rafmacalaba/datause-encoder-data huggingface dataset:
Within the EU, the effective operation of the system has also benefited from a history of relatively strong supervision.

This system has proven easy and cost effective to apply and understand. It also has the advantage of being entirely based on insurance company accounting data and, as a result, is subject to the audit and actuarial processes and oversight. The simplicity and cost effectiveness have also meant that the system has been adopted by a large number of non-European countries.

However, the view is emerging that the system is in need of revision. Concerns include the absence of a specific reflection of asset risks in the solvency margin requirement itself, particularly with respect to non-life insurance, the reliance on premiums and provisions even when the company may be under pricing or under provisioning (and the built-in incentive that may exist to do so), the lack of sensitivity to the risk profile of the company, the absence of qualitative considerations of the risk management process within the company, and the absence of a forward-looking construct.

At the same time, the European insurance market has seen increasing competition, pressure on shareholder value, continued capital market integration, the emergence of significant financial conglomerates, new distribution channels, and the development of market techniques such as “Asset Liability Management”, “Alternative Risk Transfer” and the increased use of derivatives (Martinho 2003). The recent unfavorable investment climate (noted in section 2.3 above) is also relevant to the review.

The response has been to move toward a new solvency regime, first through an essentially tactical change (Solvency I) that has now been completed and, more recently through a more wide-ranging review intended to lead to Solvency II. The Solvency II work has developed a number of basic working premises and it is expected that the regime will:

- be more risk-sensitive than the current system

- assess “overall solvency” of an insurance company including both quantitative and qualitative aspects such as requirements on and the assessment of management and internal risk controls

- be based on a three-pillar structure although somewhat adapted to the needs of insurance supervision. (The three-pillar concept is discussed further in 4.1 below)

- be based on more sophisticated risk measurement encouraging insurance companies to improve the measurement and management of their risks

16
use rates across the different inputs, particularly when considering fertilizer and pesticide (Figure 2.4). The Western region lags in the use of all agricultural inputs. Also, the use rate of improved (hybrid) seeds in Uganda is considerably high compared to the rest of the agricultural inputs.

Moreover, the joint adoption of both inorganic fertilizer and improved seeds remains very low at an average of 3 percent over the period of interest, which limits the potential yield gain and could be harmful in the long run. These trends are consistent with the findings of earlier studies that show how the adoption of these complementary inputs is incomplete in Uganda (Nkonya et al. 2005 and Sserunkuuma 2005). Farmers tend to adopt improved crop varieties (hybrid seeds) without simultaneously applying fertilizer. This worsens soil fertility and yields in the long run, and can undermine the potential yield gain over time.

# **Figure 2.4: Poorer households in poorer regions are less likely to use inputs**

<!-- Start of picture text -->
1. Inorganic Fertilizer Use by Consumption  2. Pesticide Use by Consumption Quintile, 2005/06<br>Quintile, 2005/06 ‐2013/14  – 2013/14<br>14% 40%<br>12% 35%<br>10% 30%<br>8% 25%<br>20%<br>6%<br>15%<br>4%<br>10%<br>2%<br>5%<br>0%<br>1st 2nd 3rd 4th 5th 0%<br>1st 2nd 3rd 4th 5th<br>Central Eastern Northern Western<br>Central Eastern Northern Western<br>3. Improved Seed Use by Consumption Quintile,<br>2005/06 – 2013/14<br><!-- End of picture text -->

protocols to generate gross income aggregates across waves. Where inconsistencies were noticed they were communicated to the RIGA team and corrected in the protocols we used. The RIGA aggregates calculate crop and livestock income using information on the amount of goods produced by the household in the last 12 months and the price of these goods reported by the household. Prices are imputed from other sampled households when they are not collected. The measures of crop and livestock income calculated are gross income not netting out costs of land, labor or purchased inputs. Wage income is generated from data collected on wages earned in the last 12 months. The self‐employment income aggregate calculated all self‐employment income earned in the last 12 months and nets out the cost of purchased inputs. Purchased inputs can be quite a large share of the income from self‐ employment activities such as petty trading or handicrafts and netting out the cost of these inputs is important. For more details on how these aggregates are constructed, see Carletto et al. (2007).

