rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 20<br>19<br>18<br>a<br>& 17<br>So<br>S 16<br>N<br>2 15<br>><br><!-- End of picture text --> <!-- Start of picture text --> 14<br>13<br>12<br>2008 2009 2010 2011 2012 2013 2014<br>—@®— expenditure per capita<br>—@®— expenditure per capita, excluding durables<br>—®— expenditure per capita, excluding durables and health<br><!-- End of picture text -->
**Figure 7: Number of Check Points in Selected Figure 8: Time Spent at Check Points as % of Routes Total Travel Time**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> <!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> <u>Source: NBS (2011) Source: NBS (2011)</u> Source: Authors’ elaboration based on NBS (2011).

Market conditions typically matter for integration and price differences. Among all regions in South Sudan, Jonglei (containing Bor) is the largest region, while Northern Bahr el Ghazal (containing Aweil) is the smallest one (Table 5). In terms of cereal production, Kajok shows the highest, while Bentiu the lowest. When taking into account cereal requirements, the cereal deficit is larger in Bor than in the rest of the regions, whereas Wau, in the fertile Greenbelt, presents the smallest deficit. The Consumption and consumption per capita are highest in Wau and Malakal, respectively, and on the other side stand Wau and Aweil as the lowest regions for the mentioned indicators, respectively.

17
a child) falls ill. Similar responses are observed on days just after a woman recovers from the symptoms of an STI (which arguably might be seen as an exogenous shock to her ability to supply sex). Women do this in order to capture the roughly 42 Kenyan shilling (US $0.60) premium for unprotected sex and the 77 shilling (US $1.10) premium to anal sex.

Our results are related to a number of other studies of risk-coping mechanisms in poor countries, especially since the women we study make up a sizeable fraction of the population of adult women in the area. Like most people in developing countries, the women in our sample lack access to formal credit or savings, and the informal risk coping mechanisms that are typically available to people (such as informal insurance systems of gifts and loans between friends and family) have consistently been shown by other authors to be ine¢ cient in insuring risk (i.e., Townsend, 1994; Paxson, 1992; Gertler and Gruber, 2002). The increase in transactional sex we ...nd is similar to the labor supply e¤ect documented by, for instance, Kochar (1995, 1999), though our results are of independent interest because it comes at such a high cost: HIV prevalence has been estimated at 9.8% in Busia District (Central Bureau of Statistics, 2004). Over time, the increases in risky sex that we observe here have enormous health consequences for these women, their sexual partners, and society as a whole as HIV is passed on to the general population. These results are all the more striking because we focus on daily shocks, rather than larger shocks such as annual or seasonal ‡uctuations in agricultural income.

This paper is one of the few studies to identify and document speci...c costs to ine¢ cient consumption smoothing, beyond consumption ‡uctuations themselves. Other examples include Rosenzweig and Wolpin (1993), who show that Indian farmers are forced to use productive assets (bullocks) to smooth consumption, incurring substantial reductions in long-term productivity in the process, and Jacoby and Skou...as (1997), who ...nd that Indian households pull their children out of school to work on the farm when shocks occur, which may reduce the long-term earnings potential of their children. Our study also highlights the di¤erence between income smoothing and consumption smoothing, as discussed in Morduch (1995). Empirically, consumption by the women in our sample is relatively insensitive to health shocks, so that standard tests would conclude that women are well-insured against these shocks. Such tests do not explicitly account 2
- 42 -

Table 16: Mean Percentage of Total Instruction Time Devoted to Selected Subject in the Primary School Curricula of LDCs by Predicted GNP Growth Rate and Primary Enrollment Rate (PER), 1960s and 1980s* (Constant Cases)

