rafmacalaba/datause-encoder-data huggingface dataset:
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14
depletion.<sup>7</sup> We use this savings measure and further adjust it by subtracting disaster asset losses in the given event, and in order to compare also examine gross and net savings, overall leading to three disaster adjusted savings indicators: (i) gross disaster savings, (ii) net disaster savings, and (iii) genuine disaster savings. We compare these constructs to the savings measures unadjusted for disaster losses. The intuition behind the adjustment is to include the adjustment of capital stock losses resulting from an exogenous disaster shock. In line with World Bank (2006), we conduct standard bivariate regression and do not simultaneously account for other explanatory variables beyond savings measures. Yet, we compose subsamples, such as differentiated by country income groups.

# _Data and estimation procedure_

The analysis is based on observed and calculated savings and the present value of consumption per capita from the disaster year into the future up to 2005, the last year with rather complete information (in constant 2000 USD values). As savings are likely to be affected in the event year, as discussed above, we use reported savings in the year before the event and adjust this for the disaster asset loss, then start with calculating the consumption change in the year of the event up to 2005, the last year of our time horizon. Savings and consumption are reported as per cent of GNI of the year before the event. In fact, this approach is equivalent to assuming that the event happened right at the start of the disaster year.<sup>8</sup> The global disaster sample initially consisted of 168 large natural disaster events during the period 1971-2005, for which about 7,900 events are recorded overall. The sample is based on information from two databases and was compiled by Okuyama (2009) with the threshold for a large event defined arbitrarily by a loss exceeding 1 percent of GDP.<sup>9</sup> One database is the open-source EMDAT database (CRED, 2009) maintained by the Centre for Research on the Epidemiology of Disasters at the Université Catholique de Louvain. EMDAT currently lists information on people 7 Pollution damage in terms of health effects of air pollution may be factored in a next step as well, yet such inclusion does not add to our discussion here, and thus is omitted here, also as there are generally even more data constraints.

> 8 This holds mostly for sudden onset disasters which are instantaneous events occurring over minutes and hours, whereas droughts have a lead in of weeks to months.

> 9 In order to define the “event set” the threshold of stock losses is set as a share (1%) of flow effects (GDP). It would have been more systematic to define an asset based threshold, yet we responded to the larger intuitive appeal of using GDP as a denominator, and the fact that this threshold was also used by other papers in the EDRR working paper series, which we wanted to be in line with.

9
2000-07 compared with 2.9% during 1990-99. Developing countries benefitted from the growing demand in higher income countries while themselves contributing more to world growth than in decades past. World trade grew at an average of 7.1% in real terms during 2000-07,<sup>3</sup> so that in 2007, world trade in goods and services was at 33% of world GDP compared to only 16% in 1990 and 25% in 2000.<sup>4</sup> While the rise of China, and to a lesser extent India, has provided alternative markets for the developing world, and in regions like East Asia, intra-regional trade accounts for a much larger share of total trade than it did decade ago, the US and European Monetary Union countries still accounted for 50 % of world trade in 2007.<sup>56</sup> Moreover, a trade shock to one country, from say, US markets, could still be propagated widely to other trading partners, when _production_ is internationally integrated.

World trade growth was accompanied by large cross-border flows of funds both in terms of _current_ transfers (e.g., workers’ remittances) as well as large capital account movements to fund national savings/investment imbalances. Remittances have boosted expenditures (and imports) in many developing countries rising from US$82.5 million in 2000 to US$ 199 million in 2005 to US$ 334 in 2008. Capital account flows between countries responded to both “push” factors (excess savings looking for high returns/ portfolio diversification) and “pull” factors (such as reforms in the receiving country, changing consumption patterns, better investment opportunities, better financial intermediation and innovation). For most small developing countries that participate in international financial markets, push factors probably played an important role in the years leading up to 2008 when capital flows to all developing countries

> 3 Computations based on the Oct 2009 WEO; world trade grew only 0.3% in volume terms in 2001. In 2008 world trade and growth both were 3%, though world trade has grown faster on average.

> 4  Trade grew in HICs at 13.1% during 2000-07 compared to 5.% during 1990-99, while trade in MICs and LICs recorded a growth of around 23% each during 2000-07 compared with 4.9% and 11.3% respectively during 199099.

> 5 That proportion was   44.72 % in 2000- IMF Direction of Trade Statistics, Dec 2009.

> 6  Brülhart and Thorpe (2000) observe important and similar   development levels in selected east-Asian countries trade along with substantial growing static and marginal intra-industry trade in each case. They also find the changing structure of East Asian trade patterns to be explained by fewer labor adjustment pressures.

3
simulations suggest that cotton prices are likely to rise at most by 28% or 49% respectively. Thus, cotton is likely to contine being an efficient export crop and an efficient import substitute.