8
**Table II-3 Regression results for the sudden-onset, hydrometeorological events sample** **<u>(N=62)</u>**

|Model|Gross savings<br>PV dConsump|Gross<br>disaster<br>savings<br>tion inper cen|Net<br>savings<br>t ofper capita|Net<br>disaster<br>savings<br>income of the|Genuine<br>savings<br> year before t|Genuine<br>disaster<br>savings<br>he event|
|---|---|---|---|---|---|---|
|Constant|-0.031|0.041|0.061*|0.120***|0.075**|0.124***|
||0.67|(1.07)|(1.79)|(4.03)|(2.01)|(3.95)|
|Savings measures|1.214***|1.014***|1.229***|0.995***|1.109***|0.974***|
||(5.33)|(4.74)|(5.05)|(4.42)|(3.96)|(3.71)|
|R Square|0.325|0.276|0.302|0.249|0.210|0.189|

Note: Significance at the * 10% level;** 5% level; *** 1% level

**Table II-4 Regression results for the low income sample, sudden hydrometeorological events revised for aid** **<u>(N=35)</u>**

|Model|Gross savings<br>PV dConsumpt|Gross<br>disaster<br>savings<br>ion inper cent|Net<br>savings<br>ofper capita|Net<br>disaster<br>savings<br>income of the|Genuine<br>savings<br> year before t|Genuine<br>disaster<br>savings<br>he event|
|---|---|---|---|---|---|---|
|Constant|-0.112*|-0.063|-0.013|0.033|0.033|0.062|
||1.77|1.17|0.25|(0.73)|(0.55)|(1.22)|
|Savings measures|1.472***|1.483***|1.461***|1.526***|1.155***|1.369***|
||(4.94)|(5.21)|(4.36)|(4.75)|(2.73)|(3.23)|
|R Square|0.425|0.452|0.366|0.406|0.184|0.240|

Note: Significance at the * 10% level;** 5% level; *** 1% level 31
- Wright, Brian D, and Jeffrey C Williams. 1982a. “The Roles of Public and Private Storage in Managing Oil Import Disruptions.” _The Bell Journal of Economics_ 13 (2): 341–353.

- ———. 1982b. “The Economic Role of Commodity Storage.” _The Economic Journal_ 92 (367): 596–614.

- ———. 1988a. “Measurement of Consumer Gains from Market Stabilization.” _American Journal of Agricultural Economics_ 70 (3): 616–627.

- ———. 1988b. “The Incidence of Market-Stabilising Price Support Schemes.” _The Economic Journal_ 98 (393): 1183–1198.

WTO. 2004. “WTO Agriculture Negotiations: The Issues, and Where We Are Now.” 45
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# 7. Bibliographical references

Acemoglu, D., 2002. “Technical Change, Inequality, and the Labor Market”. Journal of Economic Literature 40(1): 7-72.

Acemoglu, D. and D. Autor, 2011. “Skills, Task and Technologies: Implications for Employment and Earning”. Handbook of Labor Economics. Elsevier, pp: 1043-1171 Aedo, C., J. Hentschel, J. Luque and M. Moreno, 2013. “From Occupations to Embedded Skills. A Cross-Country Comparison”. Policy Research Working Paper 6560, Banco Mundial, Washington, D.C.

Autor, D., 2015. “Why Are There Still So Many Jobs? The History and Future of Workplace Automation”. Journal of Economic Perspectives 29(3), 3–30.

Autor, D., F. Levy and R. Murnane, 2003. “The Skill Content of Recent Technological Change: An Empirical Exploration”. The Quarterly Journal of Economic <mark>s 118(4): 1279-1333.</mark> Bresnahan, T., 1999. “Computerisation and Wage Dispersion: An Analytical Reinterpretation”. Economic Journal, 109(456): 390-415.

Frey, C. and M. Osborne, 2013. “The Future of Employment: How Susceptible are Jobs to Computerization?” Oxford University Paper, United Kingdom.

