rafmacalaba/datause-encoder-data huggingface dataset:
automation equipment in the 1990s. Consequently, the share of LEs in factory automation employment rose from 0 in 1973 to 73.1 percent in 1979 but fell back to 51.7 percent in 1983 and 31.4 percent in 1988, reflecting the strong emergence of SMEs in this sector.

# III. Technical Support Systems

# **M.11** Overview

Technology improvement is an important means by which finms strengthen their market competitiveness. SMEs are at a disadvantage in this area compared to LEs, however, because they often lack both the qualified technical personnel and the resources to invest heavily in R&D or to acquire foreign technology. While therefore, extenal technical support systems play a key role in promoting the growth of SMEs, as shown below their use varies considerably according to differences in process technology. This chapter discusses Korea's collective technical support systems (state, public, and private non-profit)<sup>12</sup> , based on data collected from the four selected sectors.

Although some collective agencies date back to the 1960s, these early sources of technical support were not SME-oriented. Most agencies extending support to SMEs appeared in the late 1970s and early 1980s, as the growing importance of SMEs in Korea's economy was recognized. These tended to be state or public organizations with a general broad-based mandate. Most recently, in the late 1980s, industry-specific non-profit R&D centers began to appear as the technical needs of SMEs became more complex.

Major State and Public Agencies. Korea offers an extensive network of agencies providing collective technical support. This support is generally in the form of technical assistance, training programs, information services, or joint research opportunities. Most technical support agencies offer a combiration of services, often supported by financial and tax incentives.

12 State support refers to government technical support institutes staffed by civil servants. Public support refers to institutes established by the state but not staffed by civil servants. Non-profit institutes are often established jointly by the state and industry associations to provide specific types of technical support.

12
Figure 10: GIRFs for a one percent decline in China’s GDP growth rate with and without full set of commodities <!-- Start of picture text --> china usa euro japan<br>0.0 .00 .00 .00<br>-0.4 -.04 -.04 -.04<br>-0.8 -.08 -.08 -.08<br>-1.2 -.12 -.12 -.12<br>-1.6 -.16 -.16 -.16<br>-2.0 -.20 -.20 -.20<br>-2.4 -.24 -.24 -.24<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>austlia india indns kor<br>.05 .02 .05 .10<br>.01 .00 .05<br>.00 .00 -.05 .00<br>-.05 -.01-.02 -.10 -.05<br>-.10 -.03-.04 -.15-.20 -.10-.15<br>-.15 -.05 -.25 -.20<br>-.06 -.30 -.25<br>-.20 -.07 -.35 -.30<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>mal nzld phlp sing<br>.05 .08 .08 .1<br>-.05.00 .06.04 .04 .0<br>-.10-.15 .02 .00 -.1<br>-.20-.25 -.02.00 -.04-.08 -.2<br>-.30-.35 -.04-.06 -.12 -.3<br>-.40 -.08 -.16 -.4<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>thai<br>.04<br>.00<br>-.04<br>-.08<br>-.12<br>-.16<br>-.20<br>-.24<br>-.28<br>-.32<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 the 16th and 84th percentiles of the “oil-only” case( _red dots_ ) and the “optimal combination of multiple commodities” case ( _blue line_ ) the most, reflecting both demand and terms of trade shocks. Export-dependent countries on the East Asian production cycle, such as Japan, Singapore, Malaysia and Thailand, are also severely affected.

A shock to the Chinese real GDP also has an impact on the international prices of not only the crude oil market but also metals and agricultural markets, showing the degree of influence of China on the global terms of trade.

With regard to the future research, we consider improvement of the trade weight matrix to be the key. In this paper, following CPRX, we used the trade weight matrix representing the linkages to different countries. However, capital inflows and outflows significantly affect 34
The Malawi weather insurance scheme is a variant of index-based microinsurance, as it couples microlending with mandatory crop insurance rather than directly providing microinsurance to farmers. Without financial protection, lending to farmers, particularly so to rainfed farmers, has been generally considered very risky by banks because of the high systemic risk of loan default in the aftermath of droughts and other weather extremes. Consequently, the dominant government response to recurrent drought-induced food crises in Malawi has been to provide _ad hoc_ disaster relief (Hess and Syroka, 2005).

