rafmacalaba/datause-encoder-data huggingface dataset:
total of $625 million to support CONAFE. The World Bank operated several similar loans between 1991 and 1998. The Inter-American Development Bank has operated the Integrated Program to Abate Educational Underachievement (PIARE) and the Distance Education Program. These loans provided a nominal total of nearly two billion dollars between 1991 and 2003 (Table 1).

|**Table 1. International Fi**<br>|**nancial**<br>|**Support for**<br>|**CONAFE**<br>|
|---|---|---|---|
|**Project name**|**Years**|**Amount**|**Details**|
|First Primary Education|1991-|$250|World Bank. Supported compensatory programs|
|Project (PARE)|1997||for primary education in the four Mexican states<br>with the highest incidence of poverty.|
|Initial Education Project<br>(PRODEI)|1993-<br>1997|$74<sup>4</sup>|World Bank. Supported human resource<br>development of educators; educational materials,<br>and institutional strengthening|
|Integrated Compensatory|1995-|$393|Inter-American Development Bank. Supported|
|Education Program<br>(PIARE)|2000||CONAFE’s Compensatory Programs in the 17<br>states that PAREB did not cover.|
|Second Primary|1994-|$412|World Bank. Supported compensatory education|
|Education Project<br>(PAREB)|2001||in 14 Mexican states.|
|Distance Education|1998-|$171|Inter-American Development Bank. Supported|
|Program|2003||_telesecundaria_education.|
|Basic Education|1998-|$625|World Bank. Supports basic compensatory|
|Development (PAREIB)|2006||education in all Mexican states.|
|Comprehensive|2003-|$420|Inter-American Development Bank. Supported|
|Community Education|||CONAFE’s Community Education Programs in|
|Program|||all Mexican states.|

Amounts are in nominal US$ million. Sources: World Bank 1991, World Bank 1994, Inter-American Development Bank 1997, World Bank 1997, World Bank 1998, World Bank 2002, Inter-American Development Bank 2003.

That international funding supports a relatively low-cost program. CONAFE’s real costs have grown in the last decade, and CONAFE’s compensatory programs now cost just over $50 per student per year. Those costs compare to a typical cost of $527 per _telesecundaria_ student and $477 per general middle school student (Castro, Wolff and García 2001) (See Table 2).

> 4 Originally $80m, but in 1995, $6m of the loan was canceled due to Peso devaluation.

6
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a benchmark without market failures are generally small. However there are exceptions, indicating that careful analysis is needed of the operation of a given market in a country.<sup>17</sup>

# **1.2  Natural resource markets**

As is true for agricultural markets, governments have a long history of intervening in markets for natural resources, both renewables and non-renewables. Contrary to policies that affect agriculture – where import protection has tended to dominate – in the case of natural resources the focus has been much more on export restrictions. Protection rates on imported nonagricultural commodities tend to be relatively low because many of these commodities are intermediate inputs, for which powerful user industries apply pressure against high rates of protection. On the export side, numerous countries have intervened on export markets in an effort either to raise export prices or to support specific industries. Export restrictions are sometimes also justified on environmental grounds, although any measures targeted efficiently to environmental problems would generally target production or consumption, rather than exports. Data reported in WTO (2010) suggest that it is mostly developing countries implement export restrictions. Export taxes on natural resources account for about one-third of all export taxes imposed—some 11 percent of world trade in natural resources is covered by such taxes, with timber, iron, copper, pearls and gemstones being among the most frequently affected.<sup>18</sup> Although export restrictions are more frequently observed than import protection, the underlying motivations have been very similar, either exploiting market power (terms of trade) and or political economy/industrial policy—an effort to subsidize/tax certain industries or activities. In cases where a country does not have the ability to affect its terms of trade, the objective underlying the use of export restrictions is frequently to subsidize the domestic processing or another downstream industry by providing it with access at less than world market prices. Such support to specific industries may also be granted through direct subsidies or tax concessions. Many energy producers subsidize domestic consumption by charging nationals

> 17 See Delpeuch and Leblois (2011) for a recent example of such analysis.