Goos, M. and A. Manning, 2007. “Lousy and Lovely Jobs: The Rising Polarization of Work in Britain”. Review of Economics and Statistics 89:118-133.

Hardy, W., R. Keister and P. Lewandowski, 2015. “Do Entrants Take It All? The Evolution of Task Content of Job in Poland”. IBS Working Paper 10, Poland.

Katz, L. and K. Murphy, 1992. “Changes in Relative Wages, 1963 – 1987: Supply and Demand Factors”. The Quarterly Journal of Economics 107(1): 35-78.

Keister, R. and P. Lewandowski, 2016. “A Routine Transition? Causes and Consequences of the Changing Content of Jobs in Central and Eastern Europe”. IBS Policy Paper 5/2016, Intytut Badan Strukturalnych, Poland..

Mincer, J., 1974. Schooling, Experience, and Earnings. National Bureau of Economic Research, New York.

Polanyi, M., 1966. The Tacit Dimension, New York, Doubleday.

- 34 -
revenue.<sup>30</sup> The ...rst row of Table 3 shows the aggregate DVAR estimates based on the total population of ...rms from the manufacturing census in 2004. The estimated DVAR is 0.479. In the next row, we restrict the census sample to include only ...rms that overlap with our original manufacturing survey. The estimated DVAR dropped slightly to 0.478, which is not statistically di¤erent from the previous row. This con...rms that sample selection bias is not an issue in our manufacturing survey sample, despite the exclusion of ...rms with less than 5 million RMB revenue in our sample.<sup>31</sup> Listed in Row (3) is the IO table-based DVAR estimate of 0.408 from KWW12. Consistent with our results in previous section, the IO table-based DVAR estimate is statistically lower than the DVAR estimates in Rows (1) and (2), based on their respective bootstrapped standard errors. In Row (4), we further restrict the census sample, according to the sample selection criteria speci...ed in the Chinese IO table manual –...rms with over 300 million RMB revenue. The aggregate DVAR estimate drops to 0.453. Not only is the resulting DVAR estimate based on this large ...rm sample lower than the estimates in Rows (1) and (2), it is also not statistically di¤erent from the IO table-based estimate by KWW12 in Row (3), based on a standard error of 0.034 from bootstrapping with 100 repetitions. This exercise con...rms that DVAR decreases when samples that only include larger ...rms are used, due to ...rms’heterogenous input sourcing.

Thus, the result in Table 3 nicely shows that ignoring ...rm heterogeneity may lead to downward aggregation bias in the IO table-based approach. While there can be many reasons why our ...rm-based estimates and the IO table-based estimates of KWW12 are di¤erent, such

> 30 Unlike our industrial survey dataset, the census dataset does not provide direct information on ...rms’ costs of materials. We follow the guideline of the user manual of the census dataset to compute a ...rm’s cost of materials by substracting its total sales by its value added. As such, our DVAR estimates based on the census dataset is not directly comparable to the estimates in the previous sections based on the industrial surveys, which provide direct information on ...rms’costs of materials.

> 31Recall that our sample consists of ...rms from the Annual Surveys of Industrial Firms, which has sales cuto¤ of 5 million RMB (about 600,000 USD) and above, while the 2004 census covers all industrial ...rms.

16
low power against the hypothesis of no seasonality – see the final column in each of Tables 3 and 4.

In summary, parsimonious seasonal models are likely to be preferable to the standard dummy variable procedure for estimating the extent of seasonality when data samples are short or seasonal processes are poorly defined. These are typical circumstances in data on prices for developing country food crops. These procedures substantially reduce the bias resulting from use of dummy variable estimators of the seasonal gap. Significance tests on the presence of seasonality remain correctly sized (i.e. they incorrectly reject the hypothesis of no seasonality in the expected proportion of cases) but they may have low power (they fail to correctly reject the hypothesis of no seasonality in a large proportion of cases). Their limitation is that they will perform poorly for crops in which there are two harvests per year.

