rafmacalaba/datause-encoder-data huggingface dataset:
poverty line in period j, j= 1 or 2, the percentage of extreme poor households in the first period that escape extreme poverty but still remain poor in the second period is<sup>12</sup> Put differently, while equation (3a) (or 3b) offers the framework of analysis for a two by two transition matrix of poverty mobility, equation (7) provides a corresponding framework for a three by three transition matrix. The former considers two outcome categories for poverty dynamics (i.e., poor and non-poor), and the latter three outcome categories (i.e., extreme poor, poor, and non-poor).

# **III.2. Selection of Estimation Models**

We apply the framework discussed above to data from ESPSs 1 and 2 to construct synthetic panel data. We begin by identifying the potential time-invariant variables available in the two surveys, which include household heads’ gender, age, years of schooling, marital status, disability status, and residence area (i.e., urban or rural). These variables can usually be assumed to be time-invariant if the underlying population remains unchanged over time. One way to test this assumption is to use a t-test for the equality of the means of the same variables in the two survey rounds. We provide test results that take into account the complex survey design of twostage sampling with stratification in Appendix 1, Table 1.1.<sup>13</sup> The assumption of equality of means is satisfied for heads’ years of schooling and residence area but is not satisfied for heads’

> 12 See Dang and Lanjouw (2014) for the full derivations and a related investigation that disaggregates the non-poor population into two groups: those that are vulnerable (but not poor) and those that can be classified as belonging to the middle class.

> 13 This can be straightforwardly done in Stata by first setting the data to incorporate sampling design (use “ _svy_ ” command), and then use a combination of this “ _svy_ ” command and “ _lincom_ ” to obtain estimation results.

13
-7-

<!-- Start of picture text --> Figure JA: DOMESTIC/FOREIGN INT RATES 6MO DEPOTS<br>- sosf^So~AE Lo0oING-DCAN@E bkT<br>0.7<br>0.4<br>0.2<br>0.1<br>tU 1U |92 t 9U4 igug<br>ANNUAL AO E5<br>a USA + TUNxY<br><!-- End of picture text -->

<!-- Start of picture text --> Figure 1B: DOMESTIC/FOREIGN LENDING RATES<br>0.55OWMWOIOaKAC A<br>0.3<br>0. 73<br>a. 7<br>0.I5<br>; 0.53-<br>0.41<br>0.4<br>lUG0 t92 19*4 INS<br>ANUOL A%R5 A lSRAE<br>0 USA TUninRKE<br><!-- End of picture text -->
**Table 4: Effects of Health Care Worker Deaths from Ebola on Under-five Mortality***

||Doctors,<br>Stock<br>pre-<br>Ebola|nurses, &<br>Stock<br>post-<br>Ebola|midwives<br>%<br>change|Under-f<br>Pre-<br>Ebola<br>rate<br>(2013)|ive mort<br>May<br>2015<br>rate|ality rate (<br>%<br>change|per 1,000 li<br>Lower<br>bound<br>%<br>change<br>(95%)|ve births)<br>Upper<br>bound<br>%<br>change<br>(95%)|
|---|---|---|---|---|---|---|---|---|
|Guinea|5395|5317|-1%|101|110|10%|-2%|21%|
|Liberia|1029|946|-8%|71|91|28%|-5%|61%|
|Sierra Leone|1153|1074|-7%|161|191|19%|-4%|41%|

Source: Author calculations based on Ebola mortality data from the WHO,<sup><u>8</u></sup> population and under-five mortality data from World Development Indicators,<sup><u>9</u></sup> and health worker-mortality coefficients from Speybroeck et al.<sup><u>18</u></sup> Note: Data on pre-Ebola stock of health workers is for the most recent years available for each country: 2004 (nurses and midwives) and 2005 (doctors) for Guinea, 2008 for Liberia, and 2010 for Sierra Leone. *Health care worker mortality coefficients in Speybroeck et al. are not statistically significant for under-five mortality.

Table 5 presents estimates for maternal mortality using health care worker mortality coefficients from other studies. All three alternative methods also produce large increases in maternal mortality for all countries. The most comparable effects arise from Method 3, which – similarly to Speybroeck et al. – uses an aggregate coefficient for doctors, nurses and midwives, and produces increases in maternal mortality of 31% in Guinea, 61% in Sierra Leone, and 92% in Liberia. The next most similar estimates arise from Method 2, which uses disaggregated coefficients for doctors, and nurses and midwives, plus an interaction term between them. The smallest estimates come from Method 4, which uses disaggregated coefficients but does not account for an interaction effect. This is a serious limitation because doctors and nurses are likely to be complementary: it is not difficult to imagine that a doctor is more likely to be effective at saving lives when there is a nurse present, and vice versa. Nonetheless, even using this method as an absolute lower bound, health care worker deaths to date would increase maternal mortality by between 12% and 23% across the three countries.