> 18 Export restrictions on natural resource products accounted for one-third of the 7,328 notified export restrictions in the time period covered by WTO (2010).

16
commodity heterogeneity (the fact that not all commodity costs share similar features). A set of trade-pair dummies is included to control for differences between countries, and time dummies are included to control for time-specific effects.<sup>13</sup> Distance, economies of scale, and value-weight have substantial effects on transport costs, as expected. Our results for the countries in the Indian and Western Pacific Oceans show that doubling distance increase transport costs by 10 percent. This estimate falls in the lower range of estimates obtained by other studies (see table 1). In the early 2000s, increasing size and capacity in both ports and vessels resulted in gains from economies of scale. The results in table 5 confirm the presence of economies of scale: Unit costs decrease with weight. As Clark, Dollar, and Micco (2004) note, insurance fees are about 2 percent of the traded value, and they represent about 15 percent of total maritime charges. Because of the insurance component of transport costs, products with higher unit value have higher transport costs per unit of weight.

**Table 5 Determinants of maritime transport costs**

|Dependent variable: Unit transport cost<br>|(ln)<br>|||
|---|---|---|---|
|_Variable_|_(1)_|_(2)_|_(3)_|
|Oil prices*distance (ln)|0.02|0.10**|0.10**|
||(0.02)|(0.04)|(0.04)|
|Value-weight (ln)|0.60***|0.60***|0.60***|
||(0.03)|(0.03)|(0.02)|
|Containerization (percent)|0.19|0.24|0.25|
||(0.13)|(0.15)|(0.16)|
|Directional imbalance (percent)|–0.17*|–0.22**|–0.22**|
||(0.09)|(0.09)|(0.09)|
|Weight (ln)|–0.07***|–0.07***|–0.07***|
||(0.01)|(0.01)|(0.01)|
|Port efficiency|–0.22**|–0.24***|–0.23***|
||(0.08)|(0.10)|(0.10)|
|Exporting country GDP per capita (ln)||–0.17**|–0.17**|
|||(0.07)|(0.07)|
|Connectivity index|||–0.20|
||||(0.34)|
|�<sup>�</sup>|0.26|0.26|0.26|
|_F_-statistic|362.94|312.20|273.18|
|Number of observations|5,906|5,906|5,906|

_Note:_ Standard errors in parenthesis.

** Significant at the 5 percent level, *** significant at the 1 percent level.

13 Including these dummies controls for port-specific costs (average by country) over time. The time dummies are not always significant.

15
probability of a household being in the post-Covid-19 interview samples. The included covariates are similar to those used in the propensity score matching discussed in our principal methodology. Second, we consider the case of the QTT estimation in the context of the Comoros Covid-19 lockdown and its anticipation. Both treatment variables (Covid-19 lockdown impact and Covid19 anticipation) are defined as a dummy taking the value 1 if a household is interviewed either post-Covid-19 lockdown or after the president’s address on Covid-19, and zero otherwise, respectively. Finally, we explore the impact of Covid-19 at different points of the household expenditure distribution. We focus on household expenditure as it provides an outcome that can be observed in understanding household welfare distribution.<sup>13</sup>

**<u>Table 9: Quantile Treatment Effects using Log Per Capita Household Expenditure</u>**

||**10**<sup>**th**</sup>|**20**<sup>**th**</sup>|**50**<sup>**th**</sup>|**75**<sup>**th**</sup>|**90**<sup>**th**</sup>|
|---|---|---|---|---|---|
|**Covid-19 Impact**|-0.044***|-0.057***|-0.053***|-0.055***|-0.077***|
||(0.011)|(0.010)|(0.010)|(0.012)|(0.015)|
|**_Observations_**|28902|28902|28902|28902|28902|
|**Covid-19 Anticipation**|-0.042***|-0.056***|-0.064***|-0.065***|-0.089***|
||(0.011)|(0.010)|(0.010)|(0.012)|(0.015)|
|**_Observations_**|28902|28902|28902|28902|28902|

