rafmacalaba/datause-encoder-data huggingface dataset:
surveyed 320 micro and small industries from three industrial clusters within the Kumasi metropolis in Ghana to explore the relationship between frequent and unscheduled electricity outages to micro and small-scale industries. Bose et al. (2006) use a two-stage random sampling survey of 500 manufacturing firms and 900 farmers to assess willingness to pay for reliable supply of electricity in Karnataka in India. However, these studies are different from the current study in two respects. First, they deal with unplanned electricity outages and do not necessarily deal with the planned outages or load shedding the current study is investigating. Second, these studies estimate only direct economic costs of not getting a reliable supply of electricity. Their methodology is not useful for assessing the overall economic costs of either unreliable supply or shortage of electricity supply.

Some existing literature, such as Kaseke and Hosking (2012), Oseni and Pollitt (2013) and Allcott et al (2016), assesses the impacts of planned electricity outages or load shedding. Kaseke and Hosking (2012) estimate the economic costs of load shedding in the mining sector in Zimbabwe. Oseni and Pollitt (2013) use cross-sectional data on electricity outages collected by the 2007 World Bank Enterprise Survey of 6,854 firms in 12 African countries. Allcott (2016) analyzes the impacts of weekly “power holidays” or load shedding in the manufacturing industry in India. The methodologies in these studies captures only the direct costs of load shedding and they do not include the economic costs due to interlinkages of industries and economic agents. These indirect impacts are important because, depending upon the structure of an economy, the size of indirect impacts could be higher than the size of direct impacts.

We used a computable general equilibrium (CGE) model for assessing the economic costs of load shedding. The CGE model we used in this study has a capacity to capture load shedding’s economywide impacts caused directly and indirectly. For example, load shedding causes either short-supply of electricity to an industry or increased electricity input cost of that industry. The result would be either cut- or increased price- of outputs from that industry. This would further cause increase in prices of outputs of other industries where this industry supplies an input. The chain continues, and overall output of the economy will be curtailed and prices of most goods and services of domestic industries will rise. Households not only suffer with discomfort due to irregular supply of electricity, but also face higher prices of goods and services. Moreover, domestic production would be more expensive as compared to their international counterparts and 4
20

# **Table 5 MATURITY AND PERFORMANCE**

(Data sorted by maturity)

||Below Ist<br>quartile|Ist to 2nd<br>quartile|2nd to 3rd<br>quartile|Above 3rd<br>quartile|Correlation with<br>last period<br>maturity|
|---|---|---|---|---|---|
|avcfkc|.01245<br>(.08662)|.02807<br>(.06934)|.03805<br>(.05777)|.03803<br>(.05798)|.128|
|avgout|.01093<br>(.22828)|.02822<br>(.19935)|.05865<br>(.17853)|.08745<br>(.21804)|.150|

Notes: (1) avgout, = (In q%+2<sup>- In qt,</sup> 1<sup>)/3; (ii) avcflc, = (cfk, + cfl,, + cfl,</sup> 2<sup>)/3; (iv) standard deviation in</sup> parentheses.

In order to investigate in more detail a channel that may explain the improved performance in terms of profitability and sales growth of firms with more initial long term finance, we will investigate the impact of maturity (and other related variables) on firm level TFP. More specifically, we assume the existence of a production function, where Q, K, L, and M are output, capital stock, labour and materials and **eh0** is the firm specific productivity parameter, Xi, is the vector of financial variables which are expected to influence productivity and I and t are firm and time subscripts. The production function is assumed to be Cobb-Douglas and h(.) is parameterized as a linear function. The estimating equation is:
# ( _Continued_ )

|||Non-farm se|lf-employment||
|---|---|---|---|---|
|_Access to_:|||||
|Medium or strong 3G/4G<br>|0.005<br>(0.010)|<br>|<br>|<br>|
|Medium or strong 3G/4G||0.004|||
|for one year||(0.008)|||
|Medium or strong 3G/4G|||0.004||
|for two years or more|||(0.009)||
|Medium or strong 3G/4G||||0.005|
|for three years or more||||(0.009)|
|_Control variables_|||||
|Access to electricity|0.003|0.003|0.003|0.003|
||(0.009)|(0.009)|(0.009)|(0.009)|
|Household size|0.002|0.002|0.002|0.002|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Housing ownership|-0.011|-0.011|-0.011|-0.011|
||(0.011)|(0.011)|(0.011)|(0.011)|
|Wealth index|0.003|0.003|0.003|0.003|
||(0.003)|(0.003)|(0.003)|(0.003)|
|Access to|0.004|0.004|0.005|0.005|
|medium or strong 2G, 3G, or 4G|(0.043)|(0.043)|(0.043)|(0.043)|
|# of observations|40,621|40,621|40,621|40,621|
|R-squared|0.003|0.003|0.003|0.003|
|Number of individuals|19,191|19,191|19,191|19,191|
|F statistics|3.297|3.569|3.422|3.412|

_Notes_ : Standard errors reported in parentheses are clustered by LGAs. *** p<0.01, ** p<0.05, * p<0.1.

