturingExport Promotion Project||
|ManufacturingExport Promotion Project||
|Petroleum Fuels SupplyTechnical Assistance Project||
|Power Project||
|Power Project||
|Power Project||
|Power Project||
|Power Project||
|Power Project||
|HighwayProject(01)||
|HighwayProject(01)||
|HighwayProject(01)||
|HighwayProject(01)||
|National Agricultural Extension and Research Project(01)||
|National Agricultural Extension and Research Project(01)||
|National Agricultural Extension and Research Project(01)||
|National Agricultural Extension and Research Project(01)||
positive relationship found earlier in Table 6 (for poorer countries) still held even with the inclusion of these potentially omitted variables. This result suggests that other (omitted) country characteristics create the omitted variable bias, for instance gender culture and anti-discrimination legislation.<sup>33</sup> However, because we have no proxies for these omitted factors we are not able to test this formally.

We still find that trade has a negative impact on the low skill gender gap for the poorer countries. For the richer countries we also find a significant negative effect for the sectoral trade intensity variable, but a significant positive effect for the aggregate trade intensity variables. However, the finding for the sectoral trade variable is most relevant in this respect, because it measures most directly the link between changes in trade and the gender gap within _given occupations and countries_ . The impact of FDI net inflows is now found to be positive for low skill occupations in richer countries and insignificant otherwise, contradicting the results in Tables 6 and 7. Also here we are suspicious of these results given that we lack information on _sectoral_ rather than aggregate FDI net inflows to measure the link between changes in FDI and the gender gap within _given occupations and countries_ .

# **5.  Conclusion**

In this paper we have undertaken one of the first truly global studies of the effect of globalization on the gender wage gap. The study is based on the most far-ranging crosscountry survey of wages available, the ILO October Inquiry, allowing us to measure the gender wage gap within narrowly defined occupations. Assuming that skills are relatively homogenous within these narrowly defined occupations, the occupational gender wage gap provides a direct measure of gender differences in labor market treatment and therefore discrimination. The occupational gender wage gap appears to fall with increasing economic development and trade, but not always. The lack of evidence of a narrowing impact of trade and evidence of a widening impact of FDI net inflows on the high skill occupational gender gap in poorer countries show that globalization may not

> 33 Although table 9 includes country by occupation dummies, the positive correlation between GDP per capita and the gender gap is already reversed if only country dummies are included.

30
- 29 -

The dependent variable is the (censored) frequency of travel per month. Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

Another set of complementary data covers local businesses. Along with the household surveys, local enterprises were also interviewed. Again, the sample size is limited; Only 30 firms, such as retailers and restaurants, were surveyed along each RSDP road every year. Each firm was asked about the number of employees and their basic business environment. In the sample, the average size of firms is about 5 employees ( **Table 12** ).

Logarithmic panel regression was performed on the size of firms. It was found that the improvement of RSDP roads had a positive and significant impact on the firm size ( **Table 13** ). This is consistent with the above local business hypothesis: as the local economies pick up, local businesses are hiring more people. The demand for local services increases along the main corridors. Feeder road accessibility is also important: The size of firms is significantly reduced when they are located more than 30 minutes from an all-weather road. The coefficients of _FEED30-60min_ and _FEED>3hr_ are significantly negative.<sup>7</sup> Though, there is no clear synergy between the RSDP and feeder road proximity.

By type of business, hotels, restaurants and pharmacies are among the rapidly growing subsectors, compared with a baseline, which is retailers. As the traffic increases, these businesses along the improved roads are likely to expand, generating more local jobs. While supply increases, prices are also likely to decline. These indirect effects are considered as a main reason why the road programs have a positive and significant impact on nonagricultural household income.

7 Note that in our sample data, there are no firms that are located 1 to 2 hours from an all weather road.
# **1. Introduction**

It is hard to be confident in our responses to rare events for which we have little direct experience. Big shocks are a case-in-point. By “big shocks” we mean unusual and highly covariate (negative) shocks, impacting virtually every member of society. Since such shocks do not happen often, it is hard to learn from them. Granted, something can be learned from observation or from documented occurrences of big shocks in other places, but direct experience is likely to be far more valuable.<sup>2</sup> The misfortune of directly observing one’s own welfare, or that of friends and family, in the (negatively) shocked state carries important information. This is v <mark>isceral knowledge that is not so easily transferable. Pa</mark> st experience with a big shock teaches people about the gains from investing in adaptation and protection, which brings benefits if a future big shock is realized. On the other hand, with little or no direct experience of a big shock, the expected benefits from such investments will be lower.

