rafmacalaba/datause-encoder-data huggingface dataset:
Taken together WITSA and ITU provide US with data on IT and TC investment for an unbalanced sample of 70 countries that also appears in the PWT for the period 1993-2011. About 40 countries have have complete data series for all years from all sources but for many countries (especially for poor countries) the data is much more sparse. Therefore, like in the construction of the PWT capital investment series (Inklaar and Timmer, 2013), we employ two layers of interpolation and two layers of extrapolation. First, we interpolate ”in sample” (1993-2011) missing values in proportion to total WITSA ICT spending where available, and aggregate PWT capital investment otherwise. To facilitate the construction of starting values for our ICT stocks in 1993 we further extrapolate the ”in-sample” investment series backward to 1950 in proportion to aggregate PWT investment and a time trend where PWT investment is available (many countries in PWT only have investment data starting in 1970) and any remaining missing values are extrapolated based purely on a log linear time trend.<sup>18</sup> To compute the stock of ICT and NICT we deflate the resulting investment expenditures (from 1950-2011) using capital specific price indexes as estimated by Eden and Gaggl (2014). We then use a version of the standard (Solow) steady state condition, _Kc,_ 0 = _g_ ̄ _cI_ + _c,δ_ 0 _c,_ 0<sup>, to estimate an initial value</sup> in 1950, where _Ic,_ 0 is real ICT investment in the first period, ̄ _gc_ represents country specific long run growth, and _δc,_ 0 is the depreciation rate in the initial period. We use our extrapolated value in 1950 as the initial value for capital specific investment and use the associated implied constant growth ̄ rate as a proxy for _gc_ . Based on this initial capital stocks we then use the perpetual inventory method separately for IT, TC, and NICT capital and iterate on the following equation: where we assume the following depreciation rates: 31.5% for IT, 11.5% for TC (see Inklaar and Timmer, 2013), and 6% for NICT (as in Eden and Gaggl (2014)).<sup>19</sup> We have experimented with a

> 18 There are three countries for which we use a time trend only to extrapolate backward: Nigeria, Sri Lanka, and Japan. We make this exception because the PWT investment series are poor predictors of ICT investment trends for these countries.

> 19Our choice of mapping the NICT depreciation rate to the estimate in Eden and Gaggl (2014) rather than to the PWT is due to aggregation issues, as the PWT distinguishes between several types of NICT assets with different depreciation rates.

34
# SUMMARY

Uncertainty is an inherent phenomena in global warmning issues. First, there is considerable scientific uncertainty around the magnitude of global warming, and second, even if this problem was resolved, it remains difficult to measure in monetary tenns the benefits and the costs associated with the policies aimed at reducing global warming. Although uncertainty affects the policy-makers' decision to reduce (or not) global warming, such an influence is ignored in most existing studics.

The aim of this paper is to explicitly incorporate the effect of uncertainty on the choice of global warming abatement policies. The approach developed in this paper is related to the emerging literature on investment under uncertainty, and in particular to the option-valuation approach. Our numerical applications will focus on Cline's (1992) analysis of global wanning, but our approach may be applied to a wide range of global warming analyses presented in the literature. We are able to obtain two kind of results. First, we assess whether it is optimal to implement a given strategy of limiting global wanning today or whether it should be postponed, and for how long. Second, we identify the optimal policy to be implemented today, for different levels of uncertainty around the costs and benefits of limiting global warming.
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+ _72 ( a0_ + at Iog(Qs),2<sup>+a</sup> 2<sup>log(Qd)X.</sup> 2<sup>+a,logbidt),</sup> 2<sup>+a. MAV (Alog(qs)),.</sup> 2 + ac4ogber),., + be DUMDutch + 6O Dl + & DZ + 6, D3 - log oen.J + P 1Alog(Qd), + js&log(Qd),fr + 37 Alog(Qd)..2 + 9,alog(Qd), + falog(ber), + , 1 Oalog(ber), + fi,Alog(ber)l2 + Pf 12eIog(ber),"+ + fl13Alog(bidt), + fj 44&og(bidt),1<sup>.</sup> + fi, 5 1Log(bidt),. 2<sup>+Pj</sup> 6<sup>alog(bidt),+</sup> 1 (6) The variables are as defined in equation _(5)_ above, where oer is the auction rate; Q, and q are, respectively, actual foreign exchange supplied and total foreign exchange demanded&% bidt is number of total bids?"; ber is the bureaux or the parallel market exchange rate (the opportunity cost of holding foreign exhange); MAV(6log qs) is the moving average of the monthly variance (t3, t 2, t4 , t) of the rate of change in foreign exchange supply, reflecting supply variability?' DUMDutch is a dummy for the Dutch auction, and DI, D2 and D3 stand for regime shift dummies (defined in section 4.1). The first two bracketed terms give the first and second lagged equilibrium error (the long-run coefficients are the same). The remaining differenced lagged and lead terms represent the transitory dynamic effects on the auction rate. The equilibrium error represent the dynamic effects on the current auction rate of previous periods' departures from equilibrium. For example for a lagged positive equilibrium error (i.e a more depreciated equilibrium rate than the acmal in the previous period), the fundamentals will call for an auction rate increase (depreciation) in the current period.