||Pos.<br>High P|GNP<br>ER|Neg<br>High|. GNP<br>PER|Pos.<br>Low|GNP<br>PER|Neg<br>Lo|. GNP<br>w PER|
|---|---|---|---|---|---|---|---|---|
||(N=1|7-19)|(N|=6-7)|(N=7|-8)|(N|=5-6)|
||D190|l90|1960|1980|15|1980|196Qs|1980|
|Comb.Lang.|34.1|30.7|35.1|35.9|34.6|33.1|32.9|36.0|
|Nat.Lang.|25.1|24.8|17.4|16.2|16.6|18.1|12.3|14.7|
|<br>Loc.Lang.|2.6|0.3|2.2|7.7|2.0|1.7|0.8|0.0|
|<br>For.Lang.|0.6|1.8|0.4|2.1|2.7|2.5|0.0|1.4|
|Off.Lang.|5.3|4.1|15.0|9.9|13.3|10.7|19.8|19.9|
|Mathematics|16.3|17.1|16.0|18.5|15.8|17.6|13.2|18.3|
|Science|7.9|8.9|7.0|6.1|7.9|10.9|7.1|7.9|
|Comb.Social Sci.|9.0|9.2|7.9|6.7|6.6|7.7|8.3|9.6|
|Social Studies|4.4|6.3|0.0|4.2|4.1|5.4|3.0|7.1|
|History|1.9|1.0|3.4|1.2|0.9|0.8|1.9|0.8|
|<br>Geography|2.0|1.0|3.0|1.0|1.0|0.8|0.5|1.2|
|<br>Civics|0.7|0.9|1.5|0.4|0.7|0.7|2.9|0.5|
|Coomb.MoraI/Re|l. 6.2|6.6|3.1|4.1|5.9|4.0|5.6|3.0|
|Religion|4.0|3.8|2.2|3.1|6.0|5.2|2.9|2.1|
|<br>Moral Ed.|2.0|2.3|0.9|1.1|0.6|0.0|2.6|0.9|
|Aesthetic Ed.|10.3|9.3|10.4|9.5|12.5|10.7|8.6|9.3|
|Music|4.4|3.6|3.9|4.5|4.5|4.2|3.2|3.8|
|Art|5.9|5.7|6.5|5.0|8.1|6.5|6.0|5.0|
|Physical Ed.|7.1|5.2|6.2|7.7|8.1|7.1|5.3|5.5|
|Hygiene|1.4|2.3|3.3|0.7|2.6|1.2|0.9|2.1|
|Comb.Voc.Ed.|7.7|7.1|6.3|7.1|4.3|5.0|16.3|8.5|
|<br>Manual Training|<br>2.1|<br>1.6|<br>2.6|<br>4.0|<br>1.5|<br>1.8|<br>5.9|3.9|
|<br>Agriculture|2.8|2.2|1.5|2.1|1.3|1.3|6.3|2.1|
|<br>Domestic Scienc|e 2.9|2.2|1.6|0.8|0.9|1.4|4.1|2.5|
|Vocational Ed.|0.5|0.8|0.0|0.0|0.5|0.5|0.0|0.0|
|Business|0.0|0.1|0.0|0.0|0.0|0.0|0.0|0.0|
|Other|1.0|4.1|6.3|3.0|1.0|1.4|1.4|1.4|

*Key: Pos.GNP = Positive GNP Growth Rate; Neg.GNP = Negative GNP Growth Rate
basis, covering the universe of commercial banks, as well as all regulated non-bank financial institutions, such as _Cajas Municipales_ , _Cajas Rurales_ and microfinance institutions.<sup>14</sup> For each ID number in any given month, we obtained the total amount borrowed from each SBS supervised financial institution in Peru. The total amount borrowed is disaggregated into eight different loan types: microloans, loans to small firms, loans to medium firms, loans to large firms, loans to corporations, non-revolving consumption loans, revolving consumption loans, and mortgages. If a borrower has more than one loan of the same type with the same intuition, Equifax reports only the sum of these loans, with no information on how many loans constitute this total amount. Given our sample of SME loan applicants, most loans correspond to consumption credit and SME loans (which include loans to micro, small and medium-sized firms), with 78% and 84% of the SMEs in our sample having these types of loans. We drop information on loans to large firms, corporations and mortgages. Less than 1% of the SMEs in our sample have loans to large firms or corporations, and about 8.5% have mortgages. We keep information on consumption loans to examine whether loan applicants substitute SME loans for consumption loans.

Equifax also calculates credit scores for consumer loans, microfinance loans, and business loans. Our partner bank used the microfinance loan score in their conventional screening method. Equifax staff also advised us that for SMEs, the microfinance loan score would be the most relevant one. We thus purchased the microfinance loan score for two points in time (i) the month when the SME applied for the loan with our partner bank, to be used as a background variable, and (ii) April 2016, to be used as an outcome variable. For the credit score in the month when the SME applied for the loan, Equifax included a dummy variable indicating whether this score was primarily based on their credit history, i.e. a “thick file,” or on demographics and other sources, such as the Peruvian tax authority (SUNAT), i.e. a “thin file”.

