rafmacalaba/datause-encoder-data huggingface dataset:
While the shares for dimension-specific groups in the general population are based on the sample of all household members captured by the IHS3, the shares for dimension-specific groups within the leadership are informed by the roster of individuals that were interviewed as part of the focus group for the IHS3 Community Questionnaire. A fifth diversity index on landholdings is constructed solely for the general population as the information on the leadership landholdings was not available. Given the continuous nature of the variable, we capture it as difference between the 10<sup>th</sup> and 90<sup>th</sup> percentile of the distribution of household landholdings in each community.

The diversity indices for the general population are measures of heterogeneity within the community and should be control variables in the multivariate analysis of collective action capacity. The ethnicity, education and landholding diversity indices for the general population are featured explicitly as independent variables. The gender and age diversity indices for the general population do not enter into the analysis as they are highly correlated across communities.

Finally, the similarity between the leadership and the general population in terms of gender, age, ethnicity and education are captured as the ratios of leadership to community diversity (Jost, 2006). Since we wish to capture the overlap between the leadership and the general population diversity and are not concerned with the comparisons of diversity, the similarity indices are constructed using the greatest diversity index (the leadership or the general population) as the denominator.

# **4.4.    Additional Control Variables**

Transactions costs of collective action are hypothesized to be lower where more diverse people actively participate in community organizations. To explore this hypothesis, we rely on the share of female members and share of members under the age of 30 in parent-teachers associations, education committees and health committees that might exist in the community.<sup>14</sup> Additionally, we control for median estimates of education and age for the leadership to account for the potential separate impacts exerted by these variables (through leadership capacity) on collective action capacity. The inclusion of number of households and its squared term as an independent variable accommodates the possibility that very small communities face difficulties in high per

> 14 The IHS3 Community Questionnaire solicited information on the presence and membership dynamics of various local organizations, including VDC, agricultural cooperative, farmers’ group, savings and credit cooperative, business association, women’s group, youth group, political group, religious group, cultural group, health committee, education committee, parent-teacher association, sports group, NGO, community police/watch group, and disabled association.

18
45 ^,ONCLUSION Two very different types of analysis have been applied to very different types of data to try to quantify any short-term effect of economic crisis and subsequent adjustment on child mortality in Sub-Saharan Africa. The first analysis is of aggregate data for 10 countries covered by the Demographic and Health Surveys project. The second is an individual-level analysis of a very elaborate data set for Cote d'Ivoire collected in the mid-1980s. Both analyses use time period dummy variables to identify effects of crisis and adjustment. Despite very different methodologies and data sets, the two analyses come up with surprisingly similar results.

The aggregate study, covering 10 countries that contribute somo 20 percent of Sub-Saharan Africa's births in the late 1980s, achieves a respectable level of representativeness. Results suggest that neither economic crisis nor the resulting adjustment policies had the effect of increasing child mortality in the short run at the national level relative to countries not undergoing adjustment (but not necessarily avoiding crisis). The results are, however, consistent with some differential effects across socioeconomic groups, with adjustment policies associated with some increase in child mortality in urban areas, particularly among the urban poor.

The individual-level analysis examined the child mortality effects of adjustment in Cote d' Ivoire. The stabilization policies, together with the economic crisis, have led to a decline in real income in urban areas. In contrast, rural incomes among producers of cash crops may not have been negatively affected, because agricultural policy components of the SAP have been relatively protective of agricultural incomes. The effect of the SAP on the health sector is unclear.
less labor-intensive crops like tree nuts, which are harvested using “shake-and-catch” machines (Rutledge and Taylor 2019; Rutledge et al. 2019). However, consumer demand for fresh fruits and vegetables, both in the United States and abroad, continues to rise, and food imports are expanding (see Figure 4). Consumers’ demand and willingness to pay for locally-grown produce increases as incomes rise, and this creates limits to countries’ reliance on food imports as a solution to the farm labor problem.

At the other end of this trade often are countries with much lower land per laborer, such as China. Since the turn of the century, China has dramatically raised its exports of labor-intensive fruits and vegetables, while increasing its imports of less strategic, and more land (and water) intensive ones such as soybeans and corn for animal feed, much of it from the United States (soybeans) and Brazil (Christiaensen 2013).

**Figure 4.** Fruit Imports for Europe and the United States

<!-- Start of picture text --> 16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>U.S. Europe (top 10 EU countries)<br>Metric Tons (in millions)<br><!-- End of picture text -->

**<u>Source</u>** : FAO (http://www.fao.org/faostat/en/#data).

14
supply provisions. Therefore, the elimination of load shedding, particularly from the industrial sector, is crucial to avoid billions of dollars of economic loss in years to come. Moreover, elimination of load shedding is also critical to improve the business environment for potential investors, which is absolutely needed to boost Nepal’s economic growth.