We employed general-to-specific modelling (e.g. see Gilbert, 1986) when estimating the nonlinear empirical equation, and the results for the four countries are shown in Table 2. The results broadly corroborate the predictions of the theory, in that long-run (static) determinants of the auction rate have theoretically consistent and statistically significant effects. The data lend strong support to the ECM, 2 It is possible dtht the quantiy demanded is collinear with the number of bids, and where 2here is leaning thrugh the use of a reserve price or pre-anonced supply, also the quantity supplied. Under non-stationarity and co-integration, however, these foms of endogeneity should not constitute a problem for consistent OLS estimation of the paraneters of inerest, given the super-consistency of the OLS estimator (section 3.1).

= A positive relationship is indicated by auction theory between the number of bidders and die auction rate (Branaman a a1, 1987). Given that these are integrated price and quantity bids, total demand mnay be a better demand indicator than the. number of bids (renorio. 1993).

2 Variance is not defined for non-statonary variables. Given the departure from normality (skewness and excess kurwsis) found to characteriz auction data (Aron and Elbadawi (1994), skewness and excess kurtosis measures were included in our general equations, as finther proxies for foreign exchange supply uncertinty (these were not found to be significant).
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choice of location as a choice between selling at the market and at the farmgate.

According to Table 1, the median annual quantity sold through all di ff erent marketing channels is about 1 metric tons (mt) and average is about 2.3 mt. The median and average annual sales at village markets are higher than that of other locations. Apart from village markets, there is little di ff erence in the median quantity of sales across di ff erent locations. However, the estimates of standard deviation suggest very large variation in the quantity sold at the farmgate. Among di ff erent crops, about one- fi fth of maize and 48 percent of mangoes are sold at the market. Most of the tomatoes are also sold at the market (79 percent). The average size of quantity sold (about 1 mt, except for tomatoes) shows that these commodities can not simply be carried by the farmers as head-load to the market. This ameliorates concerns that farmers may be making market trips for other personal reasons and carrying crops to the market on the side does not necessarily impose any transaction costs on them. A critical piece of information for our econometric analysis is the state of infrastructure and facilities available at each market location. The India agricultural marketing survey 2005 collected detailed information on the characteristics of, and the di ff erent facilities available in, the markets. Figure 1 shows the commodities traded in the surveyed markets. Majority of the markets trade in fruits, vegetables and grains. Markets are not completely specialized either: only 6 market is found to be trading in one commodity (mostly fruits). Table 2 summarizes the basic characteristics and facilities in Tamil Nadu’s wholesale markets. The wholesale markets in Tamil Nadu are smaller in size and heavily congested compared with the overall sample of all 4 states (World Bank, 2007). As Table 2 indicates, infrastructure and facilities in Tamil Nadu are limited. Apart from having bus stations, police stations, commercial banks, and post o ffi ces, most markets o ff er few facilities. For instance, only 21% of the markets o ff er parking facilities. Only a limited number of the markets possesses any equipment. Because 14
Policy Research Working Paper 10660

