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Electricity shortages and manufacturing productivity in Pakistan
Energy Policy 132 (2019) 1000–1008
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Electricity shortages and manufacturing productivity in Pakistan ∗
T
Corbett A. Grainger , Fan Zhang University of Wisconsin-Madison, World Bank, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Electricity shortages Pakistan Manufacturing productivity Climate changeJEL Classification: D04 D24 L11 L94 O12 O13 Q48
Electricity shortages present a significant challenge to manufacturers who require a reliable power source as an input to production. In Pakistan, power shortages are commonplace, but empirical evidence on the impact of shortages is still lacking. Using a survey of 4500 manufacturing firms for the year 2010–2011, we exploit regional differences in outages to estimate the impact of electricity shortages on firm revenues, value-added and the labor share of output. We find that an additional average daily hour of unexpected shortages decreases annual revenues by nearly 10%. Similarly, an increase in shortages by 1 h per day decreases annual value-added at the firm level by roughly 20%, and increases the labor share of output. We find that the impact for a similar amount of load-shedding is significantly smaller, likely due to predictability and firm adaptation. Our results suggest that a more reliable electricity supply would significantly improve manufacturing productivity in the region.
1. Introduction In many developing countries, a significant barrier to economic growth is unreliable supply of electricity. Electricity shortages in South Asia are especially widespread, with the average firm experiencing nearly an outage per day, lasting roughly 5.3 h each1. Within South Asia, Pakistan had the most severe power shortages, with the average firm reporting 2.5 outages each day for an average total daily duration of 13.2 h. More than 75 percent of firms in Pakistan identified a lack of reliable electricity as a major constraint to their operation and growth (Fig. 1). Outages occur because of technical failures. In South Asia, they also reflect the efforts of utilities to cope with persistent power shortages through scheduled blackouts, known as load shedding. (see Table 8) Because electricity is an essential input for most business processes, an unreliable supply can hut firm's productivity. Several recent studies have quantified the impacts of electricity shortages on firm-level outcomes. Allcott et al., (2016) estimate the productivity impacts of large manufacturing firms in India. Grainger and Zhang (2017) follow a similar identification strategy but focus on micro-, small, and medium Indian enterprises, and they find much larger impacts on unit costs. Fisher-Vanden et al. (2015) examine the productivity and environmental effects of electricity shortages in China. Foster and Steinbuks (2009), Alby et al. (2011) and Andersen and Dalgaard (2013) examine
the impact of outages on firm size and technological adoption. Our paper contributes to the literature in two ways. First, despite the wide recognition that electricity shortages result in substantial losses for the economy in Pakistan, empirical estimates of the impact of power shortages in Pakistan are still lacking. Second, to the best of our knowledge, this is the first paper that differentiates the impact of expected and unexpected power outages on firms. To study the impact of power shortages on manufacturing firms in Pakistan, we match firmlevel data from Census of Manufacturing Industry conducted by Pakistan Bureau of Statistics with district-level power outage data (both expected and unexpected) reported by distribution utilities. Using a sample of 4500 manufacturing firms for 2011, we estimate the impact of electricity shortages on manufacturing productivity. Productivity is measured by both revenue productivity and labor productivity. Revenue productivity is defined as firm-level total revenue and valueadded per unit of input costs (including labor, raw materials, and energy input costs). Labor productivity is defined as labor input per unit of outputs. Our estimates suggest that an additional hour per day of expected outages (scheduled blackouts) and unexpected outages decreases revenues by nearly 1.3% and 10%, respectively. Similarly, an increase in unexpected outages by 1 h per day decreases value-added at the firm level by roughly 20% and increases the labor share of output. The impact for a similar amount of expected outages is significantly smaller, likely due to predictability and firm adaptation.
∗
Corresponding author. E-mail address: [email protected] (C.A. Grainger). 1 World Bank Enterprise Survey 2011–2015. https://doi.org/10.1016/j.enpol.2019.05.040 Received 3 December 2018; Received in revised form 18 May 2019; Accepted 20 May 2019 Available online 03 July 2019 0301-4215/ © 2019 Published by Elsevier Ltd.
Energy Policy 132 (2019) 1000–1008
C.A. Grainger and F. Zhang
Fig. 1. Power outages and their impact. Source: World Bank Enterprise Survey for Pakistan (2013).
sector. Distributors are often not fully compensated through the combination of tariffs and subsidies, so they in turn cannot fully compensate generators, who cannot pay fuel suppliers, which then cut off their fuel supply, leading to idled generation capacity and electricity shortages. In Pakistan, accumulated arrears of distribution companies to suppliers, commonly known as circular debt, reached about PRs 414 billion by March 2017 (Business Recorder, 2017). Circular debt has caused up to 5 GW (GW) of capacity to lie idle, accounting for almost 22 percent of the total installed capacity in Pakistan (World Bank, 2015). Furthermore, when capacity is used, it is substantially inefficient. For example, the average efficiency of gas power plants in Pakistan is 30 percent, compared to the average efficiency of 43 percent for gas power plants in the United States2. Aging infrastructure and overloaded transmission and distribution systems also contributed to high losses in the network. In 2016, average transmission and distribution losses have reached to about 20 percent of all the electricity generated. Another cause for Pakistan's power shortages is upstream gas shortfalls. Underpricing of domestic gas has contributed to widening gap between domestic supply and demand for gas. Meanwhile, there are huge losses in gas transmission and distribution, which reached 12 percent of total supply in 2016. In addition, domestic gas supply is routinely diverted from power to other sectors such as fertilizer and transport even though the economic benefit of using gas in power generation is expected to be among the highest in all sectors in Pakistan in the medium term (USAID, 2011).
As we discuss in this paper, there are several reasons our estimates are likely lower bounds of the costs of shortages in Pakistan. However, these caveats aside, our estimates suggest that addressing the electricity would have a large, positive impact on productivity and growth. Providing a reliable flow of electricity would undoubtedly lead to substantial improvements in productivity for existing firms, but it would also likely lead to additional investment in manufacturing and other sectors. There are several institutional and economic causes of the shortages experienced in Pakistan, and a comprehensive sectoral reform would likely be necessary to solve the electricity shortage problems facing Pakistan. This includes addressing inefficiencies in the distribution and generation network, addressing pricing problems on inputs and outputs in electricity markets, and investing in additional capacity in hydro- and renewable energy. In what follows, we provide an institutional and economic background on Pakistan, focusing on electricity shortages and their causes. We then describe the firm-level data and power shortage data in Section 3, describe the empirical model in Section 4, and provide the corresponding cross-sectional estimates in Section 5. Section 6 offers a discussion and concluding thoughts. 2. Background 2.1. Causes of power shortages Pakistan faces massive electricity shortages. Since 2006, nationwide power shortages, the difference between projected demand and actual supply, have been steadily increasing each year and has risen to 26 percent of total demand, or 29 TWh in 2013 (the latest year such data is available) (Fig. 2). To address supply shortfalls, utilities implemented systematic rolling blackouts that can last 6–14 h each day. In some areas load shedding of 18–20 h occurred on a regular basis. Multiple price and institutional distortions have contributed to the current power crisis in Pakistan (Zhang, 2019). On the pricing side, historically electricity prices have been set much lower than the cost of supply. The gap is largely financed by direct budget support. Recent electricity tariff reforms have substantially lowered subsidy spending. However, they still amounted to $2.15 billion (0.8 percent of GDP) in fiscal year 2015. Underpricing of electricity undermines the incentives of power utilities to provide high-quality services, creating a so-called “subsidy trap” (McRae, 2014). Institutional problems are present at different levels throughout the
2.2. Impact of power shortages on firms Electricity shortages could affect firm productivity in several ways. First, electricity shortages may force firms to invest in expensive dieselbased captive generators, thereby diverting capital from more productive uses. Second, when firms lack an alternative source of electricity, they must shut down operation, causing wastes of non-flexible and semi-flexible inputs, such as labor and certain material inputs that can be spoiled during a power outage (Allcott et al. 2016). Third, facing persistent power shortages, firms may choose to purchase rather than make electricity-intensive intermediate inputs. Because outsourcing is more costly, electricity shortage would significantly increase 2 United States Energy Information Administration. https://www.eia.gov/ electricity/annual/html/epa_08_01.html.
