rafmacalaba/datause-encoder-data huggingface dataset:
COVID-19 spread mitigation measures. Business closure due to COVID-19 and lack of customers (which could partially be attributed to the pandemic) are the most widely reported reasons both by male and female-headed households for loss in enterprise income (Figure 7).

Female-headed households who earn their main livelihood from enterprise income have particularly experienced income losses (Table 6, Column 3). Compared to male-headed households, a larger proportion of female-headed households reported non-agricultural business or enterprise income (47 percent) and help from others as their main source of income. These are also income sources from which the largest proportion of total households reported having decreased income (around 70 percent). Of the male-headed and female-headed households who reported enterprise income as their main source of income, a statistically significant larger portion of female-headed households reported decreased income (81 percent) compared to male-headed households (72 percent).

The significant coefficient for female-headed households reporting income loss from non-farm enterprises during COVID-19 is not unexpected. From Ngatia et al. (2021), which uses ECOSIT4 (household budget survey), we know that although women own 57 percent of the non-farm enterprises, they make 77 percent less profit than male-owned enterprises. Female entrepreneurs, on average, have a lower level of education and have less access to electricity, running water, machinery, or telephone for their business. Female entrepreneurs who do have access to electricity or phone have lower returns from owning them, indicating underlying discrimination. Although their analysis is not exclusive to female heads of households,<sup>13</sup> but to all women, we could assume that female heads of households face similar constraints in running their business if not additional and more severe constraints. These existing vulnerabilities could render them more at risk of reduced income during the COVID-19 pandemic. For example, if customers are unable or unwilling to visit the enterprises during COVID-19 to buy commodities physically, they may alternatively place orders through the telephone; and women have less access to such technologies.

> 13 In fact, 27 percent of the female entrepreneurs in their nationally representative sample were divorced, widowed, or separated, as such by default female heads of households.

14
FE specification of model 2.24 The figures reported are evaluated at the means of the sample on which table 3 was based.

We find that the conventional factors did not enhance productivity overall. Capital accumulation increased average productivity only by 0.6%, and the expansion of labor force itself actually reduced labor productivity by 2.8%. The increasing share of average employees in the category of management personnel and "other employees" reduced productivity by an additional 1.3%. The increasing share of inputs purchased through the market reduced average productivity by 7.2%.

The growth accounting results clearly demonstrate that the productivity growth came mainly from decentralizing management. New management improved the average productivity by an impressive 9.9%. Furthermore, the decentralization of wage controls--W_DISCRETION and W_ELASTICITY--further contributed to a productivity increase of 10.9%. This demonstrates the importance of employee incentives, and the extent to which employees shirk when the government controls their wages. Finally, the decentralization of production decision rights--AUTONOMY, and decreased S_PLANQUAN--improved productivity by 13.2% percent.

The growth accounting results do not support the conventional wisdom about the importance of decentralizing cash flow rights of firms. The increased marginal retention rates accounted for only 0.8% of productivity growth, an effect far below those associated with decentralizing management.

24We have also conducted growth-accounting based on baseline model, and obtained similar results.

28
becoming increasingly tenuous. Work is currently underway at the Bank to expand the country coverage of the MUV index. Our forecast for the increase in the volume of developing countries’ exports of 5.5 percent a year proved to be too low (it actually grew by 7 percent a year), partly because we underestimated both the speed of convergence of emerging economies and the strong interplay between international trade and modern technology, which led to faster economic integration, particularly in East Asia. We also did not sufficiently emphasize the emergence of regional trade agreements and evolution of supply chains.

4. Although international oil prices during the 1990s moved less dramatically than in the 1970s and 1980s and OPEC members’ coordinated adjustments of oil production succeeded in reducing market volatility, hostilities in the Middle East in 1990–91 (Iraq’s invasion of Kuwait and the subsequent Persian Gulf War I) as well as the East Asian financial crisis of 1997–98 brought about excessive volatility at the beginning and the end of the decade. However, a solid recovery followed in a more integrated oil market, that had adjusted to the post-Soviet world and the resulting increase in economic integration. Our baseline forecast of the real increase in the price of oil during the 1990s (3.5 percent a year) and increases in the share of OPEC in world production (from about 29 percent in 1989 to about 40 percent by 2000) proved to be only slightly overoptimistic (compared to actual increase of about 2.5 percent a year in real oil price and the actual share of OPEC in world oil production of 38 percent).