34
20

**Figure 9**

<!-- Start of picture text --> Simulated changes in poverty<br>using growth rates by region, but substituting urban rate for Dar<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year<br>Mainland Tanzania Dar es Salaam<br>Other urban areas Rural areas<br>.45<br>.4<br>.35<br>.3<br>.25<br>Fraction below poverty line<br>.2<br><!-- End of picture text -->

# **5.6 Simulation with Regional Growth Rates and Inequality Adjustment**

The simulations presented thus far assume that growth was distribution neutral.<sup>9</sup> According to the survey data, inequality jumped up in Dar es Salaam, while remaining essentially unchanged in other urban areas, rural areas, and the country as a whole. The survey data shows that the Gini coefficient for Dar increased from 0.30 to 0.34 between 1992 and 2001.

In this simulation, the inequality adjustment proposed by Datt and Walker (2002) and in Datt et al. (2003) and described in the methodology section is applied by sector (Dar es Salaam, other urban, and rural) in a simulation, using region-specific growth rates. The overall urban growth rate is again substituted for the Dar-specific growth rate. A constant inequality adjustment is applied such that the Gini coefficient in the simulation increases from 0.30 to 0.34 for Dar es Salaam.

Results from this simulation are presented in Appendix Table A10 and Figure 10 below. While the general path of the evolution of poverty is similar, the net drop in poverty in Dar is smaller than that observed in the survey data.

> 9 More strictly, the simulation with separate urban-rural growth rates entails the assumption of distribution-neutral growth within each sector, while the simulation with separate regional growth rates used the assumption that growth within each region was distribution-neutral.
12 Table 1: Regression Results Dependent variable = p1,HK

|Independent<br>Variable|Coefficient|t Statistic|
|---|---|---|
|Constant<br>|2.9112<br>(10.0446)<br>|0.2898<br>|
|D,<br>|8.4295<br>(9.7795)<br>|0.8620|
|D2<br>|2.1302<br>(-0.3960)<br>|0.2240<br>|
|D3|-3.6851<br>(9.3067)<br>|-0.3960|
|D4<br>|5.4033<br>(6.8987)<br>|0.7S32<br>|
|D,<br>|3.5500<br>(1.8916)<br>|1.8767c|
|D6<br>|3.6596<br>(9.5343)<br>|0.3838<br>|
|D7<br>|2.5788<br>(9.1442)<br>|0.2820<br>|
|Do|3.0167<br>_(2.5875)_<br>|1.1659<br>|
|1X9<br>|5.5284<br>(1.7844)<br>|3.0983'<br>|
|pi, us<br>|0.3761<br>(0.1622)<br>|2.3185b<br>|
|Hit<br>|-0.0335<br>(0.0231)|-1.4503d|
|iT,,<br>|0.5765<br>(2.4903)<br> <br>|0.2315<br>|
|Vi,<br>|-0.4697<br>x 10-7<br>(0.1017x10-6)<br>|-0.4619<br>|
|T,|0.4655<br>(0.1174)|3.9643|

> R2 = 0.9653, Adjusted R<sup>2</sup> = 0.9554 Number of observations = 64

- Standard errors are in brackets beneath the estimates of the parr2meters. (These standard errors do not differ appreciably from those obtained with the White correction, therefore we discount the possibility of heteroscedasticity in our sample.)

- *: Significant at the 1 per cent level. b: Significant at the 5 per cent level. C: Significant at the 10 per cent level. d: Significant at the 20 per cent level.

Results of hyvothesis-testinc: t-statistic for test of Ho: 1=1 vs i31: T = -3.8455 -- reject the null hypothtais at the 1 per cent level.

F-statistic for test of Ho: 3=1, T=6=0=0: F = 4.9560 -- reject the null hypothesis at the 1 per cent level.
- 29 -

2.36 Goverrnment policies to stimulate increased cereal production through improvements in infrastructure, subsidized inputs, stable producer prices, and a host of institutional supports have been largely responsible for the rapid rates of growth in production. With budgetary pressures and low international prices of cereals, these policies will probably be rolled back to some degree, thus forcing marginal land out of cereal production. These lands, marginal for cereal cultivation, may be better suited to alternative farming systems.