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Fig. 2. Power shortages in Pakistan (%) (2006–2013). Source: Electricity Demand Forecast by Planning Power of National Transmission and Dispatch Company (NTDC) (2014).
production costs (Fisher-Vanden et al., 2015). Finally, electricity scarcity may induce firms to substitute away from electricity-intensive technology altogether. Because production processes that are less electricity-intensive also tend to be less technologically advanced, switching to such processes undermines long-term productivity growth for firms (Abeberese, 2017). The impact power shortages have on firm productivity is industry specific: more electricity-intensive industries are likely to be more adversely affected. The size of the impact would also depend on the type of power outages. The effect of unanticipated outages is likely to be much larger than scheduled outages. Firms can attenuate the impact of preannounced load-shedding by adjusting the scheduling of labor and storing away perishable material inputs, therefore significantly reduce the losses of semi-flexible inputs. In contrast, firms, especially those without captive generators, are much less able to cope with unanticipated outages.
Table 1 Distribution of firms in census by two-digit PSIC. Source: Authors' calculations using 2010-11 Census of Manufacturing Industries. Two-Digit PSIC
Activity Description
Number Firms
10 11 12 13 14 15 16
Manufacture of food products Manufacture of beverages Manufacture of tobacco products Manufacture of textiles Manufacture of wearing apparel Manufacture of leather and related products Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials Manufacture of paper and paper products Printing and reproduction of recorded media Manufacture of coke and refined petroleum products Manufacture of chemicals and chemical products Manufacture of basic pharmaceutical products and pharmaceutical preparations Manufacture of rubber and plastics products Manufacture of other non-metallic mineral products Manufacture of basic metals Manufacture of fabricated metal products, except machinery and equipment Manufacture of computer, electronic and optical products Manufacture of electrical equipment Manufacture of machinery and equipment n.e.c. Manufacture of motor vehicles, trailers and semitrailers Manufacture of other transport equipment Manufacture of furniture Other manufacturing
669 21 3 1235 183 77 11
17 18 19 20 21
3. Data
22 23 24 25
Data for this study come from two sources: the 2010-11 Census of Manufacturing Industries conducted by Pakistan Bureau of Statistics. The second data source is the distribution company performance evaluation report by National Electric Power Regulatory Authority (NEPRA). We describe these in turn.
26 27 28 29
3.1. Census of Manufacturing Industries The Census of Manufacturing Industries provides a thorough annual overview of firm-level activities, including detailed information on a variety of input costs, including labor, capital and electricity, as well as revenue data. The 2010–2011 Census mainly covers firms in Punjab province3. Punjab in Pakistan has the largest economy of all provinces and the majority of the manufacturing activity is concentrated in this area. The Census covers 4499 firms in 23 sectors at the 2-digit level of Pakistan Standard Industrial Classification (PSIC)4. The distribution of firms by the 2-digit classification are shown in Table 1. Of the 23
30 31 32
177 53 10 154 96 150 135 165 195 15 231 187 112 89 39 480
divisions, number 13 (Manufacturing of Textiles) covers roughly 28 percent of our sample, division 10 (Manufacturing of Food Products) accounts for 15 percent, and division 32 (Other Manufacturing) is the third-largest with 11 percent of the sample. The 23 sectors can be further broken down into 236 subcategories at the 5-digit level of PSIC. Firm-level characteristics are described in Table 2. The mean total product value at the firm level is about 523,000 Rupees5, whereas the mean value-added is 114,000 Rupees. Average total employment costs are just over 17,000 Rupees, whereas the average cost of raw materials
There are four firms in the dataset located outside Punjab province, including one in Karachi and three in Islamabad. Excluding these firms from the sample does not change the estimation results. 4 A description of the PSIC and the relationship to ISIC is available at the following http://www.pbs.gov.pk/sites/default/files/other/PSIC_2010.pdf. 3
5
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The Rupee-USD exchange rate was roughly 85:1 in June 2011.
Energy Policy 132 (2019) 1000–1008
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Table 2 Descriptive statistics of manufacturing firms. Variable
Obs.
Mean (thousands)
Std Dev (thousands)
Minimum
Maximum (thousands)
Product Value Value Added Total Employ. Cost Raw Materials Cost Value of Fixed Assets Electricity Costs
3585 4444 4488 3841 3975 4443
522.82 113.69 17.57 316.02 148.79 12.07
2944.99 1021.25 98.60 2146.54 1137.34 72.98
180 −4,429,910 36 25 24 2
125,000.00 32,400.00 3430.71 114,000.00 34,800.00 1880.29
Notes: Authors' calculations from the 2010-11 Census of Manufacturing Industries.
is over 300,000 Rupees. Firms have an average value of fixed assets of 150,000 Rupees, which is notably half the value of the raw material costs for the average firm and less than a third of the value of the product output. The median number of employees at a firm in the census is 22 people, with an average of 103 and a standard deviation of 569.
of unexpected outages on firm productivity because they may have different impacts on manufacturing firms. The frequency of disruptions may have a smaller impact if load-shedding is anticipated, but we expect that longer durations will have a measurable impact regardless of regularity. Finally, our preferred specifications will separately identify the effect of shortages and outages due to load-shedding.
