rafmacalaba/datause-encoder-data huggingface dataset:
et al., 2015) and that larger transfers may not necessarily increase the behavioral response (Filmer and Schady, 2011).

# **6.6 Health impact**

We can perform a rudimentary calculation of the potential health gain by translating the increase in adherence to labor and delivery protocols to mortality gains for newborns within the first 7 days of birth (early neonatal mortality), which is likely most malleable to the effort applied by the health worker. We do so using estimates of the impact of protocol adherence on neonatal mortality. Because there are additional benefits of better protocol adherence—to the mother and newborn—we likely under-estimate the health gains.

In a study of delivery care in health facilities in Uttar Pradesh, India, Semrau et al. (2020) estimate that each additional action (out of 10 actions) by the health care provider is associated with a 30 percent decrease in early neonatal mortality. We observe an 8 percent improvement in the rewards arm relative to the information arm, corresponding to 0.8 additional actions. Assuming linearity, this would imply a 24 percent reduction in early neonatal mortality among births in health facilities. Using the observed neonatal mortality of 33 per 1,000 deliveries in (Semrau et al., 2020), the 24 percent reduction translates into 8 averted early neonatal deaths per 1,000 facility-based deliveries. Of the approximately 7.6 million births in Nigeria in 2018, 39 percent, or roughly 3 million, occurred in health facilities (National Population Commission, 2019). Thus, the improvement in protocol adherence we observed impact would translate into 24,000 fewer neonatal deaths.

We can benchmark the size of this impact in two ways. The first is the economic benefit. The value of a statistical life in Nigeria is estimated to be USD 485,000 (Viscusi and Masterman, 2017) with a life expectancy of 55 years in 2018 (World Bank, 2019). Thus, 24,000 fewer neonatal deaths would translate into an annualized economic benefit of USD 212 million. We can also compare our estimated health gain to that of alternative policies. Okeke (2021) reports on a cluster randomized trial in Nigeria in which either qualified physicians or mid-level professionals are sent to primary care health facilities. They find that physicians produce significantly higher quality of 27
For example, in highly unequal societies only a few privileged buyers may have access to the internet. This type of buyers may be more ‘international’, i.e. may have a preference for purchases from remote countries, and hence this selection may explain why distance matters less online. A selection of ‘international’ exporters on eBay could also be driving the difference. In countries with low barriers to export and high internet penetration exporters should be most similar online and offline. Using interaction terms, we show that even in the extreme scenario in which all countries would be as equal as Sweden and as easy to export from as Hong Kong SAR, China, eBay would still significantly reduce the distance effect.

We conclude with an estimate of the gains from trade brought about by internet technologies. We use the formula proposed by Arkolakis, Costinot and Rodriguez-Clare (2012) to calculate the welfare gains that would result from a drop in offline search costs to the online level, as captured by the difference in distance effects. We find that in the average country, real income would increase by 29 percent. The reminder of the paper is organized as follows. In section 2 we provide some descriptive statistics regarding international trade flows on eBay. Section 3 presents our empirical strategy and section 4 the results. Section 5 presents the trade gains from world flattening. Section 6 concludes.

# **2 International trade on eBay: Descriptive statistics**

Our data cover all eBay trade flows between 62 developing and developed countries over the period 2004-2009. These 62 countries, identified in Figure 2 , represent around 92 percent of global offline trade in 2008. Total cross-border flows on eBay were on average USD 6 billion per year over that period, representing a small fraction (0.06 percent) of world trade. The correlation between the logs of bilateral offline and eBay trade is 0.71, suggesting trade patterns are geographically similar online and offline. Since we want to compare online and offline trade flows as precisely as possible, we focus on the period 2004-2007 to abstract from unusual experiences during the Great Trade Collapse of 2008-2009 (Baldwin 2009).<sup>4</sup> We then

> 4The Great Trade Collapse may have come with goods shifting, trade finance problems, or new protectionist pressures that may have affected online and offline trade differently.

5
# inflated while the post-reform production is understated. For example, Berg [1993] estimated that real

output decline in 1990 was more in the range of 7 to 8 percenr rather the official 12 percent in some countries (a factor of 0.67). Despite the correction however, this is still a sizable drop for a single year.