# **References**

Abdullah, Sabah and Petr Mariel (2010). Choice experiment study on the willingness to pay to improve electricity services, Energy Policy, Vol.38, pp. 4570–4581.

Allcott, H. et al (2016). How do electricity shortages affect productivity? Evidence from India. American Economic Review, 106(3): 587–624.

Armington, P. (1969). A Theory of Demand for Products Distinguished by Place of Production. IMF Economic Review, Vol. 16, No. 1, pp. 159-178.

- Bose R.K., Shukla M. Srivasta L and Yaron G. (2006). Cost of unserved Power in Karnataka, India. Energy Policy, 34 (12), 1434-1447.

Braimah, I. and O. Amponsah (2012). Causes and effects of frequent and unannounced electricity blackouts on the operations of micro and small-scale industries in kumasi. Journal of Sustainable Development, 5(2), 17-36.

Coady, D., and R. Harris (2001). A Regional General Equilibrium Analysis of the Welfare Impact of Cash Transfers: An Analysis of Progresa in Mexico. Trade and Macroeconomics Division, Discussion Paper No. 76. International Food Policy Research Institute, Washington, DC.

Decaluwé, B., L. Savard and E. Thorbecke (2005). General Equilibrium Approach for Analysis with an Application to Cameroon. African Development Review, 17, pp. 213-243.

Despotakis, K.A. (1980). Economic performance of flexible functional forms: Implications for equilibrium modeling. European Economic Review, Vol. 30, pp. 1107-1143.

Frisch, R. (1954). Linear Expenditure Functions: An Expository Article. Econometrica, Vol. 22, No. 4, pp. 505-510.

Jyoti, R., Ozbafli, A., and G. Jenkins (2006). The Opportunity Cost of Electricity Outages and Privatization of Substations in Nepal. Queen’s Economics Department Working Paper No. 1066, Queen’s University, Canada.

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**Table** **<u>3: Baseline: COVID-19 demand shock effect on gender employment gap</u>**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
||⍙**female**<br>**emp. AC**|⍙**male**<br>**emp. AC**|**gender**<br>**emp. **⍙**AC**|%⍙**male**<br>**emp AC**|%⍙**female**<br>**emp. AC**|**gender difference**in<br>% ⍙in**emp. AC(M-F)**|
|Demand shock|-0.674*|-2.95***|-2.276***|-0.235***|-0.106***|-0.123**|
||(0.348)|(0.755)|(0.802)|(0.0510)|(0.0404)|(0.0484)|
|Formal|-0.0295|-3.328*|-3.298|-0.169|0.0384|-0.207|
||(0.363)|(2.001)|(2.020)|(0.131)|(0.0475)|(0.134)|
|Export status BC|0.968|-3.011|-3.978|0.139***|0.115***|0.0209|
||(1.280)|(2.964)|(3.083)|(0.0472)|(0.0344)|(0.0465)|
|Medium size|-0.635**|-3.43***|-2.794***|-0.0638|-0.126***|0.0603|
||(0.256)|(0.954)|(0.968)|(0.0599)|(0.0476)|(0.0608)|
|Large size|-2.75***|-10.95***|-8.194***|-0.164**|-0.123***|-0.0484|
||(0.593)|(1.894)|(1.847)|(0.0663)|(0.0465)|(0.0611)|
|Private|1.227|-1.105|-2.332|-0.0262|0.0202|-0.0550|
||(1.015)|(2.008)|(1.824)|(0.0677)|(0.0496)|(0.0584)|
|Ln(age)|0.0206|-0.213|-0.234|-0.0132|-0.0222|0.0105|
||(0.140)|(0.571)|(0.566)|(0.0306)|(0.0190)|(0.0278)|
|Indus. Zone|0.709*|1.464*|0.756|0.0277|0.0442|-0.0103|
||(0.368)|(0.884)|(0.916)|(0.0544)|(0.0429)|(0.0552)|
|Female manager|-0.645|-1.274|-0.629|0.0379|0.00325|0.0309|
||(0.656)|(1.348)|(1.370)|(0.666)|(0.0378)|(1.332)|
|Technology|-0.436|2.560**|2.996**|0.145**|0.0428|0.110**|
||(0.404)|(1.236)|(1.225)|(0.0584)|(0.0385)|(0.0545)|
|R & D|0.803|1.729|0.926|0.0266|-0.0557|0.0809|
||(0.653)|(1.459)|(1.496)|(0.0651)|(0.0443)|(0.0624)|
|Good manag. practices|0.186|-0.478|-0.664|-0.0119|0.0105|-0.0241|
||(0.354)|(0.791)|(0.837)|(0.0579)|(0.0645)|(0.0616)|
|Training|-0.0394|1.266|1.306|0.199***|0.00408|0.201***|
||(0.278)|(0.882)|(0.915)|(0.0580)|(0.0521)|(0.0626)|
|Constant|-1.083|5.129|6.212*|1.935***|2.045***|-0.122|
||(0.989)|(3.387)|(3.326)|(0.176)|(0.0891)|(0.171)|
|Gov. dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|2,143|2,143|2,143|2,142|2,130|2,129|
|R-squared|0.049|0.067|0.052|0.214|0.199|0.173|