5. The World Bank used the London Interbank Offered Rate (LIBOR) on six-month U.S. dollar deposits as the principal international interest rate. The primary focus was on the real rate, defined as the nominal rate adjusted by the U.S. GDP deflator. In fact, over longer time horizons, it was the real rate that was predicted first, with the corresponding nominal rate arrived at using the inflation prediction. Our projection for real international interest rates in the 1990s was reasonably accurate: the actual annual average for the decade (3.2 percent) fell between the baseline (2.7 percent) and the low-case (3.8 percent) projections. Nominal interest rates in the 1990s, however, were considerably lower than our projections (by about 240 basis points on average in the base case). The error in predicting nominal interest rates is an immediate—one could say arithmetical— 49
Table 11 presents the results of a simplified analysis of potential fuel cost savings from substituting HFO for diesel in the reference year for the five countries listed above.<sup>8</sup> The results indicate there could be significant cost savings for those countries with a high dependence on diesel, with the QFD being reduced by 25 percent in Rwanda to as much as 63 percent in Liberia. These results are intended to be illustrative of the short term opportunity to reduce cost of generation.

**Table 11: Simplified analysis of cost savings from switching from diesel to HFO based generation**

||**% of electricity dispatched**<br>**to grid generated using**<br>**diesel**|**Potential fuel**<br>**savings as % of**<br>**GDP**|**Base-case**<br>**QFD as % of**<br>**GDP**|**Potential fuel**<br>**savings as % of QFD**|
|---|---|---|---|---|
|Liberia|100%|0.22%|0.36%|63%|
|Burkina Faso|57%|0.45%|0.99%|46%|
|São Tomé and Príncipe|91%|1.77%|6.08%|29%|
|Comoros|100%|1.10%|4.13%|27%|
|Rwanda|50%|0.26%|1.04%|25%|

_Source_ : World Bank staff calculations based on utility data.

In the medium to long term, there are further opportunities to reduce the cost of service. While a quick and practical solution in many contexts, HFO-based generation still represents a relatively expensive source of electricity, renders countries vulnerable to international oil price shocks, and has relatively high negative environmental externalities. Where alternative domestic sources of energy are not available, power trade can open opportunities for accessing lower-cost, clean and reliable power. For example, the OMVG interconnection ( _Organisation pour la Mise en Valeur du fleuve Gambie,_ The Gambia River Basin Organization) in West Africa will allow Senegal, The Gambia and Guinea Bissau to access power imports from hydropower in Guinea at a fraction of the cost of HFO- and diesel-based generation, which dominates the existing power mix in those countries. Likewise São Tomé and Príncipe is preparing to rehabilitate a dilapidated hydro plant.

> 8 For the purposes of this simplified analysis, it is assumed that 5 percent of the diesel consumed would still be used, for example, in the process of warming up engines from sitting idle. Heat rates of 9.81 and 11.13 gigajoules per megawatt-hour are assumed for HFO and diesel, respectively. Fuel prices in the reference year are used (Table 12).

52
In addition, there is a question of how many households that are ineligible for the humanitarian assistance would, in fact, be eligible for the government support. The analysis show that among households that are not deem extremely vulnerable by the SEVAT, about 15 percent would be referred for government support under the program that includes only the bottom 18 percent nationally. The share decreases to 9 and 3 percent when the other two groups are included one by one and cumulatively. That means that a non-trivial number of households not considered extremely vulnerable by the humanitarian program would nonetheless be eligible for government assistance. From a policy perspective, this represents another trade-off. More total households would be successfully referred to the government programs if those _not_ eligible for humanitarian assistance were also assessed; but the referral success rate among this group would be relatively low, possibly causing disappointment and frustration among the referral failures.