To measure loan use based on the Equifax data, we created a dummy equal to one if either the DNI or RUC associated with a loan applicant shows an increase in the amount outstanding from a given financial institution for a given loan type (where the increase could be either from an amount of 0 if the applicant did not already have this type of loan from this financial institution or otherwise an increase from a positive amount). We use four different dummy variables corresponding to the following loan types: (i) SME loan from any financial institution (including

> 14 For any given month, these institutions report information on their loan portfolio on the 23rd of the following month, so that data are available with about a two month time lag.

10
|program (percentage points)<br>g if aware and part of PBF<br>(6)<br>Across<br>responses<br>2.20<br>(2.28)<br>3.75<br>(2.41)<br>3.39**<br>(1.67)<br>0.33<br>(2.53)<br>-1.95<br>(2.64)<br>56.78***<br>(1.55)<br>Yes<br>PFP<br>531<br>0.058<br>ove-median number of correctly|
|---|
|nding of PBF <br>Understandin<br>(5)<br>Across cases<br>2.08<br>(4.30)<br>4.41<br>(3.96)<br>1.23<br>(2.75)<br>2.49<br>(4.66)<br>-0.51<br>(4.35)<br>55.80***<br>(2.63)<br>Yes<br>PFP<br>531<br>0.043<br>ng defned as ab|
|and understa<br>tanding<br>(4)<br>Across<br>responses<br>2.15**<br>(0.88)<br>2.65***<br>(0.88)<br>2.17**<br>(0.88)<br>0.64<br>(1.29)<br>-0.50<br>(1.29)<br>56.86***<br>(0.70)<br>Yes<br>DFF PFP<br>1,182<br>0.044<br>igh understandi|
|NSHIP pilot <br>Unders<br>(3)<br>Across cases<br>2.94**<br>(1.49)<br>3.29**<br>(1.45)<br>0.48<br>(1.48)<br>2.35<br>(2.13)<br>0.95<br>(2.13)<br>55.30***<br>(1.17)<br>Yes<br>DFF PFP<br>1,182<br>0.031<br>t worker level. H<br>ian = 6).|
|participates in<br>eness<br>(2)<br>Across<br>responses<br>1.81*<br>(1.06)<br>1.43<br>(1.05)<br>1.45*<br>(0.87)<br>0.96<br>(1.33)<br>1.33<br>(1.32)<br>56.78***<br>(0.78)<br>Yes<br>DFF PFP<br>1,182<br>0.042<br>; s.e. clustered a<br>tervention (med|
|that facility <br>Awar<br>(1)<br>Across cases<br>2.96<br>(1.92)<br>2.75<br>(1.98)<br>0.11<br>(1.58)<br>1.64<br>(2.31)<br>1.41<br>(2.34)<br>55.42***<br>(1.49)<br>Yes<br>DFF PFP<br>1,182<br>0.029<br>ted OLS models<br>ivized in PBF in|
|Interaction with awareness<br>at facility is part of NSHIP<br> Aware<br> Aware<br>erstanding<br> High understanding<br> High understanding<br> (%)<br>rms<br>dents<br>d<br>* p_<_0.05, *** p_<_0.01. Unadjus<br>th care services that are incent|
|Table A.7: <br>Reward<br>Penalty<br>Aware th<br>Reward _×_<br>Penalty _×_<br>High und<br>Reward _×_<br>Penalty _×_<br>Constant <br>State FE<br>NSHIP a<br>N respon<br>R-square<br>* p_<_0.10, *<br>named heal|

53
- 31 -

Table 11. Costs of Unskilled Labor in Textile Industry <!-- Start of picture text --> Region/Country Costs (US$ per hour)<br>1. OECD<br>Germany 25<br>United Kingdom 13<br>United States 14<br>'2. Non-OECD Europe<br>Hungary 2.4<br>Turkey 2<br>3. Asia<br>China 0.55<br>India 0.65<br>4. Africa<br>Malawi 0.52<br>Mauritius 0.95<br>South Africa 2.35<br>Zambia 0.95<br>Zimbabwe 0.5<br><!-- End of picture text --> Source: Muradzikwa (2001)
<!-- Start of picture text --> 2.018Te<br>16<br>B14z<br>2 3 3 104:2<br>0.8<br>206Eo4=<br>s |<br>0.20.0 |+ | 1 1 T T T 1<br>2004 2005 2006 2007 2008 2009 2010<br>l Marketing Companies Government Off-Budget m Profits on crude oil and gas production<br><!-- End of picture text -->