3.2. Shortage data
4. Empirical approach
Power shortage data are reported annually by distribution companies (DISCO) to NEPRA and are published in the DISCO Performance Evaluation Report. The latest evaluation report includes shortage data at the DISCO level from year 2010–2011 to year 2014–2015. There are 11 distribution companies in Pakistan, among which 6 provide service in Punjab province. We identify service areas by district for each DISCO and match firm-level data with shortage data based on district-level identifiers in the census data for year 2010–2011. The providers (and corresponding number of firms) in our data are IESCO (120), GEPCO (1,517), FESCO (1,281), LESCO (1,159), and MEPCO (372). Although power shortages are nationwide and systematic, there is large variation in shortages at the DISCO level, which is partially linked to differences in supply and demand for electricity and partially linked to differences in operating performance of DISCOs. In fiscal year 2014, for example, average transmission and distribution losses ranges from 9.4 percent to 38.2 percent, while collection rate ranges from 33 percent to over 100 percent across the 11 distribution companies. Shortages are characterized by two reliability indices and average daily load-shedding hours reported by distribution companies. One reliability index represents the frequency of shortages, and the second represents the duration of shortages. The System Average Interruption Frequency Index (SAIFI) is the average number of interruptions that a customer experiences in a year. Specifically, SPAIFI is calculated as the total annual number of consumer supply interruptions divided by the total number of consumers that the distribution company serves in any a given year. The second measure, the System Average Interruption Duration Index (SAIDI), captures the outage duration (in minutes) that an average customer experiences in a year. Both SAIFI and SAIDI do not encompass outages caused by load shedding and therefore indicate the severity of unexpected outages. There is still large variation in the value of SAIFI and SAIDI across regions. Among the distribution companies in our data, the SAIFI measure ranges from 0.41 (IESCO) to 185.5 (MEPCO) (Fig. 3), while the SAIDI measure ranges from a minimum value of 22.6 min for IESCO to a maximum of 15,896 min (roughly 11 days) for PESCO (Fig. 4). Among the distribution companies in our data, load shedding varies from 1 h per day to 9.8 h per day, with the average firm facing 7.3 h per day. In our econometric analysis, we exploit regional variation in the reliability of power supply measured by both SAIDI and SAIFI, and total load-shedding hours to identify the relationship between shortages and firm productivity. For the year 2010–11, these two reliability indexes have a correlation coefficient of −0.13 across the 11 distribution companies covered in the NEPRA report, but for the six distribution companies in Punjab Province, the correlation coefficient for the SAIDI and SAIFI is about 0.54. We incorporate both measures of reliability to estimate the impact
Given the data described above, we are interested in the impact of electricity shortages on firm-level productivity measured by valueadded and revenues, holding constant costs of inputs and firm-specific characteristics, as well as labor-output ratio. As our data are crosssectional, the variation in outages is between distribution companies servicing the manufacturing firms. A regression of the following form is estimated as the baseline specification:
Yij = α + β1 ∗ ln(UOi ) + β2 ∗ ln(SOi ) + γ ∗ Xi + σj + εij
(1)
where Yij is the outcome of interest (i.e. the natural log of revenues, the natural log of value-added, or the labor-output ratio) for firm i in sector j. UOi and SOi are the two types of shortages – unexpected outages and scheduled outages (load shedding) – reported by the distribution company by which firm i is serviced. Xi are input costs and other firmlevel characteristics, including firms' labor costs, raw material costs, and total energy costs. Because the effect of shortages is sector-specific, we control for sector fixed effect (σj) to capture unobserved common characteristics to all firms in the sector6, and εij is the idiosyncratic error term. β1 and β2 are the two main coefficients to be estimated. As described in Section II, electricity shortages would increase production costs and reduce marginal product of labor, we expect β1 and β2 to be negative when the dependent variables are revenues and value-added, and negative when the dependent variable is labor-output ratio. In addition, we expect β1 to be larger than β2 in absolute value. In addition, we estimate the following equation to disentangle the mechanism through which unanticipated outages affect firm productivity.
Yij = α + β ∗ ln(Shortagei ) + γ1 ∗ ln(Fi ) + γ2 ∗ ln(Di ) + γ ∗ Xi + σij + εij (2) Where Shortagei is the total duration of shortages (both load shedding and unexpected outages) reported by the distribution company by which firm i in sector j is serviced. Fi and Di are the two reliability indexes that measure the frequency and duration of anticipated outages7. All the other parameters follow the same denotation as 6 There could be heterogeneous effects of shortages at the sector level. For example, energy intensive sectors may be more vulnerable to power supply disruptions. However, estimations of the heterogeneity effects at the sector level are noisy because of a small sample size. These results are not reported in the paper. 7 We also tested for inclusion of quadratic terms for the outage variables in a log-linear specification; inclusion of these terms did not change the qualitative findings and higher-order terms were typically insignificant, so the results are
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Fig. 3. System average interruption frequency index. Notes: SAIFI is the average number of interruptions that a customer experiences in a year. Specifically, SPAIFI is calculated as the total annual number of consumer supply interruptions divided by the total number of consumers that the distribution company serves in any a given year. For the purposes of illustration, the index has been transformed using the inverse hyperbolic sine. Source: DISCO Performance Evaluation Report 2014–2015 by NEPRA.
measurement of shortages, because we only capture district-level average outages, but not actual outage experienced by each firm. In the following, we present our estimation results while recognizing the above identification challenges.
introduced in equation (1). As discussed in Section II, the main productivity loss for plants without generators is wastes of non-flexible and semi-flexible inputs. We expect the negative effect of duration of unanticipated outages on productivity to be large, as longer outages imply more wasted inputs that cannot be reallocated; the relative magnitude of the impact of outage frequency (holding constant total outage hours) is less clear. Due to data limitations, there are challenges to causal identification of the impact of shortages on firm-level revenues or value-added. First, there is likely unobserved firm-level heterogeneity, which can affect both firm performance and shortages experienced by the firm. For example, firms experience higher level of shortages are more likely to acquire self-generators8. Second, shortage is likely to be endogenous to growth and business climate at the district level. For example, areas that provide more friendly business environment and experienced faster economic growth can have higher demand for electricity. This will in turn result in worse power shortages (Allcott et al., 2016; Grainger and Zhang, 2017). Finally, there is likely measurement error in our
5. Empirical results Following the approach outlined in Section 4, we first estimate the impact of electricity shortages on three firm-level outcomes: product revenues, value-added, and the labor share of output. 5.1. Product revenues Table 3 presents the results in which the dependent variable is the natural log of product revenues at the firm level. The control variables for firms’ labor costs, raw material costs and total electricity costs all have positive, significant impacts on product revenues, as expected9. Furthermore, the regressions explain a significant amount of variation in product revenues when we include sector-specific fixed effects. The first two columns do not include controls for other inputs, and the first column does not include sector-specific intercepts. When the relevant controls are included, we find that a 10 percent increase in the
(footnote continued) not shown to conserve space. 8 We are unable to account for self-generation in the data. Though we have information on other fuel use, the effects (which are not shown) are virtually the same for firms who report purchasing other fuels, suggesting this measure of self-generation is far from perfect.
9 In most cases the value of fixed capital assets is not directly measured, but instead imputed, so we do not include that variable in our main specifications.
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Fig. 4. System average interruption duration index. Notes: SAIDI is the outage duration (in minutes) that an average customer experiences in a year. Source: DISCO Performance Evaluation Report 2014–2015 by NEPRA.