9
Table (G18) Jordanian Neighbor Primary Outcomes Heterogeneity by: Education Level

||(1)<br>Social Attitudes<br>and Perceptions|(2)<br>Economic Attitudes<br>and Perceptions|(3)<br>PolicyPreferences|(4)<br>Altruism to Syrians|
|---|---|---|---|---|
|Treat|-0.222|0.396|0.232|-0.026|
||(0.264)|(0.290)|(0.269)|(0.335)|
|Treat*Neighbor Secondary School or More|-0.163|-0.596*|-0.102|0.373|
||(0.340)|(0.353)|(0.353)|(0.417)|
|Neighbor Secondary School or More|0.184*|0.093|0.142|-0.185|
||(0.110)|(0.103)|(0.090)|(0.151)|
|p-value: T + T*Het|0.03|0.32|0.51|0.13|
|Control Mean|0.67|0.67|0.67|0.67|
|Control SD|0.47|0.47|0.47|0.47|
|N|1102.00|1102.00|1102.00|1102.00|

This table shows heterogeneity analysis of the primary outcomes for Jordanian neighbors, by whether the Jordanian neighbor respondent has secondary education or higher. The outcomes are as defined in table G1. The regressions have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%).

Table (G19) Jordanian Neighbor Primary Outcomes Heterogeneity by: Socioeconomic Status

||(1)<br>Social Attitudes<br>and Perceptions|(2)<br>Economic Attitudes<br>and Perceptions|(3)<br>PolicyPreferences|(4)<br>Altruism to Syrians|
|---|---|---|---|---|
|Treat|-0.382*|0.048|-0.320|0.511|
||(0.208)|(0.265)|(0.215)|(0.328)|
|Treat*Above Median Expenditure|0.086|-0.017|0.832***|-0.401|
||(0.270)|(0.310)|(0.288)|(0.504)|
|Above Median Expenditure|-0.191***|-0.054|-0.266***|0.011|
||(0.074)|(0.074)|(0.067)|(0.114)|
|p-value: T + T*Het|0.12|0.87|0.02|0.71|
|Control Mean|0.50|0.50|0.50|0.50|
|Control SD|0.50|0.50|0.50|0.50|
|N|1016.00|1016.00|1016.00|1016.00|

This table shows heterogeneity analysis of the primary outcomes for Jordanian neighbors, by whether the Jordanian neighbor respondent has above median non-food expenditure per capita. Non-food expenditure was selected as the measure of socioeconomic status since it was collected in the same way in both the abridged and non-abridged versions of the consumption module. It includes monthly expenditures on utilities, water, basic household items, debt repayment, linens, clothing, school fees, taxes, insurance, and phone bills. The outcomes are as defined in table G1. The regressions have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%).

52
# **1. Introduction**

This paper uses cross-sectional evidence to explore how farmers adapt to exogenous environmental factors such as climate and soils. By comparing choices of farmers who face different conditions, the model uncovers how farmers adapt. In this paper, we apply this technique to study how climate affects the crop choice of Latin American farmers. We quantify which crops farmers are likely to choose and how dependent this choice is on climate. Understanding adaptation is an important goal in itself to assist planning by policy makers and private individuals (Smit and Pilifosova 2001). However, understanding adaptation is also important if one is interested in quantifying the impacts of climate change. Forecasts of the impact of climate on agriculture cannot rely solely on how climate affects a specific crop. The forecasts must also capture crop switching. Unfortunately, data limitations make it difficult to study the crop specific impacts of climate change in this paper. However, independent data on the effect of climate on yields of specific crops could be combined with the crop switching results of this study to obtain an overall measure of damages.

Climate impact studies have consistently predicted extensive impacts to the agricultural sector from climate change across the globe (Pearce et al. 1996; Tol 2002). A large set of these studies have focused on the reduction of yields of specific crops in warmer temperatures (Reilly et al. 1996; McCarthy et al. 2001). Because these studies assume that farmers make no changes in crops, these studies predict large yield losses from climate change and therefore large losses in net revenue. Studies that do allow crops to change (Adams et al. 1999; Mendelsohn et al 1994) predict that farmers will move away from crops with low yields and substitute new crops that will perform better in the new climate. Studeis that allow adaptation predict smaller damages. However, empirical analyses of just how much farmers are likely to switch crops in response to climate are rare in low latitude 2
manufacturing and very late implementation. We have confirmed that that all results in the paper hold if excluding Arunachal Pradesh.