Note: Statistical significance: *** p < 0.01, ** p < 0.05, * p < 0.1 Controls in the treatment assignment equation: head of household age, education, and marital status; polygamous household; female-headed household; dependency ratio; the number of working-age individuals in the household; access to water, sanitation, and electricity; improved floor and roof; location (region and urban settlement) Table 9 provides the estimated impacts. The first sets of results show a reduction in household expenditure across the different quantiles. Households in the bottom quantile had a 4.3% (i.e., [e<sup>-</sup> 0.044-1] ×100) reduction in household expenditure due to the Covid-19 lockdown, with a similar pattern in the middle of the distribution. However, the negative impact observed is stronger for households in the upper distribution with a magnitude of 7.4% (i.e., [e<sup>-0.077</sup> -1] ×100) reduction. Thus, the effect of the Covid-19 lockdown is a reduction in household expenditure distribution with a more substantial impact at the top of the distribution. Similarly,

> 13 A detailed guide and understanding of the estimation method of the QTT can be found in Firpo (2007). The approach is based on close work on semiparametric estimation of the ATE (see Hahn, 1998; Heckman, Ichimura, Smith, and Todd, 1998). The semiparametric efficiency bounds are estimated as an asymptotic variance of the QTT estimator (Newey, 1990; Bickel, Klaassen, Ritov, and Wellner, 1993).

38
Equations 3 and 4 can be solved to derive unique values for ���� and ����.

Information about the ICP urban shares ��� for these three countries come from the Asian Development Bank (2014).<sup>41</sup> In contrast to the 2005 ICP, in which price collection was concentrated in urban areas, the 2011 ICP saw reportedly better participation in China, with 24% of the outlets where prices were collected being in rural areas. In India, 25.8% of locations surveyed were rural in the 2011 ICP, and for Indonesia, 38.7% of the outlets were in rural areas. Nevertheless, these are large-population countries and accounting for bias, even if small relative to other countries, may be quite important; and the rural-urban unpacking of the PPP is also used to provide separate extreme poverty estimates for rural and urban areas in these countries.

Data on the urban and rural poverty lines come from the national statistical offices of each country, or in some cases, the World Bank country poverty economists. Since China does not have an official urban poverty line (only a rural line), Chen and Ravallion (2008) use the ratio of national poverty lines computed by China’s National Bureau of Statistics (NBS), in collaboration with the World Bank, in 2002/3. For 2005, the line was 37% higher for urban compared to rural areas.<sup>42</sup> Based on changes in rural and urban prices for people living close to the poverty line, the urban-rural difference (for the poor) in China in 2011 is now estimated to be 28.5%. To arrive at this number, inflation for the urban poor was calculated using the bottom decile of the urban distribution. In 2011, the Chinese government announced a new rural poverty line, however no comparable urban line is yet available, and thus the old rural-urban ratio updated to 2011 is used here.<sup>43</sup>

> 41 From ADB (2014) “Appendix 1: Coverage by Type of Outlet and Location for Household Price Surveys”.

> 42 In addition to cost-of-living differences, this difference also reflects differences in the definition of consumption expenditures in the rural and urban surveys.

> 43 Starting in 2013, China’s NBS has conducted a unified national household survey, replacing the separate rural and urban surveys conducted in the past. In this context, NBS has also ex-post harmonized the separate rural and urban distributions from the 2011 and 2012 surveys to create unified national distributions that aim to be comparable with the new national survey. Estimates of extreme poverty in China using these ex-post harmonized national distributions for 2011 and 2012 and the un-adjusted

24
For low-income countries, the relevant question for poor households is not how much financial assets they have, but whether they have access to financial intermediaries at all. We have drawn on and synthesized recent work by several authors using information provided by banks and micro-finance institutions in combination with household survey data to produce estimates of access percentages for almost 150 countries. These estimates can be progressively refined as the results of a new wave of access surveys currently under way or planned become available.

The new access percentages are negatively correlated across countries with poverty rates, but the correlation is not a robust one and loses significance in multiple regressions that include mean per capita income. Thus the supposed anti-poverty potential of financial access remains econometrically elusive.