However, if the externality damages are hypothesised to be large so that the supply curve is shifted up by 90% (of the prevailing market price), then cotton ceases to be an efficient export and an efficient import substitute, if the supply curve is perfectly elastic. On the other hand, if the supply curve is upward sloping then we need reliable estimates of the demand and supply elasticities before we can estimate the extent of the rise in price and judge the status of cotton as an export and an import substituteia.

Implementationion of IPM and the Impact on Efficiency of Cotton Exoorts and on the Efficiency of Import Substitution On the basis of the average reduction in pesticide use and the increase in yield, referred to in Section VIII, we try to calculate the impact on the costs of cotton production for the district, on average, when IPM techniques are adopted. The Guntur Cotton Report, 1990, gives the average cost of cultivation per hectare for the 18 It must be kept in mind that we have not considered the direct environmental costs of the sorts mentioned in Section IX, in the above discussion. It is possible that these costs are large enough to alter the above conclusions, when we try to internalize them through additional taxation. Research is urgently required to quantify these costs.

In addition, the "second-round" price effect arising because of intercrop substitutions have not been factored in. Again, since these impacts are potentially important, research is necessary to quantify this effect.

86
reported here) following the same IV strategy as in Table 3 show qualitatively similar findings for the impacts of internet penetration on firm real output, labor productivity, and employment.

Second, we explore the impact on firms’ relationships with buyers and input suppliers. Some of the conceptual mechanisms for the gains in firm export performance from internet penetration discussed in Section 2 relate to the internet being a means to reduce information frictions, and hence costs of search and communications with potential and actual buyers and input suppliers. Since transactions in industries that manufacture differentiated products are more communications-intensive than transactions for industries that manufacture homogeneous products, there is likely to be greater potential in the former for the internet to reduce communications costs faced by firms and hence boost exports. We define a dummy variable identifying the 3-digit industries with differentiated products, based on the classification proposed by Rauch (1999), designated in what follows as _Rauch differentiated products_ .<sup>25</sup> We re-estimate Eq. (1) interacting the Rauch differentiated products measure with the provincial number of internet users per capita. The estimated effects on firm export outcomes are shown in columns (1), (3), and (5) of Table 5. The probability of participating in export markets, the ratio of exports to total output, and real exports all increase significantly more for firms in the more communicationsintensive industries - the Rauch differentiated products industries – as provincial internet penetration increases.

To explore heterogeneity of effects on the input side, we identify a measure that captures the degree to which highly specialized and customized inputs are used in an industry’s production process. As we noted in Section 2, the internet can help reduce the costs of communicating and coordinating with a large number of specialized suppliers; but the internet may also be less suitable for conducting transactions for these inputs because price comparisons are harder to make or because sophisticated contracts are needed to address relationship-specific investments. We use the measure of contract intensity proposed by Nunn (2007), which is given by the share of specialized and customized intermediate inputs used in the production of the final good for each 3-digit industry based on a U.S. input-output table, designated in what follows as _Nunn contract intensity_ .<sup>26</sup> We re-estimate Eq. (1) interacting the Nunn contract intensity measure with the

> 25 The Rauch classification is provided at the 4-digit SITC level which is concorded to the Chinese firm-level data at the 3-digit level based on verbal descriptions and on an SITC to ISIC concordance. We focus on Rauch’s “liberal” definition of industries with differentiated products.

> 26 Nunn uses data on the fraction of each input that is not sold on an organized exchange nor reference-priced according to Rauch (1999) to construct the share of intermediate inputs that require specialized and customized business relationships. Nunn’s contract

17
**Table 4 OECD regulatory questionnaire for air passenger transport**

|Weights<br>by theme|Question<br>weights<sup>**a**</sup>|Score|Question|
|---|---|---|---|
|1/2|||**_Entry regulation_**|
||1⁄2*w||**Does your country have an open skies agreement with the United**<br>**States?**|
|||6|No|
|||0|Yes|
||1⁄2*w||**Is your country participating in a regional agreement?**|
|||6|No|
|||0|Yes|
||(1-w)||**Is the domestic aviation market in your country fully liberalized?**<br>**That is, there are no restrictions on the number of (domestic)**<br>**airlines that are allowed to operate on domestic routes?**|
|||6|No|
|||0|Yes|
|1/2|||**_Public ownership_**|
||1|% of share|**What percentage of shares in the largest carrier (domestic and**<br>**international traffic combined) are owned by national, state or**<br>**provincial authorities?**<br>s owned by government/100*6|

> **a** The weight w is the average share of international traffic in total traffic (measured in ‘000 revenue passenger kilometres) for each country.

Source: OECD (2005b).