<!-- Start of picture text --> a 25% ——<br>©x 20% j<br>rsw@ 15%<br>2a ae9,<br>2 5%<br>=E 0%<br>2006 2007 2008 2009<br>subsidies, 2.30% 2.10% 1.80% 1.50%<br>W Implicitelectricity a _—-<br>U1 Impliciteegas subsidies JohS eae80% Ee90% 820890%<br>@ Implicit oil subsidies 11.9% 10.9% 11.2% 11.9%<br><!-- End of picture text -->
60 FIGURE 2b <!-- Start of picture text --> NET TRANSFERS SHARE OF GNP (1978-1982)<br>VOUNr ACCES VS. PM~ma. S<br>::a A<br>0024 OLNEr TRANW 1CSNP<br>0.0=F<br>0.02<br>0.016<br>0.016 TAL PEr PR%91E T<br>0.014 TRANSFERSALN P 1983-198 L /)<br>0.012-<br>0.01<br>0.0065<br>0.0065<br>0.004 -<br>0.002*<br><!-- End of picture text --> = V.LINTM AC£SS PROlEM OETOUR!

<!-- Start of picture text --> NET TRANSFERS SHARE OF ONP (198,3-1985)<br>o.a)15 V~'OLLi4AMY AOES VS. PFVMMJ DraWs<br>0-<br>-0.005<br>ALPET P%TlE T<br>-0.01<br>-0.015<br>-0.02-<br>-0.025<br>-0.0.3-<br>-0.04-<br><!-- End of picture text --> vauLNrARf? ACcEES wiotruZ CriwF
<!-- Start of picture text --> 3<br>=5S3<br>oS<br>aa28<br>a52<br>°<br>2014<br>HE Customs duties [MEE Other taxes<br>HE «Domestic VAT «=—--HY-CDiirecct taxes<br>Es Import VAT<br>Note: Impart duties on potroloun products arw pat of customs duties<br><!-- End of picture text --> <!-- Start of picture text --> ooES-<br>G<br>a3F<br>3<br>go I<br>Z<br>é<br>$8<br>2 a<br>Be=<br>é<br>5<br>HEE Import duties on petroteum products HE Customs duties<br>— VAT on petroleumroleum productsproduc! MS Importimp VAT<br><!-- End of picture text -->
Figure 1: Distribution of actual payments and of first offer in referendum <!-- Start of picture text --> 0 200 400 600 800 0 200 400 600 800<br>Approximate payments per ha (in pesos) First offer in referendum (in pesos)<br>.02 .02<br>.015 .015<br>.01 .01<br>Density Density<br>.005 .005<br>0 0<br><!-- End of picture text --> Left panel shows the distribution of payments per hectare for communities in the sample. Values for beneficiaries are the actual payments received. Values for non-beneficiaries were calculated from program rules. The right panel shows the distribution of the offers in the referendum question for the leaders.

23
<!-- Start of picture text --> 60<br>e ® Cuts in Tightening Phases<br>x * Hikes in Easing Phases<br>e x<br>40 coe<br>@<br>e ee x x<br>eee @ x @<br>e See @x<br>30 e x x X@@Ox, x<br>o> © ee mE xX aK<br>e e® == © @x@e xex<br>©@ Bo © o@x ===x x x<br>oox= 9 =M@xK @&eKR WE@m=REE££) KEKEX=EZEZOU UDKCUCUCUXG@O= @O xxxt me= XKK xxmKX<br>fe) ee<br>1975 1980 1986 1991 1996 2002 2007 2013 2018 2024<br><!-- End of picture text -->
<!-- Start of picture text --> Business Closure Rate vs Gross Domestic Product (2019)<br>(47 countries)<br>Ss<br>°<br>==°°<br>fo) ° 0 COBE! ° ° o<br>°<br>~ °<br>$s 9°<br>s - °<br>aT ° °<br>a<br>Bs se o ° is fe]<br>Se ° ;<br>4 ° oR fo)<br>°<br>°<br>oo ° °<br>fe) °<br>° °<br>°<br>oO °<br>~<br>0 5 10 15 20 25 30<br>% of Businesses Closed during COVID-19<br>Correlation Rho: -.62<br>Sample: Countries in 6 waves<br>Source: WBI (2019) data and Future of Business (2020)<br><!-- End of picture text -->
# **Table** 5: **Cost-of-Livine Index differentiated by Socio-Economic Group and Poverty Status**

