rafmacalaba/datause-encoder-data huggingface dataset:
# **The link between unreliable electricity and output losses varies between countries and industries**

Aside from econometric estimation, losses can also be quantified using specifically designed surveys. In Bangladesh, about 14% of industrial sector electricity demand could not be met between 2001 and 2003, with 94% of outages being unplanned according to a survey conducted by Wijayatunga and Jayaleth (2008). In 2001, this is estimated to have translated into damages equivalent to 1.72% of Bangladesh’s GDP. A survey conducted with Pakistani industrial firms in 2008 finds that, on average, firms suffer losses of 23% of value added as a result of unserved electricity (Siddiqui _et al._ , 2008). A newer study of Pakistani manufacturing firms finds that an increase in average daily power outages by one hour causes a reduction in value added of 1.26% (Zhang, 2019). In Cameroon, about 80% of industrial firms experience output losses of between 16% and 50% due to power outages, according to one survey. Costs of outages are found to increase with outage duration and decrease if advance notice is given (Diboma and Tamo Tatietse, 2013).

In Ethiopia, power supply disruptions result in productivity losses of about 4% to 10% among manufacturing firms, according to census data. Disaggregating the impact shows that firms with very low and very high power usage intensities suffer the most productivity losses from electricity disruptions. Intuitively, firms with low electricity intensity adopt costly coping mechanisms to a lesser degree and are therefore hit harder by disruptions, whereas firms with high power intensity do invest in adaptation measures, but are also most affected by the high costs of self‐generation, thus incurring greater losses (Gurara and Tessema, 2018).

<!-- Start of picture text --> Productivity loss due to power outages by<br>energy intensity of industry in Ethiopia<br>Energy intensity<br>Low Medium High<br>0<br>‐0.1<br>‐0.2<br>‐0.3<br>‐0.4<br>Change in labor productivity<br><!-- End of picture text --> _Figure 3 Power supply disruptions in Ethiopia cause the highest productivity losses among firms with very low or very high power use intensity in production (difference in labor productivity due to power supply disruptions on the y‐axis, energy intensity on the x‐axis) (Reproduced from Gurara and Tessem, 2018)_ For manufacturing firms in Indonesia, Rentschler and Kornejew (2017) show that lacking access to reliable electricity can cause firms to switch towards polluting and less efficient fuels. This reduces their profitability and causes them to pass on higher prices down their supply chains. Stated preference valuation methods can be used to quantify the direct and indirect cost of unreliable electricity infrastructure by asking businesses for their willingness to pay for improved service. According to one survey, small Indian manufacturing firms face on average outages of between two (winter, monsoon 11
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

**Figure 1:  Output, Consumption, Exports, Imports, Investment 1960-2002**

<!-- Start of picture text --> Cote d'Ivoire Macroeconomic Indicators<br>14<br>12<br>10<br>8 Output<br>Consumption<br>Exports<br>Imports<br>6<br>Investment<br>4<br>2<br>0<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000<br>Years since independence<br>Billions<br>US $ (1995)<br><!-- End of picture text -->

**Figure 2:  Per Capita Output, Consumption, Exports, Imports, Investment 1960-2002**

<!-- Start of picture text --> Cote d"Ivoire: Per Capita Economic Indicators<br>Output, Household Consumption, Exports, Imports, and Gross Capital Formation<br>1600<br>1400<br>1200<br>1000<br>Y/N<br>C/N<br>800 X/N<br>I/N<br>K/N<br>600<br>400<br>200<br>0<br>Years 1960-2002<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Constant US $ per capita (1995)<br><!-- End of picture text --> 3
9 households with access to a source of drinking water other than surface water, in households with some sanitation facility, particularly those with an improved sanitation facility. Children born in a health facility and children living in urban areas show significantly lower postneonatal mortality rates than their counterparts.<sup>7</sup> Neonatal mortality patterns show few significant differences using bivariate comparisons. Surprisingly, neonatal mortality rates are higher if the child was born in a health facility, and this difference is significant at the 1 percent level. This could reflect a causal effect, perhaps because of higher risk of infection due to exposure of the infant to patients in the facility. It is possible, however, that this represents at least in part a selection effect: women with higher risk pregnancies for which the newborn will face greater mortality risk may be more likely to seek birth in a health facility, and it is also possible that neonatal deaths are more likely to be reported if they take place within a facility.<sup>8</sup> With available data it is not possible to distinguish between these two hypotheses.