# **5 Seasonality in African food crop prices**

The extent of seasonality in food prices is examined for seven African countries: Burkina Faso, Ethiopia, Ghana, Malawi, Niger, Tanzania and Uganda. Monthly price series for 13 crops and food products in local markets over the period 2000‐2012 were obtained from national statistical offices and from a private marketing agency in Uganda. The crops covered the main staple cereals (maize, millet, rice, sorghum and teff) together with cassava and a number of important fruits and vegetables, as well as eggs. The number of markets varies across countries. In four countries (Burkina Faso, Niger, Tanzania and Uganda), prices are reported both at the retail and wholesale level, although not always for the same marketplaces. For the other three countries there are only wholesale prices. This data set yields a total of 1,053 location‐food crop pairs. Table 5 provides more detailed information.

Prices are all expressed in nominal terms and local currency. There has been substantial inflation during the sample period in some of the countries. Deflation of the price of a major food staple by the local CPI would, however, remove part of the variation of interest. We rely on the trend in equation (1) to account for the impact of inflation and other trend‐associated 17
remains almost constant over time, as the PIH would suggest.<sup>31</sup> In contrast, under a fixed ER, transfers are countercyclical in the short term to constrain aggregate demand (before prices have adjusted) but support consumption smoothing of the hand-to-mouth household in the long run (after prices have adjusted), consistent with its new higher permanent income. Specifically, Panel D shows that transfers fall by 0.35% of steady-state non-resource GDP in the first period (1q-MPS of - 0.35) and increase slowly thereafter.<sup>32</sup> After three quarters, transfer are back to steady state, and after three years, transfers are as high as with floating ERs/flexible prices. This dynamic path is accomplished through a high MPS accumulated public assets ( ), as assets increases rapidly (Panel F) in response to the fall in transfers and higher commodity revenue. Overall, the optimal transfer can partially stabilize aa θθ the economy in the short term, but this implies a volatile (hump-shaped) consumption path of the hand-to-mouth households, resulting in a welfare loss 28% higher than with a floating ER.

# _E. The Role of Shock Persistence_

In this subsection, we show how the procyclicality of the optimal fiscal rule changes with the persistence of commodity price shocks.

**The MPS, half-life, and ER in the short run.** Panel A of Figure 2 shows the relation between the optimal one-year MPS (y-axis) and the half-life of the commodity price shock (x-axis) in the flexible price economy (light blue line), with a floating ER (dark blue solid line), or fixed ER (solid red line).<sup>33</sup> As before, the lines for the flexible price economy and the floating ER are almost identical. In these two cases, the one-year MPS increases substantially with the half-life of the commodity price shock. When the shock is purely transitory (a zero half-life), the

> 31 Note that the consumption of the hand-to-mouth household increases less than the change in transfers (0.42% versus 0.55% of non-resource GDP) due to the negative wealth effect on labor supply. This mechanism is highlighted in Giambattista and Pennings (2017).

> 32 Despite the lower transfers from the government, the consumption of the hand-to-mouth household increases due higher wages and hours worked.

> 33 To isolate the effect of shock persistence as the half-life varies, when calculating the optimal MPS, we adjust the error standard deviation to keep constant the variance of the commodity price.

22
# **Figure 2**

# **Individual distributions of the financial inclusion indicators**

The distribution of each financial inclusion indicator along with the country code for the minimum, maximum and median value. Data averaged over the two rounds of Global Findex (2011, 2014) or over the period 2007–2014 for Enterprise Surveys data.