# **Abstract**

Disasters affect millions of people each year and cause economic losses worth many billions of dollars globally. Reporting on disaster impacts in research, policy, and news primarily relies on macro statistics based on disaster inventories. The macro statistics suggest that a relatively small share of disaster damages accrues in Africa. This paper, instead, uses detailed survey micro-data from six African countries to quantify disaster damages in one key sector: crop agriculture. The micro-data reveals much higher damages and more people affected than the macro statistics would indicate. On average, 36 percent of the agricultural plots in the sample suffer crop losses due to adverse climatic events. In the countries and time period analyzed, these losses reduced total crop production by an average of 29 percent. Importantly, many of these losses are underreported or undetected in key disaster inventories and therefore elude macro statistics. In the case of droughts and floods, the economic losses recorded in the micro-data are $5.1 billion higher than in the macro statistics, affecting 145 million to 170 million people, more than four times as many as the macro statistics suggest. The difference stems mostly from smaller and less severe but frequent adverse events that are not recorded in disaster inventories.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pwollburg@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
represent only a few days to a couple weeks’ worth of consumption. One way to see this is to compare average total 2-week purchase quantities with _qmin_<sup>_∗_,theminimumquantity</sup> required to pay the lowest unit price. Because price schedules are estimated at the district level, there are up to seven possible values of _qmin_<sup>_∗_(andtheassociatedexpendiurelevel,</sup> _e_<sup>_∗_</sup> _min_<sup>)foreachitem.Table6showsthemean,minimum,andmaximumvaluesofthesetwo</sup> statistics, across the seven districts. In columns 3, 4, 6, and 7 we see substantial spatial variation in the minimum and maximum of _qmin_<sup>_∗_and</sup><sup>_e∗_</sup> _min_<sup>—whatitmeansto“buyinbulk”</sup> varies across districts. Column 1 shows the mean quantity purchased over 2 weeks. Among households that purchase each item, the mean quantity purchased substantially exceeds the average value of _qmin_<sup>_∗_.</sup> To estimate the number of days’ worth of consumption represented by _qmin_<sup>_∗_, we divide</sup> _qmin_<sup>_∗_by the average total quantity purchased (at the district level), and multiply by 14.Figure</sup> 4 shows the histogram of results for the item-district pairs that exhibit bulk discounts (if we include the other items the distribution shifts significantly left). The median is 8.51 days. In half of cases, “bulk purchasing” involves buying roughly a week’s or less worth of consumption. The 75th and 90th percentiles are at roughly 2 and 3 weeks’ worth of consumption. Hence, even though the data are from 2-week transaction diaries, for most items the relevant time frame for storing and consuming a bulk purchase is substantially shorter. Bulk purchasing in this setting involves avoidance of very small quantities, rather than stocking up on several weeks’ or months’ worth of an item.

# **Liquidity constraints**

A natural hypothesis for the financial losses in our data is that people would like to take advantage of bulk discounts, but they lack the liquidity to do so. Prior work has argued that poor households may pay higher unit prices than wealthy households because binding liquidity constraints prevent the poor from buying in bulk. This is an intuitive idea. Credit constraints are well understood to be an important barrier to investment in low-income countries, so it is reasonable to wonder whether they are a binding constraint here.

For a liquidity constraint to drive small-quantity purchasing, a household must be unable to gather the cash needed to buy _qmin_<sup>_∗_,theminimumquantityrequiredtobuyat</sup> 19
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per pupil by a factor of 1.18 via a reduction in the pupil-teacher ratio.<sup>29</sup> The ratios imply that about 52.7 percent of the extra resources available in country B are used to increase education quantity, and the remaining 47.3 percent to improve quality.

_Comparing countries with per capita GNP from $200 to $10,000._ Following the same procedure as above, we can compare pairs of countries spanning the full range of per capita GNP, from $200 to $10,000. The results relating to differences in resource availability appear in table 7.30 Looking down column three, we note that in all the five pairs of countries, resource availability for primary education in the richer country exceeds that in the poorer country by a factor of between 1.32 and 1.46. In other words, at every stage of per capita income growth across the entire range from $200 to $10,000, substantial resources do become available for primary education.

**Table** 7: **Comparing resource availability for primary education in pairs of countries at selected levels of per capita GNP, 1993**