Table 3 Impact of shortage on total revenues (natural log).
ln (Total Outages)
(1)
(2)
(3)
(4)
0.167*** (0.0188)
0.0410** (0.0183)
−0.0153*** (0.00373)
(5)
(6)
−0.00343*** (0.00103) 0.316* (0.166)
−0.0232*** (0.00595) 0.0140 (0.00987)
ln (Duration Index) ln (Frequency Index)
0.118*** (0.00719) 0.765*** (0.00569) 0.113*** (0.00574)
0.119*** (0.00721) 0.765*** (0.00569) 0.113*** (0.00574)
0.118*** (0.00721) 0.763*** (0.00571) 0.113*** (0.00575)
−0.0116*** (0.00218) −3.621*** (0.937) 0.000260*** (6.10e-05) 0.119*** (0.00720) 0.763*** (0.00570) 0.113*** (0.00574)
X 3538 0.959
X 3538 0.959
X 3538 0.959
X 3538 0.959
Duration Index Frequency Index Duration*Frequency ln (Total Labor Cost) ln (Raw Mat. Costs) ln (Total En. Costs) Constant Sector FE Observations R-squared
7.107*** (0.419) 3553 0.025
X 3553 0.001
Notes: The dependent variable is ln(revenues). Total Outages represents the total duration (in minutes) of shortages experienced (both load shedding and unexpected outages) by firms for that distribution company. The Duration and Frequency Indices represent the average duration or number of shortages, respectively, experienced by firms for that distribution company. Heteroskedastic-robust standard errors are shown in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. 1005
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Table 4 Impact of Unexpected outages and Load Shedding on Total Revenues (Natural Log). (1)
Table 6 Impact of unexpected outages and load shedding on value added (natural log). (1)
(2) Hours/Day Outage (Hours)
−0.0934*** (0.0233) −0.0126*** (0.00389)
Hours/Day Outage (Hours) Hours/Day Load Shedding (Hrs)
Hours/Day Load Shedding (Hrs)
ln (Daily Load Shedding) ln (Labor Costs)
0.118*** (0.00719) 0.764*** (0.00569) 0.113*** (0.00574) 3538 0.959 23
ln (Raw Materials) ln (Total Energy Costs) Observations R-squared Number of 2-Digit PSIC FE
−0.201*** (0.0692) −0.0118 (0.0118)
ln (Hours Daily Outage) −0.0159*** (0.00416) −0.0139 (0.0165) 0.118*** (0.00719) 0.764*** (0.00570) 0.113*** (0.00574) 3538 0.959 23
ln (Hours Daily Outage)
ln (Daily Load Shedding) ln (Labor Costs) ln (Raw Materials) ln (Total Energy Costs) Observations R-squared Number of 2-Digit PSIC FE
(2)
0.464*** (0.0215) 0.451*** (0.0162) 0.0679*** (0.0166) 3749 0.670 23
−0.0407*** (0.0122) 0.0569 (0.0489) 0.466*** (0.0215) 0.451*** (0.0162) 0.0684*** (0.0166) 3749 0.670 23
The dependent variable is the natural log of firm-level value-added. Average Daily Outage indicates shortages experienced by firms for that distribution company, and Average Daily Load Shedding is the average daily number of hours of load-shedding experienced by firms for that distribution company. Heteroskedastic-robust standard errors are shown in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
The dependent variable is the natural log of firm-level revenues. Average Daily Outage indicates shortages experienced by firms for that distribution company, and Average Daily Load Shedding is the average daily number of hours of loadshedding experienced by firms for that distribution company. Heteroskedasticrobust standard errors are shown in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
when including duration, frequency and their interaction as in column (6), we see that both frequency and duration have a detrimental effect on revenues. The interaction term has the opposite sign, but the magnitude is significantly lower, so its offsetting impact is minor relative to the main effects. We now turn to Table 4, which shows the results of specifications that allow the impact of unexpected and expected outages due to loadshedding to vary. We find that a 1-h increase in the daily average unexpected outages decreases revenues by nearly 10%, but a similar increase in load-shedding has a smaller impact, decreasing revenues by 1.3%. As discussed in section 2, unexpected shortages would be more difficult for a manager to adapt to than planned load-shedding.
average total duration of outages (including both unexpected and load shedding) decreases firm-level revenues by roughly 0.15 percent. In Table 3 we also show specifications in which two indices for unexpected outages are included rather than the aggregate measure, which allows us to test whether duration and frequency of outages have different impacts on revenues. The estimates suggest that the duration and frequency of outages have somewhat offsetting effects. Holding constant the frequency of outages, longer outages have a significant detrimental effect on revenues. In our preferred specification in column (4), a 10 percent increase in the average duration of outages is associated with a decrease in revenues of roughly 0.23 percent. However, Table 5 Impact of outages on value added (natural log).
ln (Total Outages)
(1)
(2)
(3)
0.000830 (0.0147)
−0.0588*** (0.0180)
−0.0338*** (0.0111)
(4)
(5)
(6)
−0.00978*** (0.00293) 0.965* (0.500)
−0.0707*** (0.0173) 0.0781*** (0.0294)
ln (Dur Index) ln (Freq Index)
0.465*** (0.0215) 0.452*** (0.0162) 0.0673*** (0.0166)
0.468*** (0.0215) 0.450*** (0.0162) 0.0706*** (0.0166)
0.465*** (0.0215) 0.447*** (0.0163) 0.0709*** (0.0167)
−0.0218*** (0.00656) −4.755* (2.838) 0.000379** (0.000185) 0.468*** (0.0215) 0.447*** (0.0163) 0.0707*** (0.0167)
X 3749 0.670
X 3749 0.671
X 3749 0.670
X 3749 0.671
Duration Index Frequency Index Duration*Frequency ln (Total Labor Cost) ln (Raw Mat. Costs) ln (Total En. Costs) Constant Sector FE Observations R-squared
8.843*** (0.336) 4391 0.000
X 4391 0.002
Notes: The dependent variable is ln(value added). Total Outages represents the total duration (in minutes) of shortages (both load shedding and unexpected outages) experienced by firms for that distribution company. The Duration and Frequency Indices represent the average duration or number of shortages, respectively, experienced by firms for that distribution company. Heteroskedastic-robust standard errors are shown in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. 1006
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Table 7 Impact of outages on labor-output ratio.
ln (Total Outages)
(1)
(2)
(3)
(4)
−0.00560*** (0.000949)
−0.000734 (0.00102)
0.00111 (0.000888)
ln (Duration Index)
(5)
(6)
0.00500** (0.00231) −1.472*** (0.372)
0.00551*** (0.00141) −0.00948*** (0.00234)
ln (Frequency Index) Duration Index/1000
−0.0334*** (0.00120) 0.0112*** (0.00126)
−0.0336*** (0.00120) 0.0109*** (0.00126)
−0.411*** (0.0113) 0.154*** (0.0119)
0.0236*** (0.00488) 7.459*** (2.102) −0.000591*** (0.000137) −0.412*** (0.0113) 0.154*** (0.0118)
X 3515 0.247
X 3515 0.251
X 3515 0.350
X 3515 0.354
Freq Index/1000 Durat.*Freq./1000 ln (Raw Mat. Costs) ln (Total En. Costs) Constant
0.227*** (0.0215)
Sector FE Observations R-squared
X 3530 0.000
3530 0.010
Notes: The dependent variable is the labor share of revenues. Total Outages represents the total duration (in minutes) of shortages (both load shedding and unexpected outages) experienced by firms for that distribution company. Heteroskedastic-robust standard errors are shown in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
duration of outages each time. This result indicate that long outages are particularly harmful. In column (6) we allow for an interaction between frequency and duration. The main effects both are negative, while the interaction is positive. The interaction term has the opposite sign, but the magnitude is significantly lower, so its offsetting impact is minor relative to the main effects. In Table 6we present results of specifications that allow the impact of unexpected and expected outages due to load-shedding to vary. We find that a 1 h increase in the daily average shortage decreases revenues by roughly 20%, but a similar increase in load-shedding has a small, negative but insignificant impact (see Table 7).