*Demand shock is the sum of indicators of reported decline in quantity demanded and in export demand value. Gender gap is the change in the difference of the number of male and female employees after COVID ( ⍙ Male employment - ⍙ Female employment). Weights are used. The method of estimation is ordinary least squares with Huber-robust standard errors (reported in parentheses). Significantly different from zero at *10% significance, **5% significance level, ***1% significance level.

16
17 targets those with high marginal productivity of input use, and 3) whether it reaches households that did not use improved seeds and inorganic fertilizers before. The latter category is an important target group for market smart subsidies, as it is likely to meet both criteria, i.e. being constrained in the financial markets (as reflected in their poverty) and displaying high marginal productivity (Ricker-Gilbert, Jayne, and Black, 2009). Potential displacement of commercial input purchases, a continuous concern with input subsidies (Ricker-Gilbert, Jayne and Chirwa, 2011) is also a non-issue for this latter group, thereby increasing additionality of the voucher. It renders this group particularly attractive from an efficiency perspective.

To analyze the targeting performance based on poverty, two measures of poverty are used—the poverty measure calculated in Christiaensen and Pan (2010) and a poverty measure using region median consumption per capita level as the poverty line. About half the population would be eligible for a voucher each time.<sup>20</sup> For targeting based on efficiency, the median values in each village of the households’ marginal productivity of improved seeds and fertilizer on maize/rice (as observed in the previous survey round) are used as cut-offs. This implies that about half of the population is considered as target population. The third targeted group are the non-users of improved seeds and fertilizers, which made up about 50 and 67 percent of the households in the previous survey round.<sup>21</sup> Broadly speaking, and quite strikingly, the targeting differentials hover all around zero irrespective of the targeting criterion (first column, Table 3). This suggests that decentralized distribution of the input vouchers does not deviate much from what would have been obtained from a random/uniform distribution of these vouchers. Nonetheless, there are some nuances to this overall observation. The targeting performance appears slightly better

> 20 Note that only 26 percent of all households received (at least) one voucher.  Consequenlty, the targeting differential T for the whole sample ranges between [-0.52, 0.52].

> 21 By way of comparison, in 2003, 32% of the households used inorganic fertilizer, in 2004 35% and in 2009 39%.
**25**

- 4.2 **Impact of a US only financial crisis on Durables verus Non-Durable goods in the United States**

<!-- Start of picture text --> Production and Imports of Durables and Non-Durables<br>10<br>5<br>0<br>0 1 2 3 4 5 6 7 8 9<br>-5<br>-10<br>-15<br>-20<br>durables - imports non-durables - imports durables - production non-durables - production<br>% deviation from baseline<br><!-- End of picture text --> _Data source:_ G-Cubed model simulations exports of 5 percent below baseline in 2009. China’s trade balance worsens, but note how small the effect is: barely 1 percent below baseline (as a percent of GDP).

Note also the net small effect on China’s real GDP even though China’s exports are a large proportion of their GDP. When the United States alone is affected by the crisis, there is a small combined effect on China of a reduction in real GDP of 0.75 percent below baseline in 2009 and a positive effect from 2011 onwards. Looking at China’s real investment provides the answer. Because investment prospects in the United States are now dire under the combined scenario, money flows elsewhere, one recipient of which is China. China’s real investment could be 3.5 percent above baseline in 2011 and 2012, in response to the relatively better investment prospects. China gains at the United States’ expense. The favourable stimulus from extra investment largely offsets and eventually outweighs the negative effects from the loss of exports to the United States.