33
32 Figure 4: The Big Bang. Reform <!-- Start of picture text --> Light Industry GNP<br>E<br>C<br>0 D ~~~~~Heavy Iod.stlv Thime<br>(Al~~~~~~~~~~~~~~a<br>Light Industry - GNP<br>_ 0 KHeavy Indu5sy lime<br>(A)<br><!-- End of picture text --> Figure 5: The Gradual Reform
total value of imports. Hence, part of the puzzle is along the extensive margin. Second, within the existing “traditional” group, the poorest countries have shifted towards or generate new trade with geographically closer partners.<sup>19</sup> In the absence of data on the product composition of new trade partners, it is difficult to know what leads developing countries to choose closer trading partners since it could reflect a change in product composition as new products are initially shipped to close partners. In any case, it is clear from figure 5 that the regionalization of trade is also generated by trade redistribution within the intensive margin.<sup>20</sup> The conclusion from this inspection of the raw data is that the poorest countries have increased their trade share with geographically closer partners which would be expected from gravity theory if the relative trade costs with physically closer partners fell more than trade costs with further-away partners. This could be the case if the closer partners are those who reduced most their barriers to trade. In addition, even though on average partners with zero trade are further away than partners with positive trade, when extending trade to new partners, the poorest countries have selected those countries that are closest. Both patterns are consistent with a minimization of trade costs in a formulation in which distance matters. These patterns could also have resulted from the proliferation of regional trade agreements among the poorer countries.

The next section explores if this increasing elasticity of trade to distance only applies for the poorer countries in the sample after controlling for some of the factors that could alter distance-sensitive trade costs.

# **4. THE PERSISTENT RISING DISTANCE EFFECT FOR LOW-INCOME COUNTRIES**

If the gravity model is an adequate representation of bilateral trade, one should obtain increasing values of the elasticity of trade to distance (the “distance effect”), θ<sup></sup> , over time in the gravity model, but only for developing countries. This is indeed what comes out of the alternative estimates below: repeated cross-sections (each cross-section representing a 5 years average) as in the vast majority of cases reported in the meta19 We checked that the patterns described are robust when we drop alternatively India (included as reporter in the group of the 39 poorest countries and dominates other countries in terms of trade value) and China (included in the 123 partner countries) from the sample. Results of figure 5 are unchanged (available upon request).

20 In a cross-section estimation of trade costs on the number of products exported by a country, Allen and Shepherd (2007) find robust and quantitatively significant positive estimates of the number of product varieties to a battery of trade costs with lower trade costs leading to increases in product varieties.

19
after the tapering talk episode.<sup>16</sup> Reserve accumulation has picked up again since 2014, and as of end April 2015, reserves were at an all-time high of about $ 350 billion.

**Figure 12: Monthly percent change in Reserves**

<!-- Start of picture text --> 30<br>Large monthly percent changes<br>25 in reserves were consistent<br>Monthly changes in reserves were<br>20 with the objective to seek a stable more modest than before, consistent with<br>exchange rate<br>15 increased flexibility of the exchange rate<br>10<br>5<br>0<br>‐5 Reserves were neither<br>‐10 accumulated nor used<br>avtively to modulate<br>‐15<br>exchange rate volatility<br>‐20 during this period<br>% Chg. m-o-m<br>199011 199108 199205 199302 199311 199408 199505 199602 199611 199708 199805 199902 199911 200008 200105 200202 200211 200308 200405 200502 200511 200608 200705 200802 200811 200908 201005 201102 201111 201208 201305 201402 201411<br><!-- End of picture text --> Note: calculated using data from Bloomberg.

How reserve management affects money supply, interest rates and the real economy depends on whether they are sterilized or not. Sterilized intervention by keeping money supply unchanged is unlikely to have an impact on interest rates, investment and growth; but imposes the cost of sterilization on the central bank or the government—known as the quasi fiscal deficit in some parlance. Non-sterilized intervention on the other hand increases money supply, drives the interest rates lower and may have a positive impact on investment on one hand and generate the tendency for the economy to overheat on the other. India has for most part in the past partially, but not fully, sterilized its reserve accumulation.

Looking broadly at the decomposition of reserve accumulation into sterilized and non-sterilized components since 1991, the RBI partially sterilized the accumulation of reserves (through open-market operations as well as increasing the cash reserve ratio). This is 16 One might argue that the increased volatility of the exchange rate is a global phenomenon, and afflicted all emerging markets post 2008. Basu et al (2015) calculated measures of volatility for a number of emerging countries, and observed that besides India no other country experienced a similar increase in volatility. Empirical evidence shows that high exchange rate volatility can distort investment decisions and affect long-term growth, especially in countries with low levels of financial development (see Serven, 2003 and Aghion et al., 2009).