# Demand Factors

2.37 Three main factors determine the level of rice consumption: tastes, incomes and the price of rice relative to the price of substitutes. These three factors are interactive, and they are difficult to separate using aggregate data on consumption. Tastes of urban people are generally distinct from those of rural people because of differences in life-style and level of physical activity. The tastes of one ethnic group within a country may differ from those of other groups. Changes in the price of rice will have an effect on consumption because rice becomes more or less expensive relative to other alternatives. If rice absorbs a large fraction of total expenditures, a change in its price has substantial effects on real incomes. Absolute income levels of consumers also influence consumption patterns.6 2.38 In most countries, place of residence, whether rural or urban, is an important factor affecting patterns of consumption. Since rural people usually have lower incomes than those of urbanites, they are more likely to eat meals at home and less likely to purchase prepared foods made from wheat flour if they are more expensive. Preparation of rice in the household tends to be a time-consuming activity. As urbanization proceeds and women enter the labor force, the opportunity cost of time in the household tends to rise, and cooking rice has to compete with the 6/ This section is drawn extensively from Chapter 11 of Randolph Barker, Robert W. Herdt, with Beth Rose, The Rice Economy of Asia (Washington, D.C.: Resources for the Future, 1985).
Table B8: Robustness check using the sample of the aged 60 and below—Main reason for having chosen the job

||Good|salary|Good|location|Go<br>co|od work<br>nditions|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Social networks|1.393|-0.351|1.940|-0.501|-2.753|2.062|
||(2.190)|(2.830)|(3.708)|(6.318)|(2.818)|(5.623)|
|Abilities||X||X||X|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.064|0.082|0.030|0.052|0.045|0.054|
|N|3761|3755|3761|3755|3761|3755|
|Control mean|20.157|20.157|12.380|12.380|31.604|31.604|
||Good pro|spect for|Releva|nt to my|Good|prestige of|
||career pr|ogression|edu|cation||frm|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Social networks|-12.834<sup>_∗∗∗_</sup>|-7.790|-0.378|-0.228|-2.467<sup>_∗∗_</sup>|-2.702|
||(3.440)|(5.175)|(0.347)|(0.225)|(1.103)|(1.776)|
|Abilities||X||X||X|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.072|0.070|0.036|0.044|0.041|0.076|
|N|3761|3755|3761|3755|3761|3755|
|Control mean|21.456|21.456|0.512|0.512|4.791|4.791|
||Recomm<br>oth|ended by<br>ers|No oth|er ofers|||
||(13)|(14)|(15)|(16)|||
|Social networks|6.510<sup>_∗∗∗_</sup>|7.642<sup>_∗∗∗_</sup>|8.588<sup>_∗∗∗_</sup>|1.870|||
||(1.413)|(2.432)|(2.647)|(2.400)|||
|Abilities||X||X|||
|Firm-occ fxed efect||X||X|||
|R-squared|0.053|0.060|0.048|0.048|||
|N|3761|3755|3761|3755|||
|Control mean|0.760|0.760|8.340|8.340|||

_Note_ . This table presents linear probability models analyzing main reasons why employees chose the jobs. The dependent variable is the binary dummy for the main reason being the one indicated in a corresponding column header. The survey asked employees which of listed reasons was the main reason. The row _Control mean_ shows the sample means of the dependent variables among the employees who did not use social networks. The row _Abilities_ indicates whether the observable and unobservable characteristics (years of schooling, age at hire, and father’s and mother’s years of schooling, math and language z-scores, and Big 5 index) are included or not. All specifications include geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Coefficients, standard errors, and control means are multiplied by 100 and should be interpreted as percentage points. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

51
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32
Other non-government payments include private savings and investment. As noted in Section 2, the rest of the world finances its investment (FDI) via the balance of payments. The household (marginal and average) savings rate is gradually increased (from 7.7 percent of household disposable income in 2012 to 14.7 percent in 2030) on the assumption that the investment climate will improve, encouraging domestic private savings and investment. By 2030, the GDP share for private investment is around 15 percent with domestic and FDI shares of 12.3 and 3.4 percent, respectively; these figures are similar to recent averages for low- and middle-income countries.

# **The Base Scenario**

The results for the base scenario are summarized in Tables 3.1-3.7. Tables 4.1-4.6 contrast the base results with those of the alternative simulations (discussed in Section 4).