The packaged loan and index-based microinsurance product was first offered in 2005 by the Opportunity International Bank of Malawi (OIBM) and the Malawi Rural Finance Corporation (MRFC) to groups of groundnut farmers organized by the National Smallholder Farmers’ Association of Malawi (NASFAM). Technical assistance was provided by the World Bank and Swiss development assistance via the Swiss Secretariat for Economic Affairs (SECO). The farmer enters into a loan agreement with a higher interest rate that includes a weather insurance premium, which the bank pays to the insurer, the Insurance Association of Malawi (IAM). In the event of a severe drought (as measured by the rainfall index), the borrower pays a fraction of the loan due, while the rest is paid by the insurer directly to the bank. Thus, the farmer is less likely to default, which has a stabilizing effect on the bank’s portfolio and risk profile. Without this assurance, banks rarely loan to high-risk, low-income farmers. The advantage for farmers is that they obtain the credit they need for investing in seeds and other inputs necessary for higher-yield crops. It is envisaged that by granting farmers access to higherperforming crops, they will adopt higher yield–higher risk activities, but there is no evidence yet. The World Bank together with Opportunity International was the catalyst in developing such a weather insurance product to secure credit for groundnut farmers (Table 1).

|Table 1: Characteristics of|Malawi schemes for insuring credit and savings as of June 2007|
|---|---|
|Provider<br>(country, year of inception<br>ofdisaster insurance)|NASFAM with banks OIBM and MRFC, and insurer IAM<br>(Malawi, 2005)|
|Delivery model|Partner–agent, group-based: Weather Crop Insurance priced into<br>loan offered to farmers, bank thus is insured and receives claim in<br>case ofevent.|
|Premium|6–10% of insured assets asmark-upinterest on loan|
|Cover|Outstandingloanwithbankpaid byinsurer|
|Clients|Ca. 900 (2005), 3000 (2006)|
|Reinsurance|No|
|Assistance|World Bank and SECO with technical assistance and catalyzing<br>function|
|Majorevent experienced?|No, but some payoutsin 2005/06 season|
|Outlook|Should lead to higher yield–higher risk activities but no evidence<br>yet.|

Source: Mechler and Linnerooth-Bayer, 2006 In November 2005 the first policies were sold and about 900 smallholders in Malawi bought weather insurance that allowed them to access an input loan package to purchase 5
**Figure 2. Share of households owning animals and cultivating fields, by country and urban-rural**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Burkina Faso Mali Niger Senegal Chad All<br>Any animals Any fields<br>Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total<br>Share of households (percent)<br><!-- End of picture text --> Note: Household-level sampling weights applied. ‘Any animals’ measured at the time of the interview. ‘Any fields’ refers to whether fields were cultivated in the previous agricultural season. Source: EHCVM and World Bank estimates.

We also observe that diversification of income sources is relatively low among Sahelian households. Across the EHCVM sample, just under half of households (46.8 percent) had any working-age members primarily engaged in activities outside of agriculture, livestock, or fishing, with households as a whole engaging in 1.5 different sectors on average (Figure 3). Income-generating activities also appear to be less diversified in rural areas than urban areas.

**Figure 3. Diversification of household employment sectors, by country and urban-rural**

<!-- Start of picture text --> 2.5 100<br>90<br>2.0 80<br>70<br>1.5 60<br>50<br>1.0 40<br>30<br>0.5 20<br>10<br>0.0 0<br>Burkina Faso Mali Niger Senegal Chad All<br>Number of sectors (LHS) Any non-agriculture/non-livestock (RHS)<br>Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total Share of households with a worker<br>Mean number of sectors of primary work over the last 12 months in the household livestock in the last 12 months (percent)<br>primarily engaged outside of agriculture or<br><!-- End of picture text --> Note: Household-level sampling weights applied. Only working-age household members (those aged 15-64 years or more) that reported being employed or in unpaid family work in the previous 12 months used to assess the sectoral diversification of the household. Households without any working members coded as zero for number of sectors. Source: EHCVM and World Bank estimates.

9
mean price of wheat increased from 8.20/kg in wave 1 to 10.48/kg in wave 3; the mean price for maize increased from 5.33/kg in wave 1 to 6.40/kg in wave 3. For purposes of our analysis, we compute a yearly average price for each of the three crops at the zone level.<sup>1213</sup>

# **4. Empirical Approach**

To assess how food prices affect child undernutrition, we estimate the following equation: Where Y�� is the outcome indicator for child _i_ at time _t_ in terms of the z score for height for age and weight for height;<sup>14</sup> X�� is a vector of individual characteristics; X�� is a vector of household characteristics; X�� is a vector for community characteristics such as road access and access to markets. Our variable of particular interest, P��, is the price for each crop at the district/zone level within each region in Ethiopia while γ� are region fixed effects<sup>15</sup> and ε�� is the error term _._ For stunting, which measures persistent long-term child undernutrition, the impact of changes in prices is likely to take time to be realized. We therefore lag the crop price by one period before merging again with wave 2 and wave 3 of the LSMS data set and include P���� in the model. For wasting, which captures more rapid weight loss, we use the contemporaneous price.