||Abidjan|Other<br>Cities|East<br>Forest|West<br>Forest|Sarah|
|---|---|---|---|---|---|
|Export Crop Farmers|(100.0)|88.0|87.4|78.2|70.2|
|Food Crop Farmers|(100.0)|88.9|87.8|76.7|75.6|
|Public Sector Employees|100.0|93.7|83.7|(96.5)|(80.1)|
|Formal Private Sector Employees|100.0|94.6|(88.8)|(84.0)|(77.5)|
|Informal Private Sector Employees|100.0|92.7|(81.3)|(75.0)|(118.1)|
|Self-Employed|100.0|93.1|83.2|85.4|80.6|
|Unemployed|100.0|(97.7)|-|-|-|
|Inactive|100.0|92.3|88.2|(83.5)|(79.5)|
|Very Poor|100.0|86.3|99.7|93.1|77.0|
|NMid-Poor|100.0|90.9|89.7|78.3|76.1|
|Non-Poor|100.0|93.1|85.9|78.1|75.8|
|C6te d'lvoire|100.0|92.8|87.0|78.2|76.0|

Note: The very poor are the bottom decile of individuals ranked by per capita expenditure, and the mid-poor are the 2nd and 3rd decile. Numbers in parentheses indicate cells with less than 10 observations.

# **6. Conclusion**

Regional price variations can make a big difference to estimates of poverty. It is important, therefore, that analysts use the best and most comprehensive combinations of available information sets in constructing these indices. We have shown how the CILSS and 14
Table 5: Effects of forecast errors and forecast growth on worker compensation, high vs low earners, according to managerial skill (managers defined by occupational category)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. variable:|||log mont|hly wage|||
|High vs. low earners|high|low|high|low|high|low|
|Definition|5%|95%|15%|85%|25%|75%|
|A. Firms with high-skilled managers|||||||
|Weighted forecast error|0.0435***|0.0057|0.0359**|0.0067|0.0288**|0.0044|
||(0.0159)|(0.0068)|(0.0139)|(0.0061)|(0.0133)|(0.0061)|
|Weighted forecast growth|0.0191**|0.0011|0.0130*|0.0009|0.0088|-0.0009|
||(0.0083)|(0.0042)|(0.0072)|(0.0043)|(0.0066)|(0.0052)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|6226|6226|6226|6226|6226|6226|
|N (firms)<br>|2739|2739|2739|2739|2739|2739|
|Adj. R<sup>2</sup>|0.0242|0.0366|0.0276|0.0440|0.0293|0.0433|
|RSS|447|80|292|71|236|66|
|B. Firms with low-skilled managers|||||||
|Weighted forecast error|-0.0028|-0.0013|-0.0147|-0.0044|-0.0139|-0.0041|
||(0.0131)|(0.0065)|(0.0111)|(0.0065)|(0.0106)|(0.0065)|
|Weighted forecast growth|0.0040|-0.0034|-0.0044|-0.0034|-0.0039|-0.0028|
||(0.0094)|(0.0040)|(0.0080)|(0.0039)|(0.0077)|(0.0039)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|6478|6478|6478|6478|6478|6478|
|N (firms)<br>|3015|3015|3015|3015|3015|3015|
|Adj. R<sup>2</sup>|0.0383|0.0376|0.0314|0.0419|0.0326|0.0466|
|RSS|406|72|276|66|224|63|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
_ν_ = 1 and assign arbitrary values to _η_<sup>_i_</sup> .

For the simulations, we use a multiple shooting algorithm similar to Lipton et al (1982), but one can use other shooting methods instead.

We consider four simulation exercises: In Simulation I, we simulate the model around steady state<sup>13</sup> . Then, we estimate the model using 26 years of simulated data.

In Simulation II, we drop the manufacturing prices 20 percent as a surprise one time shock, which implies a tariff reduction in the protected manufacturing industries (sectors 4 and 5). After this one time shock, we let the system reach new steady state over time. Then, we estimate the model using simulated data during this transitory period.

In Simulation III, we increase the number of years from 26 to 100 to show the asymptotic properties of the estimation method.

In Simulation IV, we decrease the number of choices from 16 to 8 to show the impact of having a smaller number of observations because of smaller number of choices.

Then, we repeat all four simulation exercises 300 times. All simulations are conducted with _L_ = 20 _,_ 000 agents, which is approximately equal to the sample size of March-CPS that is used for the estimation in the previous section.