Table 8 Impact of unexpected outages and load shedding on labor-output ratio. Dependent Variable:
Daily Outage (Hours) Daily Load Shedding (Hrs) ln (Daily Outage) ln (Daily Load Shedding) ln (Raw Materials) ln (Total Energy Costs) Observations R-squared
(1)
(2)
(3)
(4)
Labor Share of Output
ln (Labor Share of Output)
0.00407 (0.00555) −0.000136 (0.000927)
0.0780 (0.0524) 0.0189** (0.00875)
−0.0333*** (0.00120) 0.0112*** (0.00126) 3515 0.247
0.00182* (0.000990) −0.00772** (0.00393) −0.0335*** (0.00120) 0.0111*** (0.00126) 3515 0.248
−0.413*** (0.0113) 0.158*** (0.0119) 3515 0.348
0.0194** (0.00934) 0.00435 (0.0371) −0.414*** (0.0113) 0.158*** (0.0119) 3515 0.348
5.3. Labor share of output As with the revenue and value-added results, we find that an increase in outages tends to increase the labor share of output. That is, the cost of labor relative to the value of output increases when electricity outages are prevalent. Both duration and frequency of outages increase the labor share of output, while the interaction again has a small dampening effect. The results for planned vs. unplanned outages are consistent with the results for output and value-added, though the coefficients for the labor share of output are more noisily estimated.
The dependent variable is the firm's total labor costs as a share of total revenues (or the natural log of that ratio in (3) and (4)). Average Daily Outage indicates shortages experienced by firms for that distribution company, and Average Daily Load Shedding is the average daily number of hours of load-shedding experienced by firms for that distribution company. Heteroskedastic-robust standard errors are shown in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. There are 23 2-Digit PSIC Fixed Effects included in each regression.
6. Conclusion and policy implications Pakistan faces severe power shortages, but the question remains as to what impact shortages have on its economy. Using a survey of 4500 manufacturing firms in Punjab Province for fiscal year 2010–2011, we provide new empirical evidence on the impact of power shortages on firm productivity in Pakistan. Results suggest that there is indeed a strong negative correlation between electricity shortages and manufacturing revenues, value-added, and a positive relationship for the labor share of output. We use several measures of the reliability of power supply, including the duration and frequency of unexpected power outages, the total outages faced by firms, and separate measures for unexpected outages and load-shedding. Our results suggest that unexpected outages are especially detrimental to firm productivity. We find that an additional average daily hour of unexpected shortages decreases revenues by nearly 10%. Similarly, an increase in shortages by 1 h per day
5.2. Value-added In Table 5 we show estimates analogous to those for the product revenue regressions in Table 3, but with the dependent variable being the natural log of firm-level value-added. When the relevant controls and sector fixed effects are included (i.e. column 3), we find that a 10% increase in outages leads to a 0.34 percent decrease in value-added. In column (4) we include indices for duration of unexpected outages and the frequency of unexpected outages. As with our revenue regressions, we find that holding constant the total duration of outages in a year, more frequent shortages have an offsetting effect on the decrease in value-added due to power shortages. When the total duration of outages is held constant in a year, more frequent outages means shorter
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decreases value-added at the firm level by roughly 20%, and increases the labor share of output. We find that the impact for a similar amount of load-shedding is significantly smaller, likely due to predictability and firm adaptation. We note that the overall cost of shortages in Pakistan (and elsewhere in South Asia) are likely much larger than indicated by our estimates. In the absence of outages, firms would likely to behave very differently, including producing at different times during the day, employing different mixes of capital and labor, and even engaging in different activities altogether. Therefore, we believe our estimates to be conservative and underestimates of the actual losses due to the frequent outages experienced by firms in Pakistan. There are a few caveats worth discussing. First, as discussed earlier, self-generation at the firm level is likely to endogenous to outages; Second, outages are likely to be endogenous to economic growth at the district level. Endogeneity of shortages at firm- and district-level could both lead to an underestimation of the impact of power shortages on firm productivity. Therefore, our estimated impact of power shortages is likely to be a lower bound of the true effect. Producing a reliable source of electricity would no doubt help generate significant growth in Pakistan. As a development imperative, improving the reliability of electricity should be a first-order concern, but because demand for electricity would grow along with per-capita GDP, this provides a critical opportunity to consider a comprehensive power sector reform. That includes addressing pre-existing
inefficiencies in the generation and distribution networks, removing distortions in the pricing and subsidies in the sector, and expanding investment in hydroelectric capacity and renewables. References Abeberese, A.B., 2017. Electricity cost and firm performance: evidence from India. Rev. Econ. Stat. 99 (5), 839–852. Alby, P., Dethier, J.-J., Straub, S., et al., 2011. Let There Be Light! Firms Operating under Electricity Constraints in Developing Countries. Technical Report. Toulouse School of Economics (TSE). Allcott, H., Collard-Wexler, A., O'Connell, S.D., 2016. How do electricity shortages affect industry? Evidence from India. Am. Econ. Rev. 106 (3), 587–624. Andersen, T.B., Dalgaard, C.-J., 2013. Power outages and economic growth in Africa. Energy Econ. 38, 19–23. Business Recorder. https://fp.brecorder.com/2017/03/20170305148067/2017. Fisher-Vanden, K., Mansur, E.T., Wang, Q.J., 2015. Electricity shortages and firm productivity: evidence from China's industrial firms. J. Dev. Econ. 114, 172–188. Foster, V., Steinbuks, J., 2009. Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa. Grainger, C., Zhang, F., 2017. The Impact of Electricity Shortages on Small Enterprises: Evidence from India. World Bank Policy Research Working Paper. McRae, S., 2014. Infrastructure quality and the subsidy trap. Am. Econ. Rev. 105 (1), 35–66. USAID, 2011. Evaluation of Economic Value of Natural Gas in Various Sectors of Pakistan. World Bank, 2015. Second Power Sector Reform Development Policy Credit Program Document. Washington DC. Zhang, Fan, 2019. In the Dark: How Much Do Power Sector Distortions Cost South Asia. World Bank Reports, Washington DC.