The conclusion is that the financial crisis which started in the United States, had it been confined to the US alone, would not have had dire consequences for the world economy. Of course the real story is different. Contagion and rising risk premiums everywhere have caused a different scenario. When everyone is affected the consequences for the United States also depends on who and how other countries are affected.
copyright infringement prevention; and ii) professional personnel training.<sup>64</sup> The following points are among the many outcomes from Korea’s education strategy: Figure 10 “A total of 1,355,207 persons were educated under the programs from 2008 to 2012.” Copyright-related content has been introduced “in textbooks of elementary, middle and high schools.” In 2010 “the Korea Copyright Commission was officially authorized as a copyright distance learning education and training institute by the Ministry of Education, Science and Technology.” And in 2012 the Commission “was approved as a “lifelong education institute” by the Seoul Jungbu District Office of Education.” This laid “the groundwork for lifelong education.”<sup>65</sup> _Source: 2012 Annual Report on Copyright in Korea_ The intellectual property battle is hardly conclusive. Yet (other things being equal) intellectual rights have potential to benefit creative workers in the developing world. While Korea’s approach may be hard to replicate, it provides a useful study to other nations. Moreover, “Korea’s infringement rate of the potential licensed works market decreased to 16.2 percent in 2012 from 21.6 percent in 2009. The U.S. Trade Representative has accordingly excluded Korea from its intellectual property rights watch list for the fifth straight year (2009~2013).”<sup>66</sup>

***

Although intellectual property is a major concern, it is not the only area in need of systematic policy development. There is a need to consider crafting other policies. Below are a few considerations:

- Table creative trade on national strategies of economic diversification

- Create national cultural trade commissions that facilitate paper work, professional training (for artists) and the like

- Give tax credits and rural grants for such artists as rural artisans

- Develop and fund local cultural institutes

- Develop a global protocol on temporary work visas for artists*

- Engage the private sector effectively — as some scholars have suggested, a modest percentage of profits from extractive industries, for example, could be used to develop culture and trade initiatives.

- Develop national branding strategies that use cultural initiatives

- Develop education training by craft makers as an export in trade in services

- Develop effective cultural trade agreements

> 64 Ibid., 55.

> 65 Ibid.

> 66 Yoo Jinryong, A Message from the Minister of Culture, Sports and Tourism.

> * See ‘Temporary Artist Visa’ — Patrick Kabanda. 2014. _The Creative Wealth of Nations_ , 44.

21
insufficient to meet the local demands for services, and provide little to no flexibility for local service providers on how resources are used (Musgrove 2010).

Plan Nacer was launched in 2004 following the deterioration in maternal and child health indicators resulting from the 2001 economic crisis. Plan Nacer is designed to improve the health status of uninsured pregnant women and children by channeling more resources to the public health care system and creating incentives to use those resources more efficiently. The program covers women during pregnancy and up to 45 days after birth (or the loss of the fetus). Coverage for children is up to age six and concentrates on services during the first year. All other care outside of the Plan Nacer package of benefits must be covered by regular provincial health services.

The program was first launched in nine provinces in northern Argentina and then expanded to cover the rest of the country beginning in 2007. By 2012 the program was operating nationally with high coverage levels.<sup>2</sup> The nine northern provinces where Plan Nacer was initiated have the highest levels of maternal and child mortality and experienced the most severe decline in health indicators during the 2001 crisis. The provinces have a population of 8.26 million<sup>3</sup> and the uninsured population of women and children in 2008 was estimated at 68,514 women and 569,984 children.<sup>4</sup> By December 2008, the program’s enrollment had reached 82 percent of the eligible population, a total of 1,886 contracted health providers (57.4 percent of all providers), and transferred over $81 million<sup>5</sup> (Musgrove 2010).

# **2.2. Payment to Provinces**

Plan Nacer was designed to supplement the existing public financing system with an innovative P4P model that incentivizes the provision of quality priority maternal and infant health services. The program is overlaid on the existing national and provincial health financing structures, and represents additional funding beyond the historical administrative budgets. Through Plan Nacer, the national government reimburses provinces on a per capita basis at a maximum cost of $8 per person per month. The provinces receive $5 (60 percent of the maximum per capita payment) for every eligible individual enrolled in the program and up to an additional $3 (40 percent of the maximum payment) if health targets for the eligible

> 2. In 2012 the program was expanded to include additional beneficiary populations and prioritized services under the name “SUMAR.”

> 3. Argentina. National Institute of Statistics and Census  2010.

> 4. Argentina. National Ministry of Health. Secretary of Health Promotion and Programs. Plan Nacer Central Implementation Unit (Unidad Ejecutora Central [UEC]), 2008 a.