<!-- Start of picture text --> T ---- -------------- l----- ----- >-<br>00<br>30 _ -~- 40 .3. Bulgaria ._ Romanla> _Yer<br>5.pee9 p1 i theformer Czechoslovakia 61.5 percent (9)o<br>IL~~~~~~~~~~ 1986 1987_ 198 198 1990_ ___1___991____<br>Polandb<br>in Ro 30 .Ro1ana percen (1991) --<br>1986 1987 1988 1989 1990 1991<br>Year<br><!-- End of picture text --> Figure 3: Revenue Effort in Eastern Europe (revenue as percent of GDP) While rae dishntegrabon of ndeitonal revebaue bases accounted _for_ much of the revenue problem, Vit did not explain ad . The revenue effort in each country, as measured by a siscple ratio of public reveise to GDP, suggested a significant decline in the overlal colection performanc ae broken upn Figure 3). The co:Rection rate fell rapidly in a few years: from 60.2 percent (19 to 34.2 percent (1992) in Bulgana; 57.6 percent Qi988) to 49.5 percent (1991) in the fonmer Czechoslovalia; 61.5 percent (1988) to 52.9 percent (1991) in H"gary; 48.0 percent (1988) to 29.2 percent (1991) in Poland; and 51.1 percent (1989) to 40.1 percent (1991) in Romania.

The radical change in the underlying tax bases has engendered collection problems in several ways. Value added of services is nsing as thie distnbution system is decenttalized. The scale of enterprises is also srhdngknk as the service sector grows and as thie large socialized enterprises arc broken up and privatized.

_Go. Renue Unwrrtainw_ _6_
<!-- Start of picture text --> Temperature<br>oO<br>N<br>oO<br>cr<br>3<br>£<br>8<br>2<br>Eo TT eT ee ee<br>Ss<br>><br>%e<br>oO<br>Yy<br>2013m1 2014m7 2016m1 2017m7<br>Year-month<br>kernel = epanechnikov, degree = 0, bandwidth = 2.56<br><!-- End of picture text --> <!-- Start of picture text --> Rainfall<br>[xx | a EES EET<br>oO<br>y<br>c<br>oO<br>o<br>=<br>E<br>2<br>58<br>S '<br>><br>3<br>oO<br>on<br>So<br>oO<br>2013m1 2014m7 2016m1 2017m7<br>Year-month<br>kernel = epanechnikov, degree = 0, bandwidth = 4.21<br><!-- End of picture text -->
# **Table A4: Panel B – Multinomial Regressions, 2006**

Log Odds ratio between Outright Owners and Mortgage holders (Marginal Effects)