Data for developing countries is insufficiently rich to assess whether asset choice by households is consistent with rational choice, a question on which there has been much discussion for advanced economies. Once again, it is more a question of whether households actually use any of a range of different types of financial instruments, than of whether they have chosen the optimal quantities of each in their portfolio. Analysis of data from the EU-25 suggests a ranking of different financial instruments and products in terms of the level of income at which each will be widely used: checking and deposit accounts and their associated cards come first, followed by life assurance and then loans.

23
Customer type varies greatly with the sector in which informal businesses operate. The sectoral breakdown of typical customer types given in Panel B of Figure 9 offers a rather interesting pattern. Manufacturing businesses are most heavily reliant on personal connections, with more than half of all businesses in this sector reporting such connections with their typical customer. Unfamiliar contacts comprise the typical customer for less than 1 in 6 informal manufacturing businesses. In contrast, less than a third of retail businesses report personal connections with their typical customers, with 1 in 4 reporting the typical customers to be unfamiliar. This stark pattern is consistent with the potential explanation that institutional settings, particularly mechanisms of fulfilling agreements, shape informal businesses' customer base. The manufacturing sector is particularly prone to contract enforcement challenges such as the hold-up problem (e.g., Nunn, 2007). For example, for manufacturing to begin, customers may need to issue advance payments, and manufacturers need to be able to get paid in the event of disputes after the product is finished. In contrast, retail businesses, e.g., resellers in market stalls, or businesses in other services sectors, such as personal transportation or hairdressing, are less complicated in terms of ensuring that agreements are fulfilled since the provision of the service and payment can occur almost simultaneously. The increased risk that transacting parties may renege on agreements in the manufacturing sector likely contributes to considerably higher reliance on personal connections in this sector than in retail or other services.

Suppliers to the informal businesses are also very likely to be informal. Informal Sector Enterprise Surveys also enable a look at the upstream integration of the informal businesses within their local markets. In particular, businesses in Somalia and Zambia were asked whether their main suppliers are themselves, individuals, businesses, or others. Figures 10 displays data for five cities where this question was implemented. The most common suppliers to informal businesses are individuals, with a large variation across cities—from 43.5% in Lusaka (Zambia) to 88.5% in Mogadishu (Somalia). While the legal status of suppliers is not measured, it is conceivable that a large proportion of individuals that supply to informal businesses operate informal businesses themselves. This pattern again points to the informal sector being largely isolated from formal business activity. This picture is further supplemented by the shares of informal businesses that report themselves as their own main supplier, varying from 4.3% in Mogadishu (Somalia) to 21.7% in Ndola (Zambia). Indeed, such an extreme and likely inefficient level of self-reliance is almost 24
> coefficient estimates of the growth equations based on general government expenditure and

> central government expenditure for the countries for which data on both were available. We

> found them to be statistically the same.

> The second inerpretation is that our results reflect a problem in the link between public expenditures and outcomes. Earlier work has established that the stock of educated and healthy people, and of public infrastructure capital, are positively associated with economic growth. What we may be capturing is the fact that public _expenditures_ in these sectors do not necessarily

> lead to increases in the stock of human and physical capital, so that the connection with

> economic growth is severed. One reason could be the efficiency with which public resources are used. Expenditure on capital goods does not necessarily lead to more capital goods. **A**

> second reason could be that the standard categories of public spending **--** current and capital expenditure **-** do not capture the difference between capital-stock-enhancing and consumption

> expenditures. For example, operations and maintenance expenditures often make a capital good

> productive, but they are classified as current expenditures. Similarly, some capital investment projects (everyone has his favorite example) end up being consumption goods for powerful

> members of society, rather than productivity-increasing activities. **A** third possibility is that

> governments use current expenditure to placate politically volatile groups. The attendant political stability, in turn, leads to higher economic growth. Regardless of which of these three possibilities is the reason, the basic message arising from this paper is that the traditional view of the link between the composition of public expenditures and economic growth is not borne out **by** the historical experience of developing countries.