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Contents lists available at ScienceDirect
Energy Policy journal homepage: www.elsevier.com/locate/enpol
Electricity shortages and manufacturing productivity in Pakistan ∗
T
Corbett A. Grainger , Fan Zhang University of Wisconsin-Madison, World Bank, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Electricity shortages Pakistan Manufacturing productivity Climate changeJEL Classification: D04 D24 L11 L94 O12 O13 Q48
Electricity shortages present a significant challenge to manufacturers who require a reliable power source as an input to production. In Pakistan, power shortages are commonplace, but empirical evidence on the impact of shortages is still lacking. Using a survey of 4500 manufacturing firms for the year 2010–2011, we exploit regional differences in outages to estimate the impact of electricity shortages on firm revenues, value-added and the labor share of output. We find that an additional average daily hour of unexpected shortages decreases annual revenues by nearly 10%. Similarly, an increase in shortages by 1 h per day decreases annual value-added at the firm level by roughly 20%, and increases the labor share of output. We find that the impact for a similar amount of load-shedding is significantly smaller, likely due to predictability and firm adaptation. Our results suggest that a more reliable electricity supply would significantly improve manufacturing productivity in the region.
1. Introduction In many developing countries, a significant barrier to economic growth is unreliable supply of electricity. Electricity shortages in South Asia are especially widespread, with the average firm experiencing nearly an outage per day, lasting roughly 5.3 h each1. Within South Asia, Pakistan had the most severe power shortages, with the average firm reporting 2.5 outages each day for an average total daily duration of 13.2 h. More than 75 percent of firms in Pakistan identified a lack of reliable electricity as a major constraint to their operation and growth (Fig. 1). Outages occur because of technical failures. In South Asia, they also reflect the efforts of utilities to cope with persistent power shortages through scheduled blackouts, known as load shedding. (see Table 8) Because electricity is an essential input for most business processes, an unreliable supply can hut firm's productivity. Several recent studies have quantified the impacts of electricity shortages on firm-level outcomes. Allcott et al., (2016) estimate the productivity impacts of large manufacturing firms in India. Grainger and Zhang (2017) follow a similar identification strategy but focus on micro-, small, and medium Indian enterprises, and they find much larger impacts on unit costs. Fisher-Vanden et al. (2015) examine the productivity and environmental effects of electricity shortages in China. Foster and Steinbuks (2009), Alby et al. (2011) and Andersen and Dalgaard (2013) examine
the impact of outages on firm size and technological adoption. Our paper contributes to the literature in two ways. First, despite the wide recognition that electricity shortages result in substantial losses for the economy in Pakistan, empirical estimates of the impact of power shortages in Pakistan are still lacking. Second, to the best of our knowledge, this is the first paper that differentiates the impact of expected and unexpected power outages on firms. To study the impact of power shortages on manufacturing firms in Pakistan, we match firmlevel data from Census of Manufacturing Industry conducted by Pakistan Bureau of Statistics with district-level power outage data (both expected and unexpected) reported by distribution utilities. Using a sample of 4500 manufacturing firms for 2011, we estimate the impact of electricity shortages on manufacturing productivity. Productivity is measured by both revenue productivity and labor productivity. Revenue productivity is defined as firm-level total revenue and valueadded per unit of input costs (including labor, raw materials, and energy input costs). Labor productivity is defined as labor input per unit of outputs. Our estimates suggest that an additional hour per day of expected outages (scheduled blackouts) and unexpected outages decreases revenues by nearly 1.3% and 10%, respectively. Similarly, an increase in unexpected outages by 1 h per day decreases value-added at the firm level by roughly 20% and increases the labor share of output. The impact for a similar amount of expected outages is significantly smaller, likely due to predictability and firm adaptation.
∗
Corresponding author. E-mail address: [email protected] (C.A. Grainger). 1 World Bank Enterprise Survey 2011–2015. https://doi.org/10.1016/j.enpol.2019.05.040 Received 3 December 2018; Received in revised form 18 May 2019; Accepted 20 May 2019 Available online 03 July 2019 0301-4215/ © 2019 Published by Elsevier Ltd.
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Fig. 1. Power outages and their impact. Source: World Bank Enterprise Survey for Pakistan (2013).
sector. Distributors are often not fully compensated through the combination of tariffs and subsidies, so they in turn cannot fully compensate generators, who cannot pay fuel suppliers, which then cut off their fuel supply, leading to idled generation capacity and electricity shortages. In Pakistan, accumulated arrears of distribution companies to suppliers, commonly known as circular debt, reached about PRs 414 billion by March 2017 (Business Recorder, 2017). Circular debt has caused up to 5 GW (GW) of capacity to lie idle, accounting for almost 22 percent of the total installed capacity in Pakistan (World Bank, 2015). Furthermore, when capacity is used, it is substantially inefficient. For example, the average efficiency of gas power plants in Pakistan is 30 percent, compared to the average efficiency of 43 percent for gas power plants in the United States2. Aging infrastructure and overloaded transmission and distribution systems also contributed to high losses in the network. In 2016, average transmission and distribution losses have reached to about 20 percent of all the electricity generated. Another cause for Pakistan's power shortages is upstream gas shortfalls. Underpricing of domestic gas has contributed to widening gap between domestic supply and demand for gas. Meanwhile, there are huge losses in gas transmission and distribution, which reached 12 percent of total supply in 2016. In addition, domestic gas supply is routinely diverted from power to other sectors such as fertilizer and transport even though the economic benefit of using gas in power generation is expected to be among the highest in all sectors in Pakistan in the medium term (USAID, 2011).
As we discuss in this paper, there are several reasons our estimates are likely lower bounds of the costs of shortages in Pakistan. However, these caveats aside, our estimates suggest that addressing the electricity would have a large, positive impact on productivity and growth. Providing a reliable flow of electricity would undoubtedly lead to substantial improvements in productivity for existing firms, but it would also likely lead to additional investment in manufacturing and other sectors. There are several institutional and economic causes of the shortages experienced in Pakistan, and a comprehensive sectoral reform would likely be necessary to solve the electricity shortage problems facing Pakistan. This includes addressing inefficiencies in the distribution and generation network, addressing pricing problems on inputs and outputs in electricity markets, and investing in additional capacity in hydro- and renewable energy. In what follows, we provide an institutional and economic background on Pakistan, focusing on electricity shortages and their causes. We then describe the firm-level data and power shortage data in Section 3, describe the empirical model in Section 4, and provide the corresponding cross-sectional estimates in Section 5. Section 6 offers a discussion and concluding thoughts. 2. Background 2.1. Causes of power shortages Pakistan faces massive electricity shortages. Since 2006, nationwide power shortages, the difference between projected demand and actual supply, have been steadily increasing each year and has risen to 26 percent of total demand, or 29 TWh in 2013 (the latest year such data is available) (Fig. 2). To address supply shortfalls, utilities implemented systematic rolling blackouts that can last 6–14 h each day. In some areas load shedding of 18–20 h occurred on a regular basis. Multiple price and institutional distortions have contributed to the current power crisis in Pakistan (Zhang, 2019). On the pricing side, historically electricity prices have been set much lower than the cost of supply. The gap is largely financed by direct budget support. Recent electricity tariff reforms have substantially lowered subsidy spending. However, they still amounted to $2.15 billion (0.8 percent of GDP) in fiscal year 2015. Underpricing of electricity undermines the incentives of power utilities to provide high-quality services, creating a so-called “subsidy trap” (McRae, 2014). Institutional problems are present at different levels throughout the
2.2. Impact of power shortages on firms Electricity shortages could affect firm productivity in several ways. First, electricity shortages may force firms to invest in expensive dieselbased captive generators, thereby diverting capital from more productive uses. Second, when firms lack an alternative source of electricity, they must shut down operation, causing wastes of non-flexible and semi-flexible inputs, such as labor and certain material inputs that can be spoiled during a power outage (Allcott et al. 2016). Third, facing persistent power shortages, firms may choose to purchase rather than make electricity-intensive intermediate inputs. Because outsourcing is more costly, electricity shortage would significantly increase 2 United States Energy Information Administration. https://www.eia.gov/ electricity/annual/html/epa_08_01.html.