|**Country**|**lage**|**female**|**hhsize**|**lnincome**|**hhnodep**|**hhdep**|**house**|**secedu**|**teredu**|**fulltime**|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|0.2519 ***|0.0208|0.0381 ***|0.0265|0.0405|-0.0898 **|0.6088 ***||-0.1990|-0.0226|
|Estonia|0.2298 ***|-0.0027|0.0458 ***|-0.0372|0.1030 ***|-0.0157|-0.0474 ***|-0.5223|-0.8001 ***|0.0685 **|
|Hungary|0.1414 **|0.0077|0.0211 ***|-0.1182 **|0.0355 **|-0.0724 ***|0.1083 ***|-0.9260 ***|-0.9197 ***|-0.0004|
|Latvia|0.0605|0.0302|-0.0040|-0.0213|0.0745 ***|0.0251|0.2284 ***|-0.2548|-0.8046 ***|0.0994 *|
|Lithuania|0.1691 ***|0.0279 ***|0.0172 **|0.0212 **|0.0345 ***|-0.0009|-0.0132||-0.6783|-0.0082|
|Poland|0.2426 ***|-0.0176|0.0216 ***|0.0821 ***|-0.0263|-0.0613 **|0.5926 ***|0.4126|0.5389 ***|-0.0296|
|Slovak Republic|0.7571 *|-0.0014|0.0362 ***|0.0077|-0.0115|-0.0826 ***|0.5959 ***|0.9146 ***|0.9877 ***|0.0681|
|Slovenia|0.1460 ***|0.0037|0.0377 ***|-0.0075|0.0700 ***|-0.0750 ***|0.0965 ***|-0.7970 ***|-0.9451 ***|-0.1114 ***|
|_EU-15 and Cyprus_|||||||||||
|Austria|0.6515|0.0217|0.0446 ***|0.2535|0.0348|-0.1480 ***|0.3496 ***|0.0000|0.0000|-0.1032 *|
|Belgium|0.5760 ***|-0.0154|0.0425 ***|-0.1560|0.1143 ***|-0.1659 ***|0.2832 ***|-0.9742 ***|-0.9915 ***|-0.0362|
|Cyprus|0.2300 ***|-0.0015|0.0296 **|0.0722 **|0.0078|-0.0043|0.1668 ***|-0.9598 ***|-0.8109 ***|-0.2310 ***|
|Finland|0.8166 ***|0.0088|0.0189 **|0.0480|0.0465 **|-0.0929 ***|0.1383 ***|0.0323|-0.6641 **|-0.0268|
|France|0.8020 ***|-0.0247 *|-0.0129|0.0081|0.1047 ***|-0.0080|0.2099 ***|-0.0027|-0.3105|-0.0855 **|
|Greece|0.2693 ***|0.0431 **|0.0850 ***|-0.0497 **|0.0230|-0.1193 ***|0.1726 ***|-0.7836 ***|-0.7469 ***|-0.0143|
|Ireland|0.8663 ***|-0.0519 *|0.0196|-0.0263|0.0478|-0.2036 ***|0.4784 ***|-0.1067|-0.7416 **|-0.0535|
|Italy|0.5573 ***|0.0207 *|0.0428 ***|0.0060|0.0500 ***|-0.1061 ***|0.1387 ***|0.2681|-0.5568 **|-0.1117 ***|
|Luxembourg|0.7725 ***|0.0541|-0.0212|0.0854|0.0432|-0.0383|0.2832 ***|-0.4913|0.1179|-0.1773 ***|
|Portugal|0.4818 ***|-0.0137|0.0620 ***|-0.0549 **|-0.0073|-0.2329 ***|0.3152 ***|-0.0053|-0.6773 ***|-0.1776 ***|
|Spain|0.6083 ***|-0.0087|0.0717 ***|-0.0360 *|0.0415|-0.1691 ***|0.1051 ***|0.4272|0.1789|-0.1219 ***|
|Sweden|0.0875 *|0.0015|-0.0015|-0.0072|0.0043|-0.0156 *|0.0483 ***|0.8637|0.7084|-0.0091|
|United Kingdom|0.8303 ***|-0.0135|0.0148||0.0886 ***|-0.1324 ***|0.2001 ***|0.9009 ***||0.0376|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

33
non-tariff measures or the compliance costs related to non-harmonized product standards, are notoriously difficult to compile, even for high income OECD countries. But as tariff rates continue to fall in ASEAN as elsewhere, it will become increasingly important to invest resources in assembling these data.

# **4 What Does Southeast Asia Stand to Gain?**

In this Section, we use a standard modeling framework to provide an indication of the possible trade gains for Southeast Asia in pursuing additional trade facilitation reforms. To do so, we will apply the gravity model. According to Leamer and Levinsohn (1995, p.1384), it has produced “some of the clearest and most robust empirical findings in economics”. In sum, the model suggests that trade between two countries is a function of their economic “mass” (usually GDP), and observable factors that impact trade costs between them. The observable factors included in gravity models usually cover distance (to capture the effect of transport costs), geographical and historical connections (such as colonization or a common language), and trade policy factors (such as tariffs).

By applying the gravity model to trade data for Southeast Asia, we can obtain statistical estimates of the sensitivity of bilateral trade flows to changes in various trade facilitation indicators.<sup>10</sup> To do this, we build on the approach of Wilson et al. (2005). The indicators that we consider here cover the following dimensions of trade facilitation: efficiency of maritime and air ports, the extent of irregular payments in relation to export/import licenses, and the level of competition amongst internet service providers (a proxy for regulation of backbone services

> 10 Although the focus of this paper is on ASEAN member countries, we estimate the model using data for all Southeast Asian countries for which we have information. This is to compensate for the lack of data on a number of ASEAN countries, as noted above.

- 12 -
a wide use of this classi fi cation system only started in the mid 1990s. The basic data set consists of an unbalanced panel of imports for 117 countries at the six digit level of the HS for the period 1988-2002. The number of countries obviously varies across products depending on the presence of import fl ows and on the availability of trade statistics at the HS level.

There are three factor endowments included in the regression: labor, capital stock and agriculture land. Data on labor force and agriculture land are from the World Development Indicators (WDI, 2003). Data on capital endowments are constructed using the perpetual inventory method based on real investment data in WDI (2003).

The estimation sample did not include tariff lines where the recorded trade value at the at the six digit level of the HS was below $50,000 per year. This eliminated less than 0.1 percent of imports in the sample, and it is necessary in order to avoid biasing our results with economically meaningless imports. The elasticities are constructed following Equation (11), where the import share is the sample average (i.e., we constrained the elasticities to be time invariant). We also purged the reported results from extreme values by dropping from the sample the top and bottom 0.5 percent of the estimates.