We conduct much of our empirical analysis at the state-industry level. Figure 3 repeats Figure 2 at this disaggregated level. The horizontal axis continues to be the timing of each state’s implementation, while the vertical axis documents pre-existing women’s entrepreneurship on a per-worker basis or as a share of activity. For the left two panels on women’s ownership shares, there is again little evidence of a relationship to the sequence of state implementations. The added data variation allows for more precise trend lines, but we continue to reject the hypothesis that the trend lines are different from zero. For the right two panels on new establishments, we do see more evidence that later-adopting states have lower entrepreneurship when each industry is treated separately. These trend lines are statistically significant. On the whole, however, they are very small in size relative to the magnitudes that we will estimate due to the reforms.

As a complement to these levels of activity and shares in 1994, we would ideally be able to examine changes in women’s entrepreneurship and ownership rates over the period immediately before 1994 to check whether there are pre-trends in the data that correlate with the relative timing of state implementations. We are unfortunately not able to conduct this analysis directly because 1994 is the first survey to collect gender-differentiated ownership information. We are, however, able to achieve the spirit of this check for pre-trends in two complementary ways.

First, the 1994 NSSO data include the ages of plants beyond the 0-3 years-old group that we use to define entrepreneurship.<sup>11</sup> One approach to testing for pre-trends is to look at the age structure of women-owned businesses in 1994 by the dates at which states will implement the elections. If there are substantial pre-trends for early adopters in the years immediately before 1994, one might anticipate seeing an abnormal profile for early adopters indicative of a pre-trend in entry levels.

Figure 4 shows that early and late adopters of the political reform have similar plant age profiles for women-owned businesses overall. In each panel, the horizontal axis provides seven age groups for establishments: 0-1 years, 2-3 years, 4-5 years, 6-7 years, 8-9 years, 10-25 years, 11 This age detail was dropped from the questionnaire in later rounds of the survey. 15
> <sup>- 49 -</sup>

industrial injury insurance). The yield curve is delivered to the supervisor from the Danish National Bank, which has contracted with an international investment company to supply the underlying data and technical expertise to simulate the zero coupon bond yield structure.

The yield curve thus obtained includes a small credit risk (reflecting the swap market, i.e. inter-bank credit risk), however not to the extent called for by the insurance industry. The Danish experience gives no clear guidance as to the choice of applying a risk-free rate for discounting or a rate including some credit risk. The very notion of a risk-free rate is rather theoretical, and there is no single risk-free interest rate. In short, the Danish yield curve is close to a risk-free term structure.

The Danish experience shows first and foremost that the discount rate must be uniform across the industry and secondly, that it is possible to estimate a zero coupon bond structure and make it available to the market on a daily basis.

# **L.   Treatment of Risk and Uncertainty**

As mentioned it was possible to apply a standard deduction of five percent from the discount rate in order to take into account risk and uncertainty in terms of cash flows. There was a consensus among the Task Force members that the size of the premium for risk, uncertainty and the value of guarantees should, in principle, be assessed and justified by each company and that the premium should be taken into account in the cash flows, rather than as a deduction in the discount rate. The Task Force members therefore proposed setting a time limit on the possibility of applying the five percent standard deduction.

This proposal has been followed by the supervisor. The new accounting rules do not contain the five percent deduction method - the companies may still notify that the five percent will be applied, but now they must argue in the notification why this is appropriate. Moreover, the deduction does not have to take place in the discount rate, but could be applied to the cash flow, which is to be discounted. This represents a further move toward refining the life insurance accounting rules in the direction of "real" fair values. Whereas the discount rate should be uniform across the industry, this is not the case for the premium for risk and uncertainty.

# **M.   The Traffic Light System**

The traffic lights system was introduced by the supervisor in 2001. The aim was to ensure that companies hold sufficient reserves to cover possible adverse market developments. The requirements were imposed on all life insurance companies and pension funds, not only those exceeding the former quantitative limit (50 percent) on the share of risky assets.