The vector of control variables is drawn from previous studies that seek to explain child undernutrition rates (see Cintron, Seff, and Baird (2016), Edris (2007), Alemayehu _et al._ (2015), Liben, Abuhay and Haile (2016)). Specifically, we control for individual, household, and community factors that are associated with individual nutritional outcomes. Individual variables include a continuous variable for child’s age in months, child’s age in months squared since there might be a non-linear relationship and a binary variable for child’s sex. Household

> 12 Administratively, Ethiopia is divided into regions, zones, woredas and kebeles. Zones are a second-level subdivision of Ethiopia, below regions. They are subdivided into woreda’s or districts, which are further divided into kebele’s (wards).

> 13 Less than 1 percent of the observations are missing crop prices. In this case, we use the region average as the crop price for that zone.

> 14 We use continuous dependent variables rather than dichotomous (stunted/non-stunted, underweight/nonunderweight) as categorization can weaken the power of statistical tests and may lead to residual confounding. A data determined 'optimal' cut-point can lead to serious bias (Royston et al., 2006; Weinberg, 1995). Using dichotomous variables instead of continuous would mean discarding an important dimension of the intensity of the phenomenon.

> 15 These regions include Tigray, Afar, Amhara, Oromia, Somalie, Benshagul Gumuz, SNNP, Gambelia, Harari, and Dire dawa.

8
Bihar and Uttar Pradesh (UP) have been largely bypassed by the positive effects of economic liberalization and strong economic growth that followed (World Bank (2011)). In this subsection, we provide additional analysis of the role of geographic location in intergenerational educational mobility.

# **(4.2.1) Rural vs. Urban Areas**

This section presents results from estimation of sibling and intergenerational correlations for families living in rural and urban areas separately. The estimates of sibling and intergenerational correlations for rural and urban areas are reported in Tables 4 and 5 respectively. Consistent with the format of Table 3, we represent estimates from three different specifications of equations (6) and (7) in three panels (A, B and C) of Table 4 and 5. These specifications correspond exactly to the specifications in Table 3 and are not discussed here again for the sake of brevity.

The sibling and intergenerational correlations for all children and for men are larger in magnitudes in urban areas compared with rural areas (Tables 4 and 5). For instance, for all children (men), the sibling correlation is 0.579 (0.556) in rural areas compared with 0.664 (0.652) in urban areas in 1992/93. The corresponding intergenerational correlations are 0.482 (0.465) and 0.572 (0.553) in rural and urban areas respectively. For women in 1992/93, there is practically no difference in sibling correlations between urban and rural areas (both approximately=0.74), though intergenerational correlation is higher in urban areas (0.593 vs. 0.523). Between 1992/93 and 2006, intergenerational correlations remained nearly unchanged for both men and women in rural areas. There was a marginal decline in the sibling correlation for rural women in the same period, but the sibling correlation among rural men increased slightly. For men in urban areas, both sibling and intergenerational correlations remained effectively unchanged between 1992/93 and 2006. In contrast, sibling and intergenerational correlations have decreased significantly for urban women during the same period. As a result, gender difference in sibling and intergenerational correlations effectively vanished in urban areas, though it is still significant in rural areas.

Using estimates from Tables 4 and 5, we decompose the total variance in children‘s education into individual and common ̳family background‘ components. The family background component is further decomposed into three separate parts accounted for by parental education, common neighborhood environment and other common family factors. The relative contributions of these different factors to total variance of children‘s education are plotted in Figures 2 and 3. Consistent with results from full sample, influences of parent‘s education and common neighborhood 20
45 information asymmetries (Fabio Schiantarelli 1995). Theory suggests that as the costs to outsiders of acquiring information about a firm rise, firm investment decisions become more tightly constrained by retained earnings and current cash flow. Thus, firm level studies test whether the investment decisions of firms with particular traits that proxy for the costs to outsiders of acquiring information are more sensitive to cash flow than firms without those traits. The sample selection criterion varies across studies.