16
**Figure 6. Density of log per equivalent adult expenditure Urban-Rural**

<!-- Start of picture text --> 5 10 15 20<br>log per equivalent adult expenditure<br>Urban Rural<br>.6<br>.4<br>density<br>.2<br>0<br><!-- End of picture text --> Source: QUIBB 2010.

In Figures 7 and 8, we compare the distribution of income across cashew producers and cashew non-producers. Interestingly, cashew producers are poorer than cashew non-producers. In Figure 7, the density of the producers lies to the left of the density of the non-producers. In part, this is because non-producers include urban households, which, as we showed, tend to be richer in general. In Figure 8, thus, we compare cashew producers and non-producers in rural areas only. This comparison thus involves mostly agricultural households that choose to produce cashews visà-vis households that self-select into other crops. In this case, the densities are very much alike, and there is a lot of overlap between them. This indicates that the distribution of well-being is more or less the same across cashew producers and cashew non-producers.

We now turn to the share of cashew income, which is our measure of exposure of different households to the change in cashew prices. The average share derived from cashews is 27.2 percent. This means that, for instance, a 10 percent increase in farm-gate prices would create income gains of 2.72 percent of initial household welfare. In other words, the average BissauGuinean household would be 2.72 percent better-off. Given the reported pass-through rates above, a 10 percent increase in international prices would translate into a 14-percent increase in farm-gate prices, and the average welfare effect would thus be 3.8 percent. The share of income derived from cashews is much higher in rural areas – 43.4 percent – than in urban areas – 8.4 percent. A given cashew price change will thus have a much larger welfare effect on rural areas than on urban areas. This is expected, since cashew farming is an inherent rural phenomenon, but it is nevertheless useful information for our analysis.

25
# Table 11: **Regression Results** – Heterogeous Effects

This table reports heterogeneous treatment effects. Each column displays results from a separate regression. The dependent variable in all regressions is the log of total agricultural investment, averaged over two post-program crop seasons, and all other variables are as previously defined. Estimates are based on the full sample and exclude household controls to rule out potential endogeneity in the investment reponse to debt relief. Panel A.1 reports treatment effects for households above and below the mean value of the survey response to the question “If you were to default on a loan from the following source, how worried are you that you will not be able to borrow from this source in the future?”. Panel A.2 reports treatment effects for households above and below the median of total pre-program debt. Panels B.1 and B.2 report treatment effects for households below and above median revenue in the season prior to debt relief, and total pre-program savings. Robust standard errors, in brackets, are clustered by bank and district. + p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01.

||Model 1|Model 2|Model 3|Model 4|
|---|---|---|---|---|
|_Panel A: Expectation of Credit Constraints_|(1)|(2)|(3)|(4)|
|_≤_median|-0.19***|-0.09|-0.09|-0.09|
||[0.08]|[0.05]|[0.05]|[0.05]|
|_>_median|-0.26***|-0.15***|-0.15***|-0.15|
||[0.08]|[0.07]|[0.07]|[0.07]|
|Obervations|2,519|2,519|2,519|2,519|
|R-squared|0.15|0.13|0.13|0.13|
|_Panel B: Pre-Program Debt, Revenue and Savings_|||||
|Total debt, pre-program|||||
|_≤_median|-0.26***|-0.19***|-0.20***|-0.19***|
||[0.08]|[0.07]|[0.07]|[0.07]|
|_>_median|0.001|0.001|0.001|_{_0.03|
||[0.08]|[0.07]|[0.07]|[0.07]|
|Obervations|2,519|2,519|2,519|2,519|
|R-squared|0.17|0.15|0.15|0.15|
|Total revenue, pre-program|||||
|_≤_median|-0.19***|-0.09|-0.09|-0.09|
||[0.08]|[0.07]|[0.07]|[0.07]|
|_>_median|0.14+|0.21***|0.21***|0.20***|
||[0.08]|[0.07]|[0.07]|[0.06]|
|Obervations|2,519|2,519|2,519|2,519|
|R-squared|0.16|0.12|0.15|0.15|
|Total savings, pre-program|||||
|_≤_median|-0.19***|-0.10|-0.10|-0.07|
||[0.08]|[0.06]|[0.06]|[0.06]|
|_>_median|-0.04|0.06|0.06|0.06|
||[0.08]|[0.06]|[0.06]|[0.06]|
|Obervations|2,519|2,519|2,519|2,519|
|R-squared|0.19|0.17|0.17|0.17|
|Bank*district effects|No|Yes|Yes|Yes|
|Respondent controls|No|No|Yes|No|
|Local linear control fctn|No|Yes|Yes|No|
|Quadratic control fctn|No|No|No|Yes|

42