Table 4 presents the Monte Carlo simulation results. The column labeled as “Sim I” shows that the estimates are reasonably close to the true values and expected to be unbiased.

The column “Sim II” shows that using data contaminated with a non-stationary trade policy shock does not affect the performance of the method. Note that we did not specify the nature of the aggregate shock in the estimation procedure. The method introduced herein does not require strong distributional assumptions about the aggregate shocks. CCP method and other maximum likelihood based methods require the aggregate shocks to be to read.

> 13Note that wages show some fluctuations over time, for that reason we added an iid normal shock to equilibrium wages with standard deviation equal to 0 _._ 05, approximately equal to the standard error of average wages in the data. We also added an unexpected surprise shock to the wages with a standard deviation equal to 0 _._ 05.

17
Education noted, “Singapore wants to attract foreign talent at all levels of society – from CEOs to professionals to skilled workers – to work here. It wants to be a cosmopolitan, global city, an open society where people from many lands can feel at home”.<sup>17</sup> The Ministry of Trade and Industry has estimated that 37% of the economic growth in Singapore in the 1990s was due to the influx of International Talent.<sup>18</sup> ).

# **_III. New Orientation of Innovation Policy_**

In his book on “Free Market Innovation Machine”, Baumol (2002) observes that innovation is derived from both the routine activities of giant firms and from independent inventors and their entrepreneur partners. The necessary conditions or determinants of innovation may be analyzed at two levels: the determinants of organizational innovation over which firms have control (the internal environment), and the external social/business/regulatory environment within which firms and individuals operate. Factors at both levels are likely to determine innovation in giant firms, while the social/business/regulatory factors alone determine the extent of entrepreneurial activity. The two activities are not substitutes. Rather, entrepreneurs provide the more heterodox, breakthrough innovations, and the R&D establishments of the larger firms often enhance and add value to those breakthroughs by improving on them.

Traditionally, researchers and policymakers have focused their attention on industrial innovation. Service sector innovations and innovation within the creative industries have been relatively neglected until recently. To be sure, there is no clear dividing line among the various types of innovations here. Quite often, industrial product innovations (e.g internet hardware) serve as an initial catalyst for innovation in the services sector and in the creative content industries (e.g e-business); subsequent innovations in all sectors then interact and cross-fertilize with each other to trigger further innovations. Such overlaps notwithstanding, the table below is a convenient way of classifying the sources of innovation in an economy.<sup>19</sup>

# **_Table 1    Sources of innovation (examples)_**

> 17 See Singapore Ministry of Education website.

> 18 See the Ministry of Trade and Industry website, <u>http://www.sif.org.sg/shanghai_conf/docs/Mike_Gray.doc</u> 19   Appendix A provides an overview of the various views on what makes a company conducive for innovation.  It looks at both the internal and the external conditions that are considered important for innovations.

10
_Table 2: Mean fraction of potential harvest lost at the plot level, by country-year_

|Country|Year|Mean fraction of<br>potential harvest<br>lost, all plots|Mean fraction<br>of potential<br>harvest lost,<br>on plots<br>affected by<br>drought|Mean fraction<br>of potential<br>harvest lost,<br>on plots<br>affected by<br>floods|Mean fraction<br>of potential<br>harvest lost,<br>on plots<br>affected by<br>irregular rains|Mean fraction<br>of potential<br>harvest lost,<br>on plots<br>affected by<br>pests|
|---|---|---|---|---|---|---|
||2013-2014|48.66%|53.46 %|missing|49.36 %|45.22 %|
|Ethiopia|2015-2016|54.84%|59.71 %|missing|55.90 %|47.35 %|
||2018-2019|47.63%|46.91 %|missing|47.65 %|42.36 %|
|Malawi|2018-2019|55.00%|57.17 %|62.65 %|49.79 %|49.26 %|
|Mli|2014|48.08%|48.37 %|missing|38.11 %|40.73 %|
|a|2017|47.96%|47.50 %|missing|35.75 %|66.73 %|
|Nir|2011|71.09%|71.79 %|77.53 %|missing|66.96 %|
|ge|2014|49.33%|52.90 %|56.79 %|missing|40.97 %|
|Nigeria|2019|56.38%|36.38 %|61.87 %|missing|30.71 %|
|All countries|2011 – 2019|53.49%|56.75 %|62.28 %|51.52 %|46.52 %|

_Note: only point estimates are reported. Plots with no losses are excluded. Sample weights are used to calculate estimates._ 23