|Per cap<br>,|ita GNP<br>M|Resource<br>availability<br>|P<br> <br>|ercentage co<br>Increase in p<br>|ntribution to<br>ublic spendi<br>|country B's<br>ng<br>|advantage o<br>Easing of<br>|ver country<br>Decline in<br>|A<br>|
|---|---|---|---|---|---|---|---|---|---|
|Country<br>A|Country<br>B|in B<br>relative to<br>A|Overall<br>govt.<br>budget<br>in GNP|Share of<br>education<br>in govt.<br>budget|Share of<br>primary in<br>education<br>budget|Primary<br>education<br>budget in<br>GNP|demo-<br>graphic<br>burden|relative<br>teacher<br>salaries|All<br>sources|
|200|400|1.43|0.0|33.0|-13.2|19.8|17.3|62.9|100%|
|400|800|1.39|6.6|20.5|_-15.3_|11.8|19.6|68.6|100 %|
|800|1,500|1.32|18.9|8.8|-13.8|13.8|19.3|66.9|100 %|
|1,500|3,000|1.32|31.3|0.0|-16.9|14.4|23.4|62.2|100%|
|3,000|10,000|1.46|51.4+-|-16.1|-22.9|12.3|31.9|55.8|100%|

Source: authors' calculation-b-ased on data in table 5.

Increased government spending on primary education contributes between 12 and 20 percent of the increase (see column 7), reflecting the expansion of overall government budgets and increased allocation to education as a whole. The former factor becomes

> 29 Note that the product of these ratios, 1.43 (=1.21 x 1.18) is the same as the product of the ratios on the left hand side of the equation.

> 30 The first row relates to the data for the two countries at the bottom of the per capita income scale-$200 and $400 respectively-that were already examined in detail above; they are included here for completeness.
|**Table 5.**<br>**Individual and**|Expenditures a<br>**Household Outcom**|nd<br>**es**||Decision-|
|---|---|---|---|---|
||Assets<br>|CO Cohesion<br>|Outlook on Life<br>|Making<br>|
||(1)|(2)|(3)|(4)|
|**Panel A: Intent to Treat Effect**|||||
|Business Training (1=Yes)|0.079***|0.090***|0.100***|0.096|
||(0.024)|(0.027)|(0.030)|(0.094)|
|Lottery Winner (1=Yes)|0.043|0.048|0.095***|-0.024|
||(0.030)|(0.033)|(0.036)|(0.133)|
|BT and LW|0.061*|0.082**|0.141***|-0.031|
||(0.032)|(0.038)|(0.041)|(0.136)|
|R-Squared|0.33|0.02|0.08|0.03|
|P-value of t - test of ...|||||
|BT = BT and LW|0.55|0.82|0.26|0.32|
|LW = BT and LW|0.61|0.44|0.28|0.97|
|BT = LW|0.27|0.27|0.89|0.38|
|**Panel B: Intent to Treat Effects with Gender Interactions**|||||
|Business Training (1=Yes)|0.109***|0.096**|0.045|0.146|
||(0.032)|(0.038)|(0.042)|(0.144)|
|BT x Female|-0.067|-0.016|0.112*|-0.100|
||(0.047)|(0.051)|(0.059)|(0.191)|
|Lottery Winner (1=Yes)|0.075*|0.054|0.070|0.053|
||(0.039)|(0.041)|(0.047)|(0.191)|
|LW x Female|-0.073|-0.014|0.049|-0.172|
||(0.055)|(0.063)|(0.068)|(0.241)|
|BT and LW|0.143***|0.131**|0.136**|-0.080|
||(0.043)|(0.056)|(0.053)|(0.190)|
|BT and LW x Female|-0.184***|-0.111*|0.007|0.116|
||(0.057)|(0.066)|(0.074)|(0.243)|
|R-Squared|0.33|0.02|0.08|0.03|
|P-value of t - test of ...|||||
|BT = BT and LW|0.37|0.50|0.06|0.20|
|LW = BT and LW|0.17|0.22|0.24|0.55|
|BT = LW|0.43|0.4|0.62|0.64|
|BT + BT x Female = 0|0.22|0.03|0.00|0.71|
|LW + LW x Female = 0|0.96|0.44|0.02|0.46|
|BT and LW = BT and LW x Female = 0|0.32|0.62|0.01|0.83|
|Mean of dependent variable among controls|0.05|-0.06|0.02|-0.10|
|N. Observations|3494|3494|3494|3494|

Note: The reported mean of the dependent variable is computed using CO members not offered business trainingnor assigned to be lottery winners.The dependent variables are aggregates of standardized z-scores. See Appendix B for a definition of the aggregates. All regressions are estimated using OLS methods and include as covariates the stratification variables (eligibility for loan lottery, business ownership at baseline and gender). Standard errors reported in parentheses are clustered at the CO level. The following symbols *, * * and ** * denote significance at the 10 , 5, and 1 percent level, respectively.
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