For the Legacy VA, about 20 percent of eligible households eligible for the humanitarian support under the extremely vulnerable group would be eligible for government support, and like that of the SEVAT, the proportion of cross eligible households decreases as the cutoff is expanded to include the less vulnerable groups. Similarly, 22 percent of those ineligible under the Legacy VA extreme vulnerable group would be eligible for the referral into the government’s SSN program. While the interpretation of the Legacy VA model is similar to that of the SEVAT, the observed difference is the share of cross-eligible households identified by the two models. Indeed, as shown in Table 3, the SEVAT tends to successfully refer more eligible households to government support compared to the Legacy VA, and the share of ineligible households referred is higher for the Legacy VA compared to the SEVAT. As discussed earlier, the discrepancies are caused by the closer similarity between the SEVAT model and the MoLSA model than the Legacy VA and MoLSA.

_Table 3: Share of humanitarian beneficiary households that are referred to government support via the Full PMT model_

|||GOI So<br>|cial assistan<br>|ce simulated<br>|program co<br>|verage<br>|
|---|---|---|---|---|---|---|
|Humanitarian caseloads|PMT cutoffs|Btm 15%|Btm 18%|Btm 25%|Btm 30%|Btm 35%|
|at<br> <br>SEVAT|Extreme vulnerable (R3)|36|40|62|64|70|
|offs|Extreme-mid vulnerable (R2)|33|36|56|59|65|
|cut|Vulnerable (R1)|29|32|54|57|64|
|le<br>ent<br>Legacy VA|Extreme vulnerable|19|20|38|41|48|
|igib<br>ffer|Extreme-mid vulnerable|22|24|43|46|52|
|El<br>di|Vulnerable|23|25|45|47|54|
|el<br>ib<br> <br> <br>ff<br>SEVAT|Extreme vulnerable (R3)|14|15|32|35|42|
|In<br>ig<br>le<br>at<br>di|Extreme-mid vulnerable (R2)|8|9|26|29|36|

17
<!-- Start of picture text --> o India: Agenc’gency<br>B<br>.°)]<br>Say<br>o<br>c<br>°<br>=<br>a:OL<br>®<br>=¢ ee<br>¢<br>Higher education<br>5 ° Urban<br>S<br>e<br>nf<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing agency status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Bangladesh: Agency<br>oO<br>o<br>a<br>aN<br>°<br>Cc<br>2<br>3e-<br>5<br>£o<br>_<br>; ——EE<br>So<br>7 Higher education<br>8<br>5<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing agency status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text -->
-<br>Pakistan: Agency<br>o<br>a<br>aN<br>3 Higher education<br>5<br>s<br>oe2<br>5<br>g<br>¢5<br>g<br>3<br>i<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing agency status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> ro) Nepal: Agency<br>&<br>o<br>e<br>5%<br>g<br>s3=Higher education-<br>oe<br>o| |<br>So Urban<br>2<br>3<br>ii<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing agency status on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
# <u>Annexes</u>

_ANNEX 1. ESTIMATING EDUCATION EXPENDITURES_ The estimation reported in this report is based on a panel data of 46 developing and developed countries from 1972 to 2000. Budget data are drawn from the International Monetary Fund’s Government Financial Statistics. The source of other control variables is the World Bank’s World Development Indicators. Unobserved country characteristics were not controlled for, because the objective of the exercise was to compute the expected value of education spending given a set of economic demographic and geographical characteristics but purposely excluding unobserved historical patterns of spending (else the fitted values would only be useful to forecast the currently observed levels). Controlling for unobserved country specifics would generate expectations given the historical (and other unobserved relevant dimensions) and country-specific levels of education spending. A fixed-effects specification that controls for a country’s specific unobserved characteristics generates even lower predictions (with an expected education spending share of approximately 12 percent of the consolidated budget).

The specification used can be written as: Where: _G_ (.): Denotes the logistic transformation applied to the model due to the fractional nature of the dependent variable (G(x) = log(x/1-x)).

_Comp_ : is the ratio of education expenditures to the total amount of public expenditures.

_X_ : is a set of control variables, which include population, population density, GDP per capita, a measure of fiscal decentralization (sub-national expenditure share), and budget balance.

_Dev_ : denotes a slope dummy defined as ( _DEC_ * _Industrial Dummy_ ). This is introduced in the model to account for possible different impact of decentralization depending on the level of economic development.