Next we analyze the correlates of postneonatal and infant mortality in a multivariate framework using a linear probability model. The dependent variable is a measure of mortality at the individual level, which takes value 1 if the child died during the postneonatal (or infant) period and value 0 if the child survived. Explanatory variables include variables measured at district, household, mother, and individual level. Most of the variables are measured at household level: urban/rural location, province, location in malaria risk zone, ITN ownership, access to drinking water and sanitation, and socioeconomic status as measured by a wealth index. Antenatal care variables (intake of iron supplement, intake of anti-malarial drugs, tetanus vaccination) and mother‘s educational level are measured for the mother.<sup>9</sup> Whether the child was born in a health facility is measured at individual level. Because vaccination records are not available for deceased children, immunization is measured as the fraction of children with all vaccines at the

> 7 All differences described in this paragraph are statistically significant at the 5% level.

> 8 Reported neonatal mortality rates are also higher for births to women with tertiary education, although because there are few such births, the confidence interval is large and this difference is only significant at the 10 percent level.

> 9 The DHS data on antenatal care was collected for the last birth only. For a child who is not the last birth, its value was approximated by its last born sibling. In the final sample (4668 observations), there are 1046 children (22.4%) who are not the lastborns.
Table 3: MPI and GDP

|VARIABLES|(1)<br>MPI|(2)<br>MPI|(3)<br>MPI|(4)<br>MPI|(5)<br>MPI|(6)<br>MPI|
|---|---|---|---|---|---|---|
|Change in GDP (current)|-0.685***<br>(0.201)|-0.506**<br>(0.203)|||||
|Change in GDP (lagged)||-0.608**<br>(0.258)|||||
|Change in GDP (current +<br>lagged)|||-1.144***||-0.558*|-1.136***|
||||(0.225)||(0.317)|(0.224)|
|Change in GDP (following<br>Santos and|||||||
|Balasubramanian)||||-0.828***<br>(0.241)|||
|Initial level of MPI|||||0.001<br>(0.008)||
|Change in GDP (current +|||||||
|lagged) x Initial level of<br>MPI|||||0.197||
||||||(0.144)||
|Change in Gini (current)||||||-0.168<br>(0.756)|
|Constant|-0.052***|-0.041***|-0.038***|-0.047***|-0.037***|-0.039***|
||(0.006)|(0.008)|(0.008)|(0.011)|(0.014)|(0.008)|
|Observations|127|127|127|77|127|127|
|R-squared|0.068|0.106|0.134|0.089|0.170|0.135|

**Note:** Robust standard errors are presented in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

Model 1 investigates the elasticity of poverty with respect to economic growth, using the current change in GDP per capita as the independent variable. Our findings reveal a negative and significant elasticity of -0.685. This result implies that a 10% increase in GDP per capita is associated with a 6.85% reduction in the MPI.

In Model 2, we explore whether the impact of economic growth on MPI is lagged, as suggested by Santos et al. (2019) and Balasubramanian et al. (2023). To this end, we incorporate the lagged change in GDP over the five-year period preceding the first survey used to calculate the change in MPI. Our results indicate that the lagged change in GDP is also negative and significant, with a magnitude similar to the current change in GDP. Further analysis in Table 4 will demonstrate that specific subcomponents of the MPI account for the immediate impact while others are responsible for the lagged effect.

Model 3 combines the current and lagged impacts of changes in GDP on the MPI to estimate the total effect. With a coefficient of -1.144, our results suggest that a 1% increase in GDP correlates with a 1.144% decrease in the MPI. Comparatively, this effect is notably smaller than the impact of GDP on monetary poverty as identified in recent research by Wu et al. (2024). They reported a growth elasticity of monetary poverty of approximately 2.5, using the World Bank's $2.15-a-day poverty line. This discrepancy highlights that while economic growth does contribute to reductions in both monetary and multidimensional poverty, its effect is considerably more pronounced on 12
across countries is allowed, we find that trade flows between Russian regions and countries which signed a free trade agreement with Russia remain similar to intra-Russian trade in the reform period, implying that national borders alone have not diverted trade flows in the FSU.

Third, our results also suggest that slow adjustment of production and of infrastructure has limited trade reorientation. Using past trade to proxy for linkages developed when the Soviet Union was integrated, we find that past linkages significantly influence current trade. Since past trade was centrally planned it is unlikely that we are picking up other unobserved persistent variables, such as tastes or comparative advantage. As linkages with other republics continue to atrophy, the domestic trade bias will increase to well over the 60 percent we estimate currently.