Elements of this argument find support in past research on how exposure to war and other forms of violent conflict affects behavior, collective actions, fairness, and cooperation. Evidence from many countries indicates that people exposed to war violence increase their social participation and are more likely to take actions intended to benefit others; see the review of this literature in Bauer et al. (2016). What is especially notable is that the impact of exposure does not appear to diminish with time and is transferred across generations (Grosjean 2014).

This paper formalizes and tests the hypothesis that countries with past experiences of a big shock tend to invest more in the institutions—including social capital as well as public health and social protection infrastructure—needed to cope with another big shock, and thus be less vulnerable to that shock. Our tests use data for the two biggest shocks of the last 100 years, namely World War 2 (WW2) and the pandemic of 2020 due to the novel coronavirus. We assess whether countries with greater exposure to WW2—as reflected in death rates— experienced different COVID-19 outcomes in the pandemic. Our reasoning is that countries with larger prior human losses from WW2 will have been more inclined to make investments that help facilitate greater willingness in the population to behave in ways that reduce the human toll of the pandemic. Voluntary compliance with various non-pharmaceutical interventions (NPIs)

> 2 Educationalists have emphasized the importance of direct experience to knowledge, separately to formal education; see, for example, the discussions in Boud et al. (1993).

2
# _v. References_

Abouleinein, Soheir; El-Laithy, Heba; Kheir-El-Din, Hanaa (2009) The Impact of Phasing Out Subsidies of Petroleum Energy Products in Egypt. Egyptian Center for Economic Studies, Working Paper No.145.

Angel-Urdinola, D. and Q. Wodon (2005) Do Utility Subsidies Reach the Poor? Framework and Evidence for Cape Verde. Paper prepared for the Poverty Reduction and Economic Management Department, Africa Region, World Bank, Washington, DC.

Bagdadioglu, Necmiddin and Necmi Odyakmaz (2009) “Turkish electricity reform” Utilities Policy 17, 144–152.

Bagdadioglu, Necmiddin, Basaran, Alparslan and Price, Catherine Waddams (2007) “Potential Impact of Electricity Reforms on Turkish Households”, CCP Working Paper No. 07-8 Bacon, R. and Kojima, M., (2006) “Phasing Out Subsidies”, Viewpoint Note 310, Public Policy for the Private Sector, World Bank, Washington D.C.

Benitez, Daniel, Chisari, Omar (2010) “Recycling Universal Energy Subsidies on Residential Consumers Alternative Fiscal Policies to Help the Poorest”, mimeo, The World Bank.

Coady, David, Moataz El-Said, Robert Gillingham, Kangni Kpodar, Paolo Medas, and David Newhouse (2006) “The Magnitude and Distribution of Fuel Subsidies: Evidence from Bolivia, Ghana, Jordan, Mali, and Sri Lanka.” IMF Working Paper WP/06/247. Washington, DC: International Monetary Fund.

Dahl, C.A. (2011). A global survey of electricity demand elasticities. Presented at 34th IAEE International Conference: Institutions, Efficiency and Evolving Energy Technologies, 2011, Stockholm School of Economics, Sweden.

Dahl, C.A. (2012). Measuring global gasoline and diesel price and income elasticities. _Energy Policy_ , 41, 2-12.

Del Granado, J., D. Coady and R. Gillingham (2010), The unequal Benefits of Fuel Subsidies: A Review of Evidence for Developing Countries. IMF Working Paper, WP/10/202.

Ebinger, Jane (2006) “Measuring financial performance in infrastructure: an application to Europe and Central Asia”, World Bank Policy Research Working Paper, 3992.

Esprey, A. and Esprey, M. (2004). “Turning of the light: a meta-analysis of residential electricity demand” _Journal of Agricultural and Applied Economics_ , 36, 1.

Goodwin, P., Dargay, J., and M. Hanly (2004) “Elasticities of Road Traffic and Fuel Consumption with Respect to Price and Income: A Review”. Transportation Reviews, 24, 275–292.