24
# **The Impact of COVID-19 on Electricity Generation: An Empirical Investigation**

# **1. Introduction**

Since early 2020, the COVID-19 pandemic has impacted human life in every corner of the world. During the first wave of COVID-19 (March–June 2020), the world went through unprecedented lockdowns that reduced the level of economic activity. However, the lockdowns also caused some positive environmental externalities, such as reduced local air pollution and greenhouse gas (GHG) emissions due to reduced energy consumption, particularly for transportation (Andreoni, 2021; Bauwens et al., 2020; Bao and Zhang, 2020; Liu et al., 2020; Le Quéré et al., 2020). The reductions in local air pollution and GHG were mainly seen in countries with the predominant use of fossil fuels for their energy supply.

Some studies (Sharma et al., 2020; Nakada and Urban, 2020; Menut et al., 2020; Chauhan and Singh, 2020) have sought to investigate the effect of COVID-19 and the accompanying public health measures on reducing local air pollutants in India, Brazil, China, Europe, and the United States. Most of these studies have reported a significant reduction in common pollutants due to the COVID-19 lockdowns. For example, using spaceborne NO2 column observations from January to April 2020, Bauwens et al. (2020) find that NO2 decreased by 40% in China and 20% - 38% in western Europe and the United States compared to the same period in 2019. In Hangzhou, a megacity in eastern China, aerosol particles data from January to March 2020 show that the concentrations of PM2.5-10, PM2.5, NOx, SO2, and CO fell by 58%, 47%, 83%, 11%, and 30%, respectively (Yuan et al., 2021). In contrast, Bekbulat et al. (2021) find that stay-at-home orders in the US did not significantly or permanently reduce pollution. Ozone, NO2, and CO returned to expected levels, and PM2.5 and PM10 levels were higher than expected after the lockdowns.

There are several questions regarding the impacts of COVID-19 on electricity demand and generation. For example, how much has the COVID reduced the electricity demand? Has electricity generation mix changed? Has COVID-19 increased the penetration of renewable energy sources (RES) in electricity generation, either through the change in electricity load profiles or through the partial use of COVID pandemic stimulus funds promoting renewables in various countries? IEA (2021) and McWilliams and Zachmann (2020) track electricity consumptions 2
calorie and calcium adequacy of different age-sex groups, as examples, are shown in Figure 3. The left (right) panel shows intakes of different age-sex group at different levels of wife’s (head’s) assets at marriage when head’s (wife’s) assets at marriage is zero. For calorie, most contrasting pattern is reflected for female child. Her calorie adequacy ratio tends to increase with wife’s assets but declines with husband’s assets. The rate of decline of female adolescent’s adequacy ratio is also much faster with the increase in head’s assets as opposed to his wife’s assets. While male adult’s adequacy ratio increases with the increase in head’s assets, a v-shaped pattern appears for this adequacy ratio and wife’s assets.

For calcium, adult female’s intake initially increases and adult male’s intake decreases with the increase in head’s assets. After a point, the female’s intake sharply declines while the opposite is observed for the male’s intake. Conversely, the initial rate of increase of adult female’s intake is faster with the increase in wife’s assets, and after a point the intake declines at a slower rate with the increase in wife’s assets (compared to the increase in head’s assets). Adult male’s intake appears to be negatively related with wife’s assets. The male child’s intake increases with head’s assets. This intake moderately increases with the initial increase in wife’s assets and then tends to decline.

All these contrasting relationships between individual intakes and spouses’ assets at marriage motivate a more detailed empirical analysis of intrahousehold bargaining on individual nutrient allocations in the following section.

# **6 Estimation**

## **6.1 Calorie intake and health technology**

Table 3 presents the estimates of the health technology (equation 9) for males engaged in highly energy intensive occupations (HEIO) (as defined in the preceding section) and non-HEIO and for females. OLS estimates are presented along with IFE estimates for comparison purpose. The results imply three points. First, the role of calorie intake on individual’s health outcome is much limited once unobserved individual and household fixed effects are accounted for. For each of the three categories of individuals, the effect of calorie intake on health outcome is much higher in OLS than IFE estimates, indicating upward bias in OLS estimates, presumably driven by unobserved individual endowment and household characteristics (such as spouses’ tastes). For males in HEIO, doubling calorie intake will increase their weights for heights by 5% in OLS estimate, while the corresponding increase is only 0.67% in IFE estimate. Second, this effect of calorie intake varies only marginally across individuals based on their energy intensity of 21