# 5 Empirical Results

To be precise, we estimate a total of 315,451 import demand elasticities at the six digit level of the HS for 117 countries. The simple average across all countries and goods is -1.67 and the standard deviation is 2.47 suggesting quite a bit of variance in the estimates. Figure 1 shows the Kernel density estimate of the distribution of all estimated elasticities. The vertical line to the left denotes the sample mean (-1.67), and the line to the right the sample median (-1.08).

All import demand elasticities are quite precisely estimated. The median t-statistics is around -11.96. Around 89 percent of the elasticities are signi fi cant at the 1 percent level; 91 percent at the 5 percent level and 93 percent at the 10 percent level.

The estimates vary substantially across countries. The top three countries with the highest average elasticity are Japan, United States and Brazil (-4.05, -3.39 and -3.38, respectively). The three countries with the lowest average import demand elasticities are Surinam, Belize and Guyana (-1.02, -1.03 and -1.03, respectively). Table 1 summarizes the elasticities by country providing the 15
such simple forms of governance, it can be costly in terms of foregone profitable opportunities requiring more complex contracting arrangements.

Transactions that involve complex products and substantial quantities, which often require inter-temporal arrangements, such as ordering in advance, invoicing, and supplier credit, face significant difficulties in SSA. First off, there is the problem of high variation in product quality. Low standardization in production in much of manufacturing – producers are often “job-shop” manufacturers, making one-off products, rather than “assembly-line” producers of standard products – and unevenness in skill levels across firms increase the likelihood of quality variation in production. Moreover, the small number of upstream suppliers in the market for many products means that buyers cannot be confident that competition will have ensured the quality of various suppliers. Such factors compel buyers to perform costly inspections of orders. Second, searching out and trying different suppliers is difficult and costly in the SSA environment where information is limited, communication is difficult, and infrastructure is poor. Third, limited information about businesses and consumers, poor communications, and the fact that many small firms do not have fixed business sites, makes it relatively easy for delinquent clients to renege on their accounts. Vetting clients for supplier credit is thus difficult and offering credit is risky. Consequently, supplier credit and invoicing are reserved for transactions with larger, well-known clients.

Legal and judicial systems in the region are plagued by antiquated laws and procedures, insufficient human and material resources, poor management, and corruption.<sup>3</sup> These problems have resulted in extensive case backlogs and long delays, high costs, and a public perception of the legal and judicial system as too costly, unworkable, and corrupt for resolution of most commercial disputes. Of course, the sophistication and quality of legal institutions varies somewhat across countries and there are cases in the RPED surveys where businesses have taken legal disputes to court.

> 3 The Global Competitiveness Report of the World Economic Forum, 2005 ranks the quality of SSA’s business environment at the bottom of the heap.  Its index of “Quality of National Business Environment,” which measures regulatory obstacles and legal obstacles to doing business, ranks the USA 2, India 32, and four countries in SSA we will be examining in this paper Kenya 63, Zambia 73, Zimbabwe 84, and Tanzania 87.

6
sector market structure, the institutional environment, and specific government policies to relax barriers to account use.<sup>25</sup> We include a number of variables to proxy for the cost of opening, maintaining, and using an account. _Cost of Opening a Bank Account_ , _Cost of Maintaining a Bank Account_ , _Cost of Direct Credit_ , and _Cost of Debit Cards_ are all central banks’ assessments of the costs of payment and associated services of these respective categories. All these variables are dummies that take the value 1 if the country’s central bank perceives the costs as medium to high, and 0 if it perceives them as negligible to low.<sup>26</sup> The data come from the World Bank Global Payment Systems Survey (World Bank, 2010). Ex ante, we expect all these variables to have a negative impact on the likelihood of using bank accounts.<sup>27</sup> We also include the dummy variable _Offer Basic or Low Fee Account_ , which takes the value 1 for countries where the government requires banks to offer a basic or low-fee account to low-income clients (CGAP, 2009). We expect this variable to be positively correlated with the use of formal accounts.

Documentation requirements are measured by information collected from regulators on “know your customer” (KYC) requirements to open accounts: (i) proof of identity through government-issued ID, (ii) proof of identity through any ID, (iii) proof of nationality or legal status in country, (iv) proof of address, (v) proof of income, and (vi) proof of employment. We

> 25 Our results excluding country fixed effects are also robust to the inclusion of lagged five-year average inflation. The coefficient is insignificant in predicting account ownership or high frequency of use. The coefficient for savings is significantly negative, but does not affect the significance of our country-level variables. Results available upon request.