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Fig. 2. Power shortages in Pakistan (%) (2006–2013). Source: Electricity Demand Forecast by Planning Power of National Transmission and Dispatch Company (NTDC) (2014).
production costs (Fisher-Vanden et al., 2015). Finally, electricity scarcity may induce firms to substitute away from electricity-intensive technology altogether. Because production processes that are less electricity-intensive also tend to be less technologically advanced, switching to such processes undermines long-term productivity growth for firms (Abeberese, 2017). The impact power shortages have on firm productivity is industry specific: more electricity-intensive industries are likely to be more adversely affected. The size of the impact would also depend on the type of power outages. The effect of unanticipated outages is likely to be much larger than scheduled outages. Firms can attenuate the impact of preannounced load-shedding by adjusting the scheduling of labor and storing away perishable material inputs, therefore significantly reduce the losses of semi-flexible inputs. In contrast, firms, especially those without captive generators, are much less able to cope with unanticipated outages.
Table 1 Distribution of firms in census by two-digit PSIC. Source: Authors' calculations using 2010-11 Census of Manufacturing Industries. Two-Digit PSIC
Activity Description
Number Firms
10 11 12 13 14 15 16
Manufacture of food products Manufacture of beverages Manufacture of tobacco products Manufacture of textiles Manufacture of wearing apparel Manufacture of leather and related products Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials Manufacture of paper and paper products Printing and reproduction of recorded media Manufacture of coke and refined petroleum products Manufacture of chemicals and chemical products Manufacture of basic pharmaceutical products and pharmaceutical preparations Manufacture of rubber and plastics products Manufacture of other non-metallic mineral products Manufacture of basic metals Manufacture of fabricated metal products, except machinery and equipment Manufacture of computer, electronic and optical products Manufacture of electrical equipment Manufacture of machinery and equipment n.e.c. Manufacture of motor vehicles, trailers and semitrailers Manufacture of other transport equipment Manufacture of furniture Other manufacturing
669 21 3 1235 183 77 11
17 18 19 20 21
3. Data
22 23 24 25
Data for this study come from two sources: the 2010-11 Census of Manufacturing Industries conducted by Pakistan Bureau of Statistics. The second data source is the distribution company performance evaluation report by National Electric Power Regulatory Authority (NEPRA). We describe these in turn.
26 27 28 29
3.1. Census of Manufacturing Industries The Census of Manufacturing Industries provides a thorough annual overview of firm-level activities, including detailed information on a variety of input costs, including labor, capital and electricity, as well as revenue data. The 2010–2011 Census mainly covers firms in Punjab province3. Punjab in Pakistan has the largest economy of all provinces and the majority of the manufacturing activity is concentrated in this area. The Census covers 4499 firms in 23 sectors at the 2-digit level of Pakistan Standard Industrial Classification (PSIC)4. The distribution of firms by the 2-digit classification are shown in Table 1. Of the 23
30 31 32
177 53 10 154 96 150 135 165 195 15 231 187 112 89 39 480
divisions, number 13 (Manufacturing of Textiles) covers roughly 28 percent of our sample, division 10 (Manufacturing of Food Products) accounts for 15 percent, and division 32 (Other Manufacturing) is the third-largest with 11 percent of the sample. The 23 sectors can be further broken down into 236 subcategories at the 5-digit level of PSIC. Firm-level characteristics are described in Table 2. The mean total product value at the firm level is about 523,000 Rupees5, whereas the mean value-added is 114,000 Rupees. Average total employment costs are just over 17,000 Rupees, whereas the average cost of raw materials
There are four firms in the dataset located outside Punjab province, including one in Karachi and three in Islamabad. Excluding these firms from the sample does not change the estimation results. 4 A description of the PSIC and the relationship to ISIC is available at the following http://www.pbs.gov.pk/sites/default/files/other/PSIC_2010.pdf. 3
5
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The Rupee-USD exchange rate was roughly 85:1 in June 2011.
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Table 2 Descriptive statistics of manufacturing firms. Variable
Obs.
Mean (thousands)
Std Dev (thousands)
Minimum
Maximum (thousands)
Product Value Value Added Total Employ. Cost Raw Materials Cost Value of Fixed Assets Electricity Costs
3585 4444 4488 3841 3975 4443
522.82 113.69 17.57 316.02 148.79 12.07
2944.99 1021.25 98.60 2146.54 1137.34 72.98
180 −4,429,910 36 25 24 2
125,000.00 32,400.00 3430.71 114,000.00 34,800.00 1880.29
Notes: Authors' calculations from the 2010-11 Census of Manufacturing Industries.
is over 300,000 Rupees. Firms have an average value of fixed assets of 150,000 Rupees, which is notably half the value of the raw material costs for the average firm and less than a third of the value of the product output. The median number of employees at a firm in the census is 22 people, with an average of 103 and a standard deviation of 569.
of unexpected outages on firm productivity because they may have different impacts on manufacturing firms. The frequency of disruptions may have a smaller impact if load-shedding is anticipated, but we expect that longer durations will have a measurable impact regardless of regularity. Finally, our preferred specifications will separately identify the effect of shortages and outages due to load-shedding.