31
In 2004-05, non-government Indian companies raised about 2.7 billion USD from the market through the issue of common stock, and US$378 million by selling bonds/debentures (no preferred shares were issued). Despite the size of the new issuance, Indian’s financial markets, relative to the size of its economy and population, are much smaller than those in many other countries. Table 3-D presents a comparison of external markets (stock and bonds) in India and different country groups (by legal origin) using measures from LLSV (1997a). Figure 2 plots the size and depth of a country’s external markets vs. the degree of protection of investors based on data in Table 3-D. The horizontal axis measures overall investor protection (protection provided by the law, rule of law, and government corruption) in each country, while the vertical axis measures the (relative) size and efficiency of that country’s external markets.<sup>16</sup> Most countries with English common-law systems (French civil-law systems) lie in the top-right region (bottom-left region) of the graph. India is located in the south-eastern region of the graph with relatively strong legal protection (in particular, protection provided by law) but relatively small financial markets.

# **_Banking Sector_**

Over the decades, India’s banking sector has grown steadily in size, measured in terms of total deposits, at a fairly uniform average annual growth rate of about 18%. There are about 100 commercial banks in operation with about 30 state owned banks, about 30 private sector banks and over 40 foreign banks. Still massively dominated by state-owned banks (they account for over 80% of deposits and assets), the years since liberalization have seen the emergence of new private sector banks as well as the entry of several new foreign banks. This has resulted in a much lower concentration ratio in India than in other emerging economies (Table 3-A, and Demirgüç-Kunt and

> markets. While stock prices in India co-move less frequently than those in China (one of the worst in the world), they are much more synchronous than those in the developed markets such as the U.S.

> 16 Following LLSV, the score on the horizontal axis is the sum of (overall) creditor rights, shareholder rights, rule of law, and government corruption. The score of the vertical axis indicates the distance of a country’s overall external markets score (external cap/GNP, domestic firms/Pop, IPOs/Pop, Debt/GNP, and Log GNP) to the mean of all countries, with a positive (negative) figure indicating that this country’s overall score is higher (lower) than the mean.

15
# **Table 1. Global Offshore Services Market Size**

||**Source**|||**Rev**|**enues (U**<br>**Ye**|**S$ Billio**<br>**ar**|**ns)**||**Comments**|
|---|---|---|---|---|---|---|---|---|---|
||||**2005**|**2006**|**2007**|**2008**|**2009**|**2010**||
|OECD<br>(2008)|Global**offsho**<br>services mark|**re**<br>et|81.4|100.8|125.6|**157.4**|198.6|252.4|Includes ITO-BPO & other<br>high value service activities.|
|NASSCO<br>M (2009)|Global**offsho**<br>services mark|**re**<br>et|44.25|59|78.3|**101**|117.5||Includes ITO-BPO & other<br>high value service activities.<br>"Derived from a 40% share of<br>marketfrom India."<sup>a</sup>|
|BCG<br>(2007)|Global<br>**offshore**|ITO|19.2|22.7|26.9|31.9|37.3|43.2|BPO includes other high value<br>service activities|
|<br>Based on|<br>services<br>|BPO|27.4|42.3|65.1|100.3|154.5|238.1|.|
|IDC data|market|Total|46.6|65.0|92.0|**132.2**|191.8|281.3||
|GARTNER<br>(2009)|Global<br>**outsourcing**|ITO|||||268||BPO includes other high value<br>service activities.|
||<br>and<br>|BPO|||||156|||
||**offshoring**<br>services<br>market|Total|||||424|||
|NASSCO<br>M and<br>EVEREST<br>(2008)|Global<br>**offshoring**<br>BPO<br>market|BPO||||26-29||||
|McKinsey<br>&|Global<br>**Offshoring**|ITO|16.7-<br>19.6||||||McKinsey calculates the<br>offshoring market potential|
|Company<br>(2006)|ITO-BPO<br>market|BPO|9.8-<br>12.3||||||with a range. They states that<br>the market has captured only<br>|
|||Total|26.5-<br>31.9||||||10% of its full potential.<br>ITO: $147-178 billion<br>(captured only 11%)<br>BPO: $122-154 billion<br>(captured only 8%)<br>From these estimates we have<br>calculated the real market in<br>2005.|
|A. T.<br>Kearny<br>(2009)|Global<br>**offshoring**<br>BPO<br>market|BPO||||30|||22% of the global BPO market<br>is offshore|

a Based on the reports from Boston Consulting Group (2007) and the Nasscom-Everest study in 2008. BCG estimated that Indian market share was 46% in 2007, while the Nasscom-Everest estimate lies between 41% and 46% (NASSCOM, 2008).