The stress testing is divided into two scenarios: Yellow Test and Red Test. When a company does not fall into one of those two categories, it is deemed to be in a green light
- [9] Grossman, G., Helpman, E., 1995. The politics of free trade agreement. American Economic Review 85(4), 667–690.

- [10] Grossman, G., Helpman, E., 1994. Protection for sale. American Economic Review 84(4), 833-850.

- [11] Hoekman, B. M. and Kostecki M. M. 2001 The political economy of the world trading system. Second Edition. Oxford University Press, Oxford and New York

- [12] Kee, H.L., Nicita, A. and Olarreaga, M., 2004, Import demand elasticities and trade distortions. Policy Research Working Paper #3452, The World Bank.

- [13] Kee, H.L., Nicita, A. and Olarreaga, M., 2006, Estimating trade restrictiveness indices. Policy Research Working paper # 3840, The World Bank.

- [14] McCalman, Ph. 2004. Protection for Sale and Trade Liberalization: an Empirical Investigation. Review of International Economics **12** , 81-94.

- [15] Mitra, D., 1999. Endogenous lobby formation and endogenous protection: a long-run model of trade policy determination. American Economic Review 89(5), 1116-1134.

- [16] Mitra, Devashish, Thomakos, D. and Ulubasoglu, M., 2002. Protection for sale in a developing country: democracy versus dictatorship. Review of Economics and Statistics **84** .

- [17] Mitra, Devashish, Thomakos, D. and Ulubasoglu, M., 2006. Can we obtain realistic estimates for the ’prtection for sale’ model? Canadian Journal of Economics **39** : 187– 210

- [18] Nicita, A. and Olarreaga, M., 2006. Trade, production and protection, 1976-2004. Mimeo, The World Bank (available at www.worldbank.org/trade).

- [19] Sato, K., 1967. A Two-Level Constant-Elasticity-of-Substitution Production Function. Review of Economic Studies 34(2), 201-218.

23
Figure 9: 68 % bands of the GIRF distributions for different trade weights <!-- Start of picture text --> china usa euro japan<br>.3 0.0 .0 0.2<br>.2 -0.4 -.1-.2 0.0<br>.1 -0.2<br>-0.8 -.3<br>.0 -1.2 -.4 -0.4<br>-.1 -.5 -0.6<br>-.2 -1.6 -.6 -0.8<br>-.3 -2.0 -.7-.8 -1.0<br>-.4 -2.4 -.9 -1.2<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>austlia india indns kor<br>.1 .1 0.2 0.2<br>.0 0.0 0.0<br>-.1 .0 -0.2 -0.2<br>-.2 -.1 -0.4 -0.4<br>-0.6<br>-.3 -.2 -0.6 -0.8<br>-.4 -.3 -0.8 -1.0<br>-.5 -1.0 -1.2<br>-.6 -.4 -1.2 -1.4<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>mal nzld phlp sing<br>0.4 .3 0.2 0.0<br>0.0 .2 0.0 -0.4<br>-0.4 .1 -0.2<br>.0 -0.8<br>-0.8 -0.4<br>-.1 -1.2<br>-1.2 -.2 -0.6<br>-1.6 -.3 -0.8 -1.6<br>-2.0 -.4 -1.0 -2.0<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>thai<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>-1.2<br>-1.4<br>5 10 15 20<br><!-- End of picture text --> Note: Please refer to Table A1 in the Appendix for a glossary of acronyms. The lines correspond to 16th and 84th percentiles of the distribution, calculated by the trade weights of 1985 ( _red dash_ ), and that of 2013 ( _blue line_ ).

is expanding through trade relations. We analyzed how and to what extent the Chinese economic fluctuations affect the global economy – the Asia Pacific region in particular. After estimating the GVAR model, we calculated the GIRFs with different trade weight matrices of 1985, 1995, 2005, and 2013 in order to compare the size and timing of the shock propagations. We also calculated the 68% and 90% confidence intervals using the bootstrapping method and tested whether the estimated impacts are statistically significant.

Unlike under the earlier trade structures of 1985 or 1995, a negative shock to the Chinese real GDP has a significant impact on the surrounding countries under the more recent trade structures of 2005 or 2013. When we evaluated the impact using the trade weights of 2013, we found that a negative Chinese GDP shock impacts commodity exporters, such as Indonesia, 33
-16-

# Economic Rate of Return (ERR)

It Is difficult to ensure comparability with regard to the economic rate of return of projects, because the basis for estimating benefits has<sup>not</sup> been consistent during the period under review. Earlier projects showed the benefits as being oetermined by the avoided costs of the next cheapest alternative; nowadays approach Is only used only to demonstrate<sup>that a</sup> particular project Is the least cost alternative. Various attempts have been made to calculate consumer surplus but the general approach established since the mid 1970's has been to use tarifs as the minimum estimate of the economic benefits.