The empirical evidence suggests that the investment decisions of firms with more severe asymmetric information problems are more sensitive to cash flow than firms where it is less expensive for outsiders to monitor. This conclusion holds when firms are classified according to whether they have received bond ratings (Toni Whited 1992; Charles Calomiris, Charles Himmelberg, and Wachtel 1995), whether firms are issuing large or small dividends (Steven Fazzari, Glen Hubbard, and Bruce Peterson 1988; Hubbard, Anil Kashyap, and Whited 1995), whether firms are large or small (James Tybout 1983; Gertler and Simon Gilchrist 1994), whether firms place a relatively high or low shadow value on internal funds based on their response to taxes (Calomiris and Hubbard 1995), and whether regulations restrict bank credit allocation (Fidel Jaramillo, Schiantarelli, Weiss 1996; John Harris, Schiantarelli, and Miranda Siregar 1994). In sum, when outsiders find it expensive to evaluate and fund particular firms, those firms find it relatively difficult to raise capital for investment and rely disproportionately on internal sources of finance. Thus, financial innovations or policies that lower information asymmetries ease firm financing constraints on more efficient firms.

More importantly for this section, a large body of work shows that when firms have close ties to financial intermediaries, this reduces information costs and eases firm financing
(for example, restrictions on tuition increases in those schools), and those relating to public schools (mostly, easing procedural restrictions on hiring new teachers), estimated the number of beneficiaries in each, and applied the known demographics of public and private school students to these findings. According to the Brazilian statistical service,<sup>4</sup> and using a slightly lower income cut-off ($50/month in per capita family income, rather than $60/month), about 80% of public school students, and 27% of private school students are ―underprivileged.‖ Brinks and Gauri estimate about 40,000 beneficiaries in public schools and 400 in private ones. Given this ratio, at least 78% of beneficiaries of education litigation come from the lower two income quintiles, so that the underprivileged are overrepresented in Brazil‘s Education-Regulation litigation by about 4 to 1.

# _South Africa_

In contrast to Brazil, South Africa showed a markedly smaller number of cases, the effect of which was felt largely through the modification of public policy as a result of the court order. That is, the South African litigation model involved the modification of public policy through broadly applicable, _erga omnes_ decisions, rather than through the accumulation of individual cases with individual remedies and the more or less voluntary adoption of these decisions on an ad hoc basis. This means that, even more than for Brazil, the demographics of the actual litigants are of trivial importance, compared to the demographics of the relevant policy area beneficiaries.

Moreover, this means the numbers of health and education rights cases are small enough to trace the impact case by case (and only six of the twenty-four cases resulted in significant measurable impact). The results for South Africa, in keeping with our expectations, are more pro-poor than those in Brazil: more than 80 percent of all those benefited by these decisions fit even a fairly narrow definition of ―underprivileged,‖ compared to the distribution-neutral effect

> 4 Instituto Brasileiro de Geografia e Estatística (http://www.ibge.gov.br).