<!-- Start of picture text --> Borrowed from a financial institution Credit card Saved at a financial institution Account at a financial institution<br>High Inc VEN ARE NZL High Inc LTU SVN ISR High Inc ARG SVK NOR High Inc URY USA NOR<br>Upp Mid TKM CRI IRN Upp Mid MNG BLR TUR Upp Mid TKM ROU THA Upp Mid TKM BLZ MNG<br>UZB<br>Low Mid Low Mid Low Mid Low Mid<br>YEM KSV ARM BTN UKR TJK CMR LKA YEM HND LKA<br>MWI<br>Low Inc Low Inc Low Inc Low Inc<br>CAF SLE KHM MDGZWE NER BEN RWA NER BFA RWA<br>0 10 20 30 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 100<br>Min Median Max Min Median Max Min Median Max Min Median Max<br>Purchased agriculture insurance Debit card Electronic payments Firms with a checking or savings account (%)<br>BLR High Inc High Inc High Inc<br>Upper middle ISR ESP NOR ARG JPN FIN SVK CHL TTO<br>PRY THA<br>MUS<br>Upp Mid Upp Mid Upp Mid<br>SEN TKM MNE IRN TKM IRN IRQ CHN SRB<br>Lower middle<br>GEO ZWB HND<br>Low Mid Low Mid Low Mid<br>MMR HND UKR MMR UKR MMR LKA SDN<br>Low Income AFG MWI<br>BFA ZWE GIN Low Inc NER ZWE Low Inc MDGZWE Low Inc KHM MDG CAF<br>0 10 20 30 40 50 0 20 40 60 80 100 0 20 40 60 80 0 20 40 60 80 100<br>Min Median Max Min Median Max Min Median Max Min Median Max<br>Firms using banks to finance investments (%) Firms using banks to finance working capital (%) Saved for business purpose (%) Used an account for business (%)<br>High Inc High Inc High Inc High Inc<br>URY POL ISR LVA POL TTO GRC FRA KOR URY PRT ESP<br>Upp Mid Upp Mid Upp Mid Upp Mid<br>PAN NAM VCT IRQ ROU DOM BIH ZAF JAM TKM AGO JAM<br>MRT<br>Low Mid Low Mid Low Mid Low Mid<br>MMR VNM BTN SDN GHA WSM EGY MRT KEN SLV MAR<br>NPL<br>Low Inc Low Inc Low Inc Low Inc<br>AFG MDG BDI AFG TGO BDI BDI BEN UGA KHM ZWE<br>0 20 40 60 0 20 40 60 80 0 10 20 30 40 0 10 20 30 40<br>Min Median Max Min Median Max Min Median Max Min Median Max<br><!-- End of picture text --> 30
points in Appendix 2, Table 2.3, assuming that there were no missing data.<sup>20</sup> The latter statistics is simply the ratio of the figures in Table 4, column 3 and the total numbers of country-indicatoryear data points that may be available (Appendix 2, Table 2.3, column 3).

Table 4 points to the data challenge with tracking the SDGs, which varies widely by each goal. First, we never have full data for all the 249 countries formally recognized by the UN, with the number of countries with missing data ranging from four (SDG 15, Life on land) to as many as 96 (SDG 13, Climate action). Furthermore, for two countries during the period considered, we do not even have any single data point.<sup>21</sup> Second, we also never have full data for all the indicators in each goal. Again, the number of indicators varies widely from goal to goal, and ranges from one (SDG 4, Quality education or SDG 9, Industry, Innovation, and Infrastructure) to 17 (SDG 17, Partnership for global development). In fact, the last row of Table 4, column 2 indicates that the total number of indicators that we have some data accounts for just over half of all the SDG indicators (i.e., 134 out of 244 indicators). While this empirical finding is consistent with our earlier discussion in Section II (i.e., we can only have some data coverage for at most 80 percent of all the indicators in theory), it also points to a seemingly worse picture on data availability in practice.

But note that the goal-by-goal “tests” for missing data offered by columns 1 and 2 in Table 4 may not uncover the full severity of the missing data challenge, since the availability of just one country data point for one indicator satisfies these tests. What is most relevant to analysis is the absolute number of country-indicator-year data points available (column 3), and its relative

> 20 Since the UN’s database does not remove the duplicate indicators, we also keep all the 244 indicators with duplicates in Appendix 2, Table 2.4 for better comparison.

> 21 This may perhaps partly explained by the fact that the two countries with completely missing data are the island nations Bouvet Island and Sark, which have a small population. Indeed, Bouvet Island is considered to be an uninhabited island (CIA, 2018), while a recent estimate puts the population of Sark at only 600 people (BBC, 2012).

21
21 TRADE FINANCE IN A LIQUIDITY CRISIS

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