> 26 To address our concerns on the subjectivity of these indicators, we compare the data with actual banking costs collected for 58 countries in 2005 (see Beck et al., 2008). Though our proxies for the perception of banking costs are imperfect substitutes, the actual annual fees for checking and savings accounts are positively and significantly correlated with our measures of the costs of direct credit and debit cards.

> 27 Beck et al. (2008) find that the cost of accounts is negatively related to the number of accounts per capita across countries.

20
> reject a null effect for outcome 2 if _p_ 2 ≤ α / ( _n_ − 1 ) , and at step _j_ , reject a null effect for outcome _j_ if and only if null effects have been rejected for all outcomes _i<j_ , and _p j_ ≤ α / ( _n_ − _j_ + 1 ) _._ Hochberg (1988) provides a “step-up” modification of this procedure,

> which rejects null effects for all outcomes _i_ ≤ _j_ if _p j_ ≤ α / ( _n_ − _j_ + 1 ) for any _j=1,2,...n_ .

The disadvantages of these approaches are that the larger the number of outcomes in the family, the smaller the average power for testing each individual outcome. Furthermore, these tests are conservative, as they are based on the assumption of independence between outcomes. This is certainly not the case in our application, where most outcomes within families are very closely related to one another. We therefore follow Katz, Kling and Liebman (2007) in calculating bootstrapped estimates of adjusted p-values using a modification of the free step-down algorithm of Westfall and Young (1993).<sup>31</sup> This approach uses the correlation across test statistics to increase the power for testing each individual outcome.

Table 11 shows the 18 outcomes for which the experimental estimates are significant (with controls added) at the 10 percent level when examined individually. If a researcher were examining migration papers for evidence of a significant impact of migration on, say working age adult BMI, then the p-value of 0.052 is strongly suggestive that migration lowered BMI. However, if the researcher is reading our paper to see what the significant effects of migration are, one should have a lot more caution in interpreting these BMI results – they may just be the outcome observed by chance to be significant among a whole host of health outcomes that are being examined at the same time. In contrast, the adjusted p-value of 0.268 for adult BMI means that if one were to search for an effect among the 12 different working age adult outcomes in Table 8, at least one effect this large would be observed 26.8 percent of the time.

Given the loss of power involved in multiple testing and our small sample sizes, we fix the family-wise error rate at 10 percent. If we were to consider all 62 outcomes as a family, the Bonferroni p-value is thus 0.0016. The only outcomes that are significant at this level are total household size, and the number of adults aged 18 to 45. The Holm and Hochberg and Westfall-Young adjustments do not reveal any other outcomes to be significant. That is we can be very confident that migration lowers the size of the sending household, a none too startling result.

> 31 See Appendix A of Katz et al. (2007) for a detailed description of how this is implemented.

26
and I omit any analysis of farm employment and rural labor markets. Moreover, and partly for this reason, I restrict the analysis to the impact of changes in the prices of agro-manufactures exports, leaving aside the impacts of changes in the export prices of agricultural commodities.<sup>9</sup> Table 1 reports the main trends in three variables of interest: the employment status of the individual, monthly wages, and the level of the price of agro-industrial exports. In the Table, I report data on all workers in all sectors.<sup>10</sup> The sample is restricted to include all “employed” workers, leaving aside the self-employed. Sample sizes of employed, unemployed and inactive individuals of working age are reported as well.

The average wage of unskilled workers (primary education) increased until 1995 and declined afterwards to reach, in 1999, a level comparable to that in 1992. The wages of semi-skilled workers (secondary education) increased until 1996, and then stabilized. In 1999, average wages declined, but were still higher than in 1992. The wages of skilled workers (college education) fluctuated more: they increased until 1995, declined in 1996, and then recovered again until 1998. In 1999, wages are higher than in 1992, indicating positive growth during the period.

The Argentine Statistical Institute provides data on the international prices of Argentine exports of agro-manufactured goods, which include dairy product, beef, oils and fats, and food-processing goods. These export prices show an increasing trend (with fluctuations) from 1992 to 1997, and a decline in more recent years.<sup>11</sup> In the EPH survey, there are three employment categories: employed, unemployed and inactive. The employment categories are defined on a weekly basis—this type of reporting is 15