_Region_ : Regional dummies (LAC, MENA, NA, EASA, Sub-Saharan, relative to ECA) 28
- BPOWatch India (2008). Best of 2008 and Challenges for 2009. Retrieved March 5, 2010, from <u>http://www.bpowatchindia.com/BPO_special_features.html</u>

- Business-In-Asia (2008). Business Process Outsourcing (BPO) Hitting a Speed Bump in Asia as a Result of Financial Crisis. Retrieved March 7, 2010, from http://www.business-in- <u>asia.com/news/business_process_outsourcing.html</u>

- Business Processing Association of the Philippines. (2007). Offshoring and Outsourcing Philippines: Roadmap 2010: Business Processing Association of the Philippines. <u>http://www.bpap.org/bpap/publications/bpap_roadmap.pdf.</u>

- Business Standard (2010). After Two-Year Lull, Top Indian IT Firms Plan 8-12% Salary -

- Increases. Retrieved March 8, 2010, from <u>http://www.business standard.com/india/news/after-two-year-lull-top-indian-it-firms-plan-8-12-salaryincreases/387460/. March 4</u>

- BusinessMirror (2010). RP's BPO Revenue Grows 19% in 2009. Retrieved March 7, 2010, from <u>http://www.businessmirror.com.ph/index.php?option=com_content&view=article&id=21 636:rps-bpo-revenue-grows-19-in-2009&catid=23:topnews&Itemid=58. February 10.</u>

- Castillo, Mario. (2010). Offshore Services Industry-CORFO,Chile. Personal communication with CGGC research team. February 18.

- CBR News (2009). FIS to Acquire Metavante: Competitive Dynamics and Implications. Retrieved March 2, 2010, from

<u>http://enterpriseapplications.cbronline.com/comment/fis_to_acquire_metavante_competit ive_dynamics_and_implications_090409. April 09</u>

- Chakrabarty, Sujit K., Prashant Gandhi and Noshir Kaka. (2006). The Untapped Market for Offshore Services: McKinsey. <u>http://www.mckinseyquarterly.com/Operations/Outsourcing/The_untapped_market_for_ offshore_services_1772.</u>

- Crain's Chicago Business (2010). Legal Outsourcer Mindcrest taps India for Talent. Retrieved -

- March 1, 2010, from <u>http://www.chicagobusiness.com/cgi</u> = = =

- <u>bin/blogs/ecity.pl?plckController Blog&plckScript blogScript&plckElementId blogDes t&plckBlogPage=BlogViewPost&plckPostId=Blog%3a16ea2629-7e90-46f0-a706dd6152764513Post%3a9a9aafb0-d460-464d-9126-</u>

- = =

- <u>12285381178c&plckCommentSortOrder TimeStampAscending&sid sitelife.chicagobus iness.com. January 26.</u>

- Current IT Market (2009a). Deloitte to Hire 15,000 People in India. Retrieved March 8, 2010, from <u>http://www.currentitmarket.net/2009/11/deloitte-to-hire-15000-people-inindia.html. November 11.</u>

- --- (2009b). IT's Not All That Hot for Fresh Recruits. Retrieved March 4, 2010, from <u>http://www.currentitmarket.net/2009/11/its-not-all-that-hot-for-fresh-recruits.html.</u> November 4.

- Datamonitor. (2009). Global IT Services. London: Datamonitor.

- Dossani, Rafiq and Martin Kenney. (2007). The Evolving Indian Offshore Services

- Environment: Greater Scale, Scope and Sophistication: Sloan Industry Studies Working Papers. http://www.industry.sloan.org/industrystudies/workingpapers/.

- Duke Offshoring Research Network & Booz&Co. (2007). Offshoring 2.0: Contracting Knowledge and Innovation to Expand Global Capabilities.

ECLAC. (2008). "Offshore corporate services in Latin America and the Caribbean." 28
the (unobservable) characteristics determining program participation and, hence, is likely to represent a credible comparison group.

A baseline survey was conducted just after the beginning of program (November 2004) and one year later (December 2005) to both participants and non-participants in the program.