Our estimate of the border effect is significantly lower than similar estimates using OECD data, despite greater trade barriers in the FSU. Using inter-provincial and province-state trade data, McCallum (1995) and Helliwell (1996) find that in 1990 Canadian provinces traded about twenty times more among themselves than with U.S. states. More recently, Helliwell (1998) estimates the bias to be twelve in 1996 following the preferential trading agreement (PTA) between the two countries.<sup>1</sup> Wei (1996) and Helliwell (1998) estimate that the domestic trade bias among OECD countries ranges from three to twelve.<sup>2</sup> One explanation that is consistent with both sets of results is that past linkages have led to a continued domestic orientation in trade in the OECD, despite falling trade barriers, and sustained integration in the FSU in spite of rising trade barriers.

**'An** alternative way to evaluate the border effect is with price data. Engel and Rogers (1996) examine the relative price variability of similar goods among cities in Canada and the United States. They find that relative price variability between cities is significantly greater when a border is crossed.

2Wei (1996) develops a technique to estimate the domestic bias in trade using input-output tables to estimate internal trade for countries without regional data. He assumes that internal trade distances are one-quarter the distance between a country and its nearest trade partner, to estimate the border effect. One problem with this method is that the border estimate is proportional to the assumed internal trade distance.

3
- The slow relocation of rural labor has hampered productivity growth in agriculture. Productivity is by far the most important determinant of income per worker. With overall agricultural output growth modest—and not, at the margin, depending significantly on the number of workers—the slow shift of labor has meant that per capita rural income growth has been low, leading to the widening rural-urban inequality. Indeed, the development of the productivity differentials can explain the development of the urban-rural income gap rather well (Figure 5).

Figure 5. Productivity gap between agriculture and other sectors and the urban-rural income gap <!-- Start of picture text --> 7 3.5<br>Urban-rural income gap (ratio) (RHS)<br>6<br>3.0<br>6<br>2.5<br>5<br>2.0<br>5<br>4 1.5<br>4 Productivity gap agriculture-(industry and<br>1.0<br>3 services) (ratio) (LHS)<br>0.5<br>3<br>2 0.0<br>1978 1982 1986 1990 1994 1998 2002<br>Source: NBS, and World Bank staff estimates.<br><!-- End of picture text --> To further illustrate these findings, we decompose overall labor productivity growth into components related to a shift of labor between sectors and components related to “organic” productivity growth in each sector. Overall labor productivity Y/L is just (Y1+Y2+Y3)/(L1+L2+L3), where Yi, Li are production and labor in sector i respectively. This definition could be rewritten as where y is overall labor productivity, and yi is labor productivity in sector i, and εi is the share of sector i in total production. The derivative of the above equation is: which means that the growth of overall labor productivity can be written as The first, third, and fifth terms combined capture the shift of labor between sectors, and the second, fourth, and sixth denote organic productivity growth in the three sectors. The decomposition results are shown in Table 2, and they confirm that the shift of labor from agriculture to other sectors has not been an important factor behind GDP growth in China, and “organic” labor productivity growth in industry is by far the most important factor.

7
<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br><!-- End of picture text --> <!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br><!-- End of picture text --> <!-- Start of picture text --> 0% — [|<br>- TE all i<br>0-25% 26-50% 51-75% 76-100%<br>TMGeneration Distribution<br><!-- End of picture text -->
# **Agricultural Production, Dietary Diversity, and Climate Variability**

Andrew Dillon Michigan State University Kevin McGee American University Gbemisola Oseni World Bank Keywords: Agriculture, Crop Diversity, Dietary Diversity, Nutrition, Rainfall, Temperature JEL Codes: Q12, O13 __________________________________ Acknowledgements: We appreciate the helpful comments of Gero Carletto, Alan de Brauw, John Maluccio, Paul Winters, an anonymous referee, and the participants of the Farm Household Production and Nutrition conference at the World Bank. The management of the Nigeria LSMSISA data collection process in Nigeria benefited from the collaboration of Gero Carletto, Kinnon Scott, the Nigerian Bureau of Statistics staff, and Colin Williams. Siobhan Murray provided invaluable support to organize the GIS and climate data. All errors are our own.
- 33 -

Table 9 Estimated Effect of Training on Employment--Females II Experienced Workers vs. New Entrants

|:_______:___<br>_:__<br>NWokExp-|WithAorkEkp..|
|---|---|
|WEIGHTED PROBITS||
|3 months<br>-.127**|.149**|
|6 months<br>-.083|.209**|
|12 months<br>-.103**|.198**|