Source: CGGC, Duke University based on (AT Kearney, 2009; Chakrabarty et al., 2006; Harris, 2009; NASSCOM, 2008, 2009; OECD, 2008; The Boston Consulting Group, 2007; Young et al., 2008).

According to OECD estimates (2008), the size of the offshore services market will reach $252 billion in 2010. They stress, however, that growth rates will be different in each segment of the value chain (see Figure 3). The OECD study, published pre-crisis in early 2008, projected that 7
**Table 13: Determinants of energy consumption (Non CRS, Pooled Effects)**

|**Dependent**<br>**variable=ln(energy/output)**|**Four indus**|**tries**|**Pulp and P**<br>**industry**|**aper**|**Cement in**|**dustry**|**Iron and St**<br>**industry**|**eel**|**Aluminum**|**industry**|
|---|---|---|---|---|---|---|---|---|---|---|
||Coef.|P-value|Coef.|P-value|Coef.|P-<br>Value|Coef.|P-<br>Value|Coef.|P-<br>Value|
|**Constant**|2.0149|0|1.886|0|1.9939|0|-0.0593|0.918|-0.59|0.376|
|**Ln(price of energy/price of**<br>**output)**|-0.5281|0|-0.508|0|-0.2271|0|-0.7399|0|-0.161|0.384|
|**Ln (technology development)**|-0.0213|0|0.0179|0.042|-0.005|0.04|-0.006|0.706|-0.003|0.863|
|**Ln(Value of industry output at**<br>**constant price)**|0.7993|0|0.7376|0|0.834|0|0.9611|0|0.9448|0|
|**Ln(Price of energy/price of**<br>**output)*Ln(technology**<br>**development)**|-0.0188|0|0.01208|0.191|-0.0078|0.003|-0.0009|0.953|-0.007|0.684|
|**Foreign capital intensity**|0.2561|0.321|-0.4599|0.303|-0.1457|0.545|6.1411|0.017|3.6265|0.017|
|**Foreign Capital**<br>**intensity*Ln(technology**<br>|-0.0300|0.028|0.006|0.855|-0.021|0.076|-0.5768|0.016|-0.003|0.975|
|**development)**<br>**Collectives**|-0.6553|0|0.066|0.659|-0.447|0|-1.325|0|-0.044|0.834|
|**Foreign**|-0.5586|0.001|0.5369|0.014|-0.164|0.368|-1.23|0.002|-2.869|0|
|**Hong Kong SAR, China; Macao;**<br>**Taiwan, China**|-0.4334|0|0.1495|0.267|-0.0442|0.504|-0.405|0.099|-1.023|0|
|<br>**Shareholding**|-0.2576|0|0.1814|0.069|-0.2085|0|-0.3288|0.02|-0.582|0|
|**Private**|-0.1957|0.095|0.2646|0.345|-0.1854|0.005|-1.1898|0.057|-0.747|0.026|
|**Other**|0.2347|0.69|(omitted)||0.0713|0.806|(omitted)||(omitted)||
|**North**|-0.566|0|-0.7231|0|-0.3217|0|-0.2828|0.237|-0.385|0.157|
|**Northeast**|(omitted)||(omitted)||(omitted)||(omitted)||(omitted)||
|**East**|-0.4117|0|-0.4876|0.004|-0.4105|0|-0.2504|0.249|-0.825|0.001|
|**South**|-0.3950|0|-0.1259|0.43|-0.3882|0|-0.4957|0.04|-0.648|0.008|
|**Southwest**|-1.1746|0|(omitted)||-0.1503|0.39|-0.6974|0.074|-0.894|0.002|
|**Year 2000**|0.0676|0.356|-0.029|0.805|0.051|0.344|-0.0207|0.909|0.1048|0.527|
|**Year 2001**|0.0860|0.241|0.0044|0.97|0.066|0.221|-0.0573|0.752|0.1042|0.537|
|**Year 2002**|0.0332|0.654|0.0642|0.594|0.0436|0.423|-0.147|0.421|0.0622|0.72|
|**Year 2003**|-0.0396|0.596|0.029|0.811|0.0184|0.737|-0.3469|0.06|-0.092|0.602|
|**Year 2004**|-0.0261|0.731|0.0166|0.892|0.0625|0.268|-0.4229|0.024|-0.121|0.513|
|**R**<sup>**2**</sup>**(obs.)**|0.6873 (15|28)|0.7354 (29|0)|0.7778 (67|7)|0.7236 (41|8)|0.8617 (14|3)|

37