The descriptive statistics give an indication that a selected subsample of _Jefes_ beneficiaries is likely to be attracted by this program. When we compare the socio-demographic profile of those beneficiaries included in the sample with the average _Jefes_ participants in the area we find that the sample is more likely to include female heads of households, slightly more educated and older than the average _Jefes_ participant.

The effectiveness of the program depends ultimately on whether the program raises significantly the labor supply and income of the beneficiaries. We use difference in difference methods to evaluate the short-term impact of the program on different labor market outcomes.<sup>13</sup> Our results show that beneficiaries of the self-employment program are less likely to maintain or to find wage jobs outside the project, especially in the case of male beneficiaries. We also find evidence that the program significantly increases the total hours of work (either in the market or in the program). Finally, while the program on average did not result on income gains to the participants on average, there are specific subgroups of beneficiaries who stand to benefit the most, namely younger and more educated beneficiaries. When we exploit the geographical variation of municipal participation, we find that the signs and significance of the effects are unchanged, though the magnitudes on hours and income effects are overestimated. Overall, we interpret this evidence as being supportive of the second ‘entrepreneurial’ view, where the jumpstarting self-employment through start-up capital and basic business education is to have a

> 13 This program was implemented at a small scale.  For this reason, we have abstracted from any indirect effects of the program on the local labor markets, through possible effects on non-participants (general equilibrium effects).

7
Table 15 Comparing different estimates of the poverty headcount rate at the US$1.90 poverty line: backcasted, interpolated, imputed methods

||HNLSS<br>2009/10;<br>2018/19<br>NLSS|Backcast, pass-<br>through=1|Interpolated series<br>without 2009/10|Imputed<br>poverty rate at<br>US$1.90, GHS<br>Original model|
|---|---|---|---|---|
|2009|56.40%|46.3%|45.9%||
|2010||42.8%|43.5%|435%|
|2011||41.8%|42.1%|.|
|2012||40.3%|41.2%|415%|
|2013||38.9%|37.2%|.|
|2014||37.4%|35.2%||
|2015||36.8%|35.2%|405%|
|2016||38.0%|37.6%|.|
|2017||38.5%|38.6%||
|2018||39.0%|38.9%|419%|
|2019|39.09%|39.1%|39.2%|.|

Note: the table shows a summary of the different estimates produces in this analysis. Column 1 shows the non-comparable survey-based estimates from 2009/10 HNLSS and 2018/19 NLSS. Column 2 shows the backcasted series using a pass-through of 1 and using sectoral GDP data mapped to household head’s employment sector to backcast the 2018/19 consumption vector. Column 3 shows the results of interpolating between the 2003/04 NLSS and 2018/19 NLSS without using the HNLSS 2009/10 estimate and following PovcalNet’s methodology (Prydz et al. 2019). Column 4 and 5 show the results of survey-to-survey imputations using GHS data from different waves and 2018/19 NLSS as a baseline survey. Column 4 shows the imputed estimates when using a full set of non-monetary indicators, column 5 shows the imputed estimates when excluding from the consumption model potentially seasonal variables such as food-consumption dummies and employment indicators.

Table 16 Main estimates using different sources of population data, WDI and NBS

||Number of<br>line (million<br>estimates<br>|poor at the US$1.9<br>s) – using NBS pop<br>|0 poverty<br>ulation<br>|Number of poor at<br>using WDI populati<br>|the US$1.90 poverty li<br>on estimates<br>|ne (millions) –<br>|
|---|---|---|---|---|---|---|
||NLSS|Backcasts<br>(pass-through<br>1)|S2S<br>Imputation|NLSS / PovcalNet|Backcasts (pass-<br>through 1)|S2S<br>Imputation|
|2009/10|91.1|||89.4|||
|2009||73.8|||71.4||
|2010||70.0|||67.8||
|2011||70.2|||68.1||
|2010/11|||72.0|||69.9|
|2018/19|80.8|||78.5|||

Note: the table shows a summary of the main estimates of number of people living below the US$1.90 poverty line using different population data. The left-hand side panel shows the number of extreme poor (in million) using the NBS population data, the right-hand side panel uses WDI population data (used throughout the rest of the paper).

40