Table 10 Monthly Salary, Hours Worked, and Hourly Wage for Trainees and Controls

|.<br>.<br>.......<br>.v'<br>A-rib<br>,<br>a<br>...|n..|Controls|
|---|---|---|
|A. Male Samples (number of observations)|1212|1051|
|Monthy salary (1,000 pesos)<br>Weekly hours worked<br>Hourly wage (1,000 pesos)|681.59<br>45.81<br>3.984|637.67<br>43.59<br>4.016|
|B. Female Samples (number of observations)|<br>681|300|
|Monthy salary (1,000 pesos)<br>Weekly hours worked<br>Hourly wage (1,000 pesos)|531.85<br>42.77<br>3.476|571.52<br>39.51<br>4.198|
skills, industry affiliation and characteristics of workers from 64 different household surveys. Details of the household surveys, years of data and number of observations are found in Table 1. For each country we have between two (Argentina, Chile, Nicaragua) and seven (Dominican Republic) years of data, ranging from 2000 to 2006, for a total of around 60,000 (Nicaragua) to 1,150,000 (Brazil) observations per country. Adding across countries and years, we have over five million observations.

Table 2 displays descriptive statistics on education and skill levels of the workers. The first two columns show sharp differences in the average number of years of education and in the share of skilled workers (defined as individuals who hold a high school diploma) across countries. Average years of education are comparatively high in Argentina (10.63), Uruguay (9.82), Chile (8.89), Panama (8.81), Colombia (8.53), and Ecuador, the Dominican Republic and Mexico (around 7.9). These countries also show the highest share of skilled workers, ranging from 27 percent in Mexico to 52 percent in Argentina (in Colombia, instead, the share is relatively lower). The lowest years of education are observed in Nicaragua, Guatemala and Honduras (5.31, 5.70, and 5.99) but the lowest share of skilled workers are observed in Nicaragua and Brazil (9 and 13 percent). In the cases of Argentina and Uruguay, the relatively high averages are partly explained by survey design because the surveys cover only urban households. In the other fourteen countries the surveys are representative of the rural as well as urban populations.

Columns 3 and 4 compare male and female workers. For some countries the share of skilled workers is higher among females than among males, most noticeably in Argentina, Brazil, Dominican Republic, Uruguay and Panama. This difference ranges between 4 and 7 percentage points. In contrast, in Colombia, El Salvador, Mexico, Peru and Guatemala the share of skilled workers is between 2 and 6 percentage points higher among males than females.

It is also informative to look at skilled workers at a finer level of disaggregation, as workers of different educational levels are grouped together in the skilled category. Column 5 presents the share of highly-skilled workers conditional on being skilled, that is, the share of workers with more than a high school diploma (individuals with tertiary education, some 4
Table 1 Cross-Correlations between Dependent and Explanatory Variables (medium-term dataset, 5-9 years, the late 1990s - 2000s)

||()<br>pcY|(2)<br>pcYp|(3)<br>Gini|(4)<br>HCR|(5)<br>G/GDP|(6)<br>Inflation|(7)<br>In_T/GDP A|(8)<br>_T/GDP|(9)<br>InICRGC A|(10)<br>_ICRGC|e85)<br>EAID/GDP|(12)<br>SAID/GDP AA|(13)<br>ID/GDP|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|(1) PeriodAverage Growth Rate ofPer Capita Income|1|||||||||||||
|(2) PeriodAverage Growth Rate ofPer Capita Income<br>ofthe Poorest Quintile|0.5676***|1||||||||||||
|(3) PeriodAverage Rate ofChange ofGini Index|-0.0110 -|0.7007***|1|||||||||||
|(4) PeriodAverage Rate ofChange<br>ofthe Poverty Head Count Ratio|-0.3970*** -|0.4415***|0.2209***|1||||||||||
|(5) Initial GovernmentConsumption/GDP|0.0555|0.0907|0.0114|-0.0163|1|||||||||
|(6) Ln(1 + PeriodAverage Inflation/CPI)|-0.0787|-0.0911|0.0504|=0.1713**|-0.0046|1||||||||
|(7) InitialTrade/GDP|0.1985** —|0.2058**|-0.0346|-0.2033**|0.1960**|-0.0016|1|||||||
|(8) PeriodAverage Growth Rate ofTrade/GDP|0.1293|0.0569|0.0834|0.2012**|0.0118|0.1019|-0.2592***|1||||||
|(9) Initial Level ofInstitutional Quality (ICRG Capacity)|0.0366|0.0301|0.0629|0.0606|0.4240***|-0.0660|0.3386***|-0.0069|1|||||
|(10) PeriodAverage Rate ofChange of<br>|0.1602*|-0.0163|0.0754|-0.0366|-0.1063|-0.0139|-0.3083***|0.1042|-0.5575***|1||||
|InstitutionalQuality(ICRGCapacity)||||||||||||||
|<br>(11)PeriodAverageEconomicAid/GDP|0.0166|0.0571|-0.1178|0.1090|-0.0977|0.1241|-0.0618|0.1661*|-0.2443***|0.1378|1|||
||(0.20)|(0.67)|(1.39)|(1.25)|(1.13)|(1.42)|(0.69)|<br>(1.83)|(2.63)|(1.45)|noone|||
|(12) PeriodAverage SocialAid/GDP|-0.1358|0.0355|-0.1979**|0.0627|0.0125|0.0097|-0.0248|0.1035|-0.1946**|0.0204|0.6071***|1||
||(1.61)|(0.42)|(2.37)|(0.71)|(0.14)|(0.11)|(0.28)|(1.13)|(2.07)|(0.21)|(8.98)|o----||
|(13) PeriodAverage AggregateAid/GDP|-0.1482*|0.0302|-0.2034**|0.1051|-0.0148|0.0631|-0.0422|0.1084|-0.2625***|0.0711|0.8102***|0.8968***|1|
||(1.76)|(0.35)|(2.44)|(1.20)|(0.17)|(0.72)|(0.47)|(1.18)|(2.84)|(0.74)|(16.24)|(23.81)|woo--|