> 5. Argentina. National Ministry of Health. Secretary of Health Promotion and Programs. Plan Nacer Central Implementation Unit (Unidad Ejecutora Central [UEC]). “Informes de Gestión: Plan Nacer — 2004– 2009.” Buenos Aires. Argentina.

5
Assessing the Accuracy of Electricity Demand Forecasts in Developing Countries<sup>∗</sup> Jevgenijs Steinbuks Development Research Group, The World Bank JEL: Q47 Keywords: electricity demand forecasting, time-series econometric models

> ∗Acknowledgments: The author thanks Deb Chattopadhyay, Vivien Foster, Arthur Kochnakyan, Herman Stekler, Mike Toman, Joeri de Wit and the seminar participants at the Oxford Institute for Energy Studies and the World Bank for their helpful suggestions and comments. Responsibility for the content of the paper is the author’s alone and does not necessarily reflect the views of his institution or member countries of the World Bank.
Twenty-nine percent of the employees in the sample belong to trade unions. The data show that female workers are not less unionized than male workers. In contrast, the data show an education-unionization gap; workers who have completed university education and those with just primary education fall below the average level of unionization (see table 4).

**Table 4. Union Membership and Education (percent)**

||||T|otal|
|---|---|---|---|---|
|Educational level completed|Union|Non-union|Percent|Number|
|Full sample|||||
|Percent|28.7|71.3|100||
|None|23.2|76.8|100||
|Primary school|14.7|85.3|100||
|Middle school|27.4|72.6|100||
|Secondary school|28.1|71.9|100||
|Vocational school|37.9|62.1|100||
|Professional and technical schools|34.3|65.7|100||
|University|0.0|100.0|100||

_Note:_ The values show the percentage of each category of workers vvith each level of education. For example, 14.7 percent of the manufacturing workers in the sample who have completed primary school belong to unions.

_Source:_ Round Three of the 1994 Regional Program on Enterprise Development for Ghana.

**Table 5. Union Membership and Occupation (percent)**

||||T|otal|
|---|---|---|---|---|
|Occupation|Union|Non-union|Percent|Number|
|Full sample|||||
|Percent|28.7|71.3|100||
|Administration|40.2|59.8|100||
|Commercial|33.3|66.7|100||
|Manager|26.2|73.8|100||
|Production|24.1|75.9|100||
|Professional|16.7|83.3|100||
|Support|40.4|59.6|100||

21
21 most active. Withdrawal of European lenders in the Middle East region is also believed to underlie the recent rush to issue Islamic bonds.<sup>15</sup>

**Figure 16. Change in Cross-Border Claims of BIS reporting Banks by Region and by Country, 2006:Q1–2011:Q4**

<!-- Start of picture text --> CEE MENA and SSA<br>120 40<br>100 30<br>80<br>20<br>60<br>10<br>40<br>20 0<br>0 -10<br>-20<br>-20<br>-40<br>-30<br>-60<br>-80 -40<br>Bulgaria Baltics Czech Republic United Arab Emirates Egypt, Arab Rep. Kuwait<br>Croatia Hungary Poland Morocco Qatar Saudi Arabia<br>Romania Russian Federation Serbia, Rep. of Tunisia South Africa Other<br>Turkey Other Total Total<br>LAC ASIA<br>40 150<br>30<br>100<br>20<br>50<br>10<br>0<br>0<br>-10<br>-50<br>-20<br>-30 -100<br>-40<br>-150<br>-50<br>China Indonesia India Korea, Rep. Malaysia<br>Argentina Brazil Chile Colombia Mexico Peru Other Total Philippines Singapore Thailand Other Total<br>Change, USD Billions Change, USD Billions<br>Q1-06 Q3-06 Q1-07 Q3-07 Q1-08 Q3-08 Q1-09 Q3-09 Q1-10 Q3-10 Q1-11 Q3-11 Q1-06 Q3-06 Q1-07 Q3-07 Q1-08 Q3-08 Q1-09 Q3-09 Q1-10 Q3-10 Q1-11 Q3-11<br>Change, USD Billions<br>Change, USD Billions<br>Q1-06 Q3-06 Q1-07 Q3-07 Q1-08 Q3-08 Q1-09 Q3-09 Q1-10 Q3-10 Q1-11 Q3-11<br>Q1-06 Q3-06 Q1-07 Q3-07 Q1-08 Q3-08 Q1-09 Q3-09 Q1-10 Q3-10 Q1-11 Q3-11<br><!-- End of picture text --> Source: BIS, Consolidated Banking Statistics, table 9C.

15 See _The Economist_ , ―Reshaping Banking,‖ April 19, 2012, http://www.economist.com/node/21553015/print.