23
Table 4.26. Probit regression. Dependent variable: visit health clinic dummy.

||Pre-|crisis||Post-|crisis||
|---|---|---|---|---|---|---|
||Coef.|Std.Err||Coef.|Std.Err||
|Othertowns|0.4389|0.1875|'t|0.2242|0.1897||
|Rural|-0.1422|0.1999||-0.1951|01939||
|Age|0.0056|0.0198||0.0109|0.0210||
|Agesq.|0.0000|0.0002||-0.0001|0.0002||
|Headfemale|0.0228|0.1170||0.0996|0.1137||
|Single|-0.2274|0.2765||0.2487|0.2599||
|Separate|-0.3823|0.3684||-0.0094|0.3210||
|Widowed|-0.2314|0.1616||-0.1481|0.1683||
|Divorced|-0.3925|0.2202|*|-0.4679|0.2402|"|
|Illiterate|-0.2262|0.2749||-0.5278|0.3397||
|Primaryeducation|-0.1073|0.1266||-0.1860|0.1379||
|Highereducation|-0.0538|0.1429||0.0927|0.1380||
|Farmers|-0.1622|0.1726||0.0541|0.1621||
|Hiredinagnculture|-0.1484|0.1332||-0.1820|0.1419||
|Selfemployed|0.3338|0.2993||-0.1481|0.3164||
|Pensioners|-0.1592|0.1754||-0.1053|0.1920||
|Other|0.1516|0.3201||0.6477|0.3833|'|
|Householdsize(log)|0.6061|0.1819|**|0.3400|0.2026|*|
|Numberunder6|0.1086|0.0991||0.2327|0.1048|t|
|Numberaged6-14|-0.0582|0.0742||-0.0198|0.0777||
|Numberaged15-17|0.0262|0.1127||0.0802|0.1120||
|Numberaged18-25|0.1041|0.0827||0.1003|0.0884||
|Numberover64|0.0557|0.0931||0.0289|0.0985||
|Numberearners|-0.3016|0.0709|t|-0.1003|0.0793||
|Plotsize|-1.2788|0.9063||-1.1399|0.5781|t'|
|Fractionagricult.|0.2395|0.3410||-0.0162|0.3623||
|Housearea|0.0028|0.0015|*|0.0029|0.0016|'|
|Fractionliving area|-0.6916|0.3032||-0.4933|0.3634||
|Housingownership|-0.0787|0.1825||-0.0779|0.1900||
|Yearquarter1|0.0046|0.0829||0.1097|0.0877||
|Year quarter2|-0.1444|0.1040||-0.0355|0.1084||
|Consumption(log)|0.3052|0.0657|4|0.2701|0.0730|t|
|Agriculturalsociety|0.1496|0.1073||0.0683|0.1175||
|Socialbenefits|0.0962|0.0963||0.3619|0.1086|**|
|Privatetransfers|0.0943|0.0785||0.0850|0.0841||
|Distancetoclinic|-0.1038|0.0682||0.0184|0.0660||
|Distanceto hospital|0.0211|0.0047|'t|0.0212|0.0049|t|
|Timetoclinic|-0.0001|0.0044||0.0043|0.0042||
|Timetohospital|-0.0023|0.0021||-0.0022|0.0020||
|Constant|-1.9303|0.7213|t|-2.2914|0.7418|**|
|Chi-squared|154.64|||148.12|||

Left-out variables are dunmmies for large cities, married, secondary education, hired in non-agriculture, number aged 26-64, year quarter 4. (*) Significant at the 10% level. (**) Significant at the 5% level.

41
**_<u>Table 12b: Effect of class size, difference in dependent variable, relative to median class size</u>_**

|Dependent Variable|Type|First|Media|Third|at|Max/Min|Signif.|
|---|---|---|---|---|---|---|---|
|Participation Ratio(Ukrainian)|urban|-0.2|0|0.4|-0.3|14.9|NS|
|Participation Ratio(Ukrainian)|rural|-0.5|0|-0.4|0|13.1|S|
|Average Ukrainian Score|urban|0.1|0|0.2|0|20.8|S|
|Average Ukrainian Score|rural|0.4|0|-0.3|-0.6|28|Almost|
|Average Mathematics Score|urban|-0.1|0|0.2|-0.1|17.9|Almost|
|Average Mathematics Score|rural|0.3|0|-0.3|-1.3|55.2|NS|
|Average Ukrainian HistoryScore|urban|0.1|0|0.2|0|21.5|S|
|Average Ukrainian HistoryScore|rural|0.7|0|-0.5|-0.7|22.4|S|
|Average Ukrainian Score - Female Students|urban|0|0|0.22|0|20.1|S|
|Average Ukrainian Score - Female Students|rural|0.4|0|-0.3|-0.6|25.6|Almost|
|Average Ukrainian Score - Male Students|urban|0.1|0|0.3|0|21|S|
|Average Ukrainian Score - Male Students|rural|0.6|0|-0.6|-1|28.4|Almost|
|% students having> 173 on the Ukrainian Exam|urban|-0.1|0|0.6|-0.1|19.1|S|
|% students having> 173 on the Ukrainian Exam|rural|0.4|0|-0.3|-0.5|23.8|NS|
|% students having< 135.5 on the Ukrainian|urban|-0.2|0|-0.4|0|21.2|S|
|% students having< 135.5 on the Ukrainian|rural|-0.7|0|0.6|1.9|35.3|NS|
|Participation Ratio - weighted average variables|urban|-0.2|0|0.5|-0.2|20.3|S|
|Participation Ratio - weighted average variables|rural|0.9|0|-1.4|1.4|5.2|Almost|
|Average Ukrainian Score- weighted average|urban|-0.2|0|0.6|-0.2|20.3|S|
|Average Ukrainian Score- weighted average|rural|0.4|0|-0.5|3.2|-38|NS|

Differences are percentage points for the participation ratios and points for the test scores For the 2010 regressions, school quartiles are at about 100, 140 and 210 for rural areas and 305,465 and 666 for urban areas. For the weighted average regressions, quartiles are at 110,154 and 225 for rural areas and 322, 484 and 685 for urban areas 58