Since SARs and audit reports use the same methodology to calculate the ERR, the ayerage ERR at appraisal and completion can be reliably compared. For projects approved between 1967 and 1982, the average ERR estimated at eppraisal was 14.6% and was calculated at c9mpletlon at 11.7%. More projects (42%) had ERRs 3% lower than forecast while 21% of projects had an ERR 3% higher than forecast.

# Demand Forecasts

The shortfall In ERR can be partlv attributed to over-optimism In load forecasting. During the 1970s there were no serious errors of estimation, since the actual results at completion were only 6% less than forecast. For projects approved In 1978 and later, however, there has been a tendency to overestimate demand by about 20%. Thle trend has continued Into the 1980s as a sample of twenty projects showed actual sales 17% less than forecast. In part, these optimistic load forecasts reflect global energy price Increases and recessionary conditions that were more severe than anticipated.

On a Regional basis, South Asia, EMENA, and West Africa showed particular over-optimism In demand forecasting, as actual sales were 23%, 18%, and 17% less than forecast.
# Rent Sharing **in** the Multi-Fibre Arrangement:

# The Case ot Mexico

# Geolfrey J. Bannisters The World Bank, Washington, D.C.

|.itroduction<br>..|I|
|---|---|
|1. Institutional Arrangements ....................<br>|5|
|1.1 Export Licensing**.................**|**5,.......,....5**|
|1.2 Trade Regimes ..................|5|
|2. Testing for Rent-Sharing ..............................<br>,<br>,<br>,|,<br>,<br>,<br>.<br>.. 10|
|2.1 The Data ...<br>.<br>.<br>.<br>|.10|
|2.2 The Price Differences ...<br>.<br>.<br>..|.<br>.. 11|
|2.3 Testing Differences in Composition .....................|**17**........|
|2.4 Testing for Fixed Differences in Quality ..............................|,<br>.,.,.<br>23|
|Conclusions ...........|29|
|References .............|30|
|Appendix: Data Sources and Calculations .............................<br>,.|,,.,.,,.31|

*This is part of a larger project at the World Bank to examine the effects of quota restriction in the MFA on developing countries. I would like to thank Kala Krishna and Will Martin for helpful comments on ar. earlier draft.
thorough measurement of the current recreational benefits to include in benefit-cost analysis. Welfare measurement would be useful to policymakers tasked with weighing the alternative options of restoring Lake Sevan. Our model and welfare comparison is also useful in this context as Lake Sevan is a single site, with no substitutes, so comparing the two samples is not confounded by alternative sites that may enter into an individuals’ water-based recreation decision. Also, since we are measuring current recreational benefits, we avoid having to predict what the impact improvements would have on expected trip demand.

To estimate benefits by the general population and users of the site, two surveys were conducted – one comprising of 3,358 households across Armenia and the other an interceptor survey of 389 on-site tourists recreating at Lake Sevan.<sup>7</sup> Both were conducted in the year 2000, with the tourist survey during the summer to better capture the high season of annual recreational use at the lake. The household sample was selected and stratified by the 1996 Population Census of Armenia, while the on-site survey relied on tourist interception at the lake.

Annual visitation to Lake Sevan by these two groups is reported in Table 2. Household survey responses indicate that nearly 75% did not visit the lake in the past year, with a sample mean of 0.81 day-trips. The tourist survey, obviously truncated at one as interviews took place at the lake, averaged 3.17 day-trips per year. The average person from the household survey was 44 years old, earned the equivalent of 1,383 USD per annum, had 10 years of formal education, and a household size of 4. The average person from the on-site survey was 36 years old, earned $2,933 USD per annum, had 10 years of education and a household size of 5 (see Appendix I for details).

In Table 2 we also note that the standard deviation of visitation in each sample exceeds its mean, thus we suspect the presence of over-dispersion, and therefore formally test the

> 7 The detailed questionnaires included six major parts: (1) environmental attitudes and perceptions; (2) a Lake Sevan action plan for restoration; (3) contingent valuation questions; (4) socio-economic characteristics; (5) recreational use of Lake Sevan; and (6) interview debriefing questions.  For the purposes of this paper, only sections (4) and (5) are used.

9