Note: *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. Absolute value of t-statistics calculated with White-corrected standard errors is in parentheses (for aid data variables only). Source: Authors' compilation
# **The Effects of Volatility, Fiscal Policy Cyclicality, and Financial Development on Growth: Evidence for the Eastern Caribbean**<sup>*****</sup>

Markus Brüeckner University of Queensland and Francisco Carneiro The World Bank

**Keywords** : Volatility; fiscal policy; procyclicality; financial development; terms of trade; growth

**JEL Classification** : E62, F1; G2; G28; O1; O11

> * The authors are grateful for comments and suggestions received from Alfonso Garcia Mora, David Rosenblatt, and Daniel Lederman. The usual disclaimers apply.
**Figure 4a. Percent of unbuilt cells depending on roads in '76 and '86**

<!-- Start of picture text --> Figure 4b. Percent of fully built cells depending on roads<br>in '76 and '86<br>25.0%<br>20.0%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br>1975 2014 2053 2092<br>r76=0 r86=0 r76=1 r86=0 r76=0 r86=1 r76=1 r86=1<br><!-- End of picture text --> Notes: Figure 4a shows the percent of unbuilt cells in various years, conditional on whether the cell had a road in 1976 (r76=1) and a road in 1986 (r86=1). Some cells with a road in 1976 no longer have one in 1986 (e.g., because the road was not maintained or was moved). Figure 4b shows the percent of fully built cells in various years, also depending on whether the cell had a road in 1976 (r76=1) and a road in 1986 (r86=1). Values for 1975 and 2014 are take from the data. Values for 2053 and 2092 are forecasts obtained in the same manner as for Figures 3a and 3b, except that we now use four different transition matrices and four 2014 buildup vectors from which to iterate.
those that report lack of money as the main barrier to account use, government policies to promote inclusion can increase the likelihood that individuals perceive financial services as being within their reach.

The rest of the paper is organized as follows. Section 2 introduces the survey data and summarizes our main variables of interest. Section 3 details the empirical approach we use to test the underpinnings of the use of accounts and the factors correlated with perceived barriers to account use. Section 4 presents the empirical results. Section 5 concludes.

# **2. Measuring Financial Inclusion**

# **_2.1 Survey Methodology_**

The data were collected by adding a new module on financial inclusion to the 2011 Gallup World Poll (GWP) survey, which has been conducted annually since 2005. Our module included questions on ownership of an individual or joint account, the use of the account for saving, and the frequency with which the account is used. Additional questions asked the unbanked for reasons why they do not use an account. In our analysis, we focus on 123 countries and over 124,000 individuals. We drop data for 25 countries because of missing demographic information, such as education and income. Table 1 lists all the countries included in our sample.

The survey was conducted in the major languages of each country.<sup>6</sup> The 2011 GWP surveyed at least 1,000 individuals per country in 148 economies—representing approximately 97 percent of the world’s population—using randomly selected, nationally representative samples.<sup>7</sup> The target population was the entire civilian, noninstitutionalized population aged 15

> 6 Detailed country-level information about the data collection dates, sample sizes, excluded populations, and margins of error can be found at http://go.worldbank.org/IGRTPHK660. 7 In some economies, oversamples are collected in major cities or areas of special interest. Additionally, in some large economies, such as China and Russia, sample sizes of at least 4,000 are collected.

6