The evolution of Liberia’s economy during the MTGDS period (2013-2017) is dominated by two events: (a) very high levels of mining FDI, which permit strong expansion in mining production and in government receipts from mining; and (b) the near-elimination of UNMIL transfers, which dampens import growth and domestic consumption (as UNMIL today represents large shares of both). By contrast, the post-MTGDS period (2018-2030) is less eventful with more uniform growth across sectors and macro aggregates.

These developments are reflected in simulated changes in real macro growth, poverty and other MDG indicators (Table 3.1). Changes in the GDP shares for macro indicators and other indicators in the government budget and the balance of payments can be found in Tables 3.23.4. Changes in the shares in GDP at factor cost for different production sectors and in the relative magnitudes of real sector growth rates are summarized in Tables 3.5-3.6.

As shown in Table 3.1, during the MTGDS period, growth is very rapid for GDP at factor cost, at 8.8 percent per year. The real exchange rate appreciates due to a large FDI volume but depreciates when this source of additional foreign exchange stagnates during the post-MTGDS period. (During the MTGDS, the decline in UNMIL transfers works in the opposite direction, encouraging depreciation.) Growth in GNI is slightly slower, dampened by the impact of net factor incomes from abroad. The impact of UNMIL is reflected not only in negative consumption growth for UNMIL itself but also in relative slow growth in absorption (domestic final demand), imports, and GNDI.<sup>13</sup> Government demands and, to a lesser extent, household consumption grow at a rapid pace, while private investment slows due to a decline in mining FDI during the MTGDS period. The decline in the trade deficit (evidenced by the large gap between very rapid

> 13  By definition, the following relationships hold: GNI = GDP + net factor income from abroad.  GNDI = GNI + net current transfers from abroad.  Absorption = consumption + investment = GNDI + current account deficit of the balance of payments.  For more on these relationships, see IMF (2007).

8
# **4.  The Share of Fish in Consumption of Animal-Source Foods**

All five DHS surveys ask mothers whether, during the previous 24 hours, their children have consumed any animal-source food -- eggs, poultry, fish, and other meats. In Section 3, we have used the full data set to explore the determinants of total consumption of animal-source foods. In this section, we extend the analysis to fish consumption using the source-specific information in DHS 2011 and 2014.

As Table 7 shows, understanding the determinants of fish consumption is particularly important for the poor in Bangladesh. The table summarizes DHS survey responses on children’s consumption of fish, eggs and meat during the 24 hours prior to the survey date. For each child, we code each animal-source food as 1 if it was consumed and 0 otherwise. We focus on cases with positive consumption (total coded value > 0) and compute the fish share of animal sources by dividing the fish consumption code [0,1] by total coded value. The computed ratio can be interpreted as the share of fish in animal-source foods, conditional on non-zero consumption.

Table 7 reports mean and median shares by asset quintile. The results highlight the particular importance of fish as a animal-source food for the poorest children. In household asset quintile 1, the mean and median fish shares are 66.7% and 100%, respectively.

# **Table  7:  Share of fish in children’s animal-source foods by household asset class**

|**Asset**<br>**Quintile**|**Mean**<br>**Fish Share**<br>**(%)**|**Median**<br>**Fish Share**<br>**(%)**|
|---|---|---|
|**1**|**66.7**|**100.0**|
|**2**|**57.5**|**50.0**|
|**3**|**52.6**|**50.0**|
|**4**|**41.3**|**50.0**|
|**5**|**35.0**|**33.3**|

15
Policy Research Working Paper 10391

# **Abstract**

This paper identifies high-priority areas for methane emissions reduction and estimates recent emissions changes in those areas using atmospheric concentration data from the European Space Agency’s Sentinel-5P satellite platform. The modeling approach is illustrated with three case studies: landfills in Spain (Madrid), irrigated rice production in India (Karnal district, Haryana state), and oil production in Iraq (Al Amarah district, Maysan governorate). For each case, the paper estimates two change models by fixed effects: the monthly trend in methane concentration from January 2019 to November 2022, and the difference between mean concentration in 2022 and the previous three years. The paper estimates the change models for 775 high-priority areas and finds that cases with decreasing methane emissions are outnumbered four to one by cases with increasing emissions. The paper also analyzes trends in high-priority areas for seven major methane source sectors (agricultural soils, livestock, gas, oil, coal, landfills, and wastewater) and finds only two where emissions decreases outnumber increases (gas and oil). Among World Bank income groups, decreases outnumber increases in high-income economies but increases are hugely dominant in the other three groups. The paper concludes with a presentation of summary emissions trend reports for all 775 high-priority areas, with accompanying maps and an Excel file. As satellite-based monitoring becomes more widely employed, such reports will provide a useful template for judging further progress toward fulfillment of the Global Methane Pledge.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team