3.2. Shortage data
4. Empirical approach
Power shortage data are reported annually by distribution companies (DISCO) to NEPRA and are published in the DISCO Performance Evaluation Report. The latest evaluation report includes shortage data at the DISCO level from year 2010–2011 to year 2014–2015. There are 11 distribution companies in Pakistan, among which 6 provide service in Punjab province. We identify service areas by district for each DISCO and match firm-level data with shortage data based on district-level identifiers in the census data for year 2010–2011. The providers (and corresponding number of firms) in our data are IESCO (120), GEPCO (1,517), FESCO (1,281), LESCO (1,159), and MEPCO (372). Although power shortages are nationwide and systematic, there is large variation in shortages at the DISCO level, which is partially linked to differences in supply and demand for electricity and partially linked to differences in operating performance of DISCOs. In fiscal year 2014, for example, average transmission and distribution losses ranges from 9.4 percent to 38.2 percent, while collection rate ranges from 33 percent to over 100 percent across the 11 distribution companies. Shortages are characterized by two reliability indices and average daily load-shedding hours reported by distribution companies. One reliability index represents the frequency of shortages, and the second represents the duration of shortages. The System Average Interruption Frequency Index (SAIFI) is the average number of interruptions that a customer experiences in a year. Specifically, SPAIFI is calculated as the total annual number of consumer supply interruptions divided by the total number of consumers that the distribution company serves in any a given year. The second measure, the System Average Interruption Duration Index (SAIDI), captures the outage duration (in minutes) that an average customer experiences in a year. Both SAIFI and SAIDI do not encompass outages caused by load shedding and therefore indicate the severity of unexpected outages. There is still large variation in the value of SAIFI and SAIDI across regions. Among the distribution companies in our data, the SAIFI measure ranges from 0.41 (IESCO) to 185.5 (MEPCO) (Fig. 3), while the SAIDI measure ranges from a minimum value of 22.6 min for IESCO to a maximum of 15,896 min (roughly 11 days) for PESCO (Fig. 4). Among the distribution companies in our data, load shedding varies from 1 h per day to 9.8 h per day, with the average firm facing 7.3 h per day. In our econometric analysis, we exploit regional variation in the reliability of power supply measured by both SAIDI and SAIFI, and total load-shedding hours to identify the relationship between shortages and firm productivity. For the year 2010–11, these two reliability indexes have a correlation coefficient of −0.13 across the 11 distribution companies covered in the NEPRA report, but for the six distribution companies in Punjab Province, the correlation coefficient for the SAIDI and SAIFI is about 0.54. We incorporate both measures of reliability to estimate the impact
Given the data described above, we are interested in the impact of electricity shortages on firm-level productivity measured by valueadded and revenues, holding constant costs of inputs and firm-specific characteristics, as well as labor-output ratio. As our data are crosssectional, the variation in outages is between distribution companies servicing the manufacturing firms. A regression of the following form is estimated as the baseline specification:
Yij = α + β1 ∗ ln(UOi ) + β2 ∗ ln(SOi ) + γ ∗ Xi + σj + εij
(1)
where Yij is the outcome of interest (i.e. the natural log of revenues, the natural log of value-added, or the labor-output ratio) for firm i in sector j. UOi and SOi are the two types of shortages – unexpected outages and scheduled outages (load shedding) – reported by the distribution company by which firm i is serviced. Xi are input costs and other firmlevel characteristics, including firms' labor costs, raw material costs, and total energy costs. Because the effect of shortages is sector-specific, we control for sector fixed effect (σj) to capture unobserved common characteristics to all firms in the sector6, and εij is the idiosyncratic error term. β1 and β2 are the two main coefficients to be estimated. As described in Section II, electricity shortages would increase production costs and reduce marginal product of labor, we expect β1 and β2 to be negative when the dependent variables are revenues and value-added, and negative when the dependent variable is labor-output ratio. In addition, we expect β1 to be larger than β2 in absolute value. In addition, we estimate the following equation to disentangle the mechanism through which unanticipated outages affect firm productivity.
Yij = α + β ∗ ln(Shortagei ) + γ1 ∗ ln(Fi ) + γ2 ∗ ln(Di ) + γ ∗ Xi + σij + εij (2) Where Shortagei is the total duration of shortages (both load shedding and unexpected outages) reported by the distribution company by which firm i in sector j is serviced. Fi and Di are the two reliability indexes that measure the frequency and duration of anticipated outages7. All the other parameters follow the same denotation as 6 There could be heterogeneous effects of shortages at the sector level. For example, energy intensive sectors may be more vulnerable to power supply disruptions. However, estimations of the heterogeneity effects at the sector level are noisy because of a small sample size. These results are not reported in the paper. 7 We also tested for inclusion of quadratic terms for the outage variables in a log-linear specification; inclusion of these terms did not change the qualitative findings and higher-order terms were typically insignificant, so the results are
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Fig. 3. System average interruption frequency index. Notes: SAIFI is the average number of interruptions that a customer experiences in a year. Specifically, SPAIFI is calculated as the total annual number of consumer supply interruptions divided by the total number of consumers that the distribution company serves in any a given year. For the purposes of illustration, the index has been transformed using the inverse hyperbolic sine. Source: DISCO Performance Evaluation Report 2014–2015 by NEPRA.
measurement of shortages, because we only capture district-level average outages, but not actual outage experienced by each firm. In the following, we present our estimation results while recognizing the above identification challenges.
introduced in equation (1). As discussed in Section II, the main productivity loss for plants without generators is wastes of non-flexible and semi-flexible inputs. We expect the negative effect of duration of unanticipated outages on productivity to be large, as longer outages imply more wasted inputs that cannot be reallocated; the relative magnitude of the impact of outage frequency (holding constant total outage hours) is less clear. Due to data limitations, there are challenges to causal identification of the impact of shortages on firm-level revenues or value-added. First, there is likely unobserved firm-level heterogeneity, which can affect both firm performance and shortages experienced by the firm. For example, firms experience higher level of shortages are more likely to acquire self-generators8. Second, shortage is likely to be endogenous to growth and business climate at the district level. For example, areas that provide more friendly business environment and experienced faster economic growth can have higher demand for electricity. This will in turn result in worse power shortages (Allcott et al., 2016; Grainger and Zhang, 2017). Finally, there is likely measurement error in our
5. Empirical results Following the approach outlined in Section 4, we first estimate the impact of electricity shortages on three firm-level outcomes: product revenues, value-added, and the labor share of output. 5.1. Product revenues Table 3 presents the results in which the dependent variable is the natural log of product revenues at the firm level. The control variables for firms’ labor costs, raw material costs and total electricity costs all have positive, significant impacts on product revenues, as expected9. Furthermore, the regressions explain a significant amount of variation in product revenues when we include sector-specific fixed effects. The first two columns do not include controls for other inputs, and the first column does not include sector-specific intercepts. When the relevant controls are included, we find that a 10 percent increase in the
(footnote continued) not shown to conserve space. 8 We are unable to account for self-generation in the data. Though we have information on other fuel use, the effects (which are not shown) are virtually the same for firms who report purchasing other fuels, suggesting this measure of self-generation is far from perfect.
9 In most cases the value of fixed capital assets is not directly measured, but instead imputed, so we do not include that variable in our main specifications.
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Fig. 4. System average interruption duration index. Notes: SAIDI is the outage duration (in minutes) that an average customer experiences in a year. Source: DISCO Performance Evaluation Report 2014–2015 by NEPRA.
Table 3 Impact of shortage on total revenues (natural log).
ln (Total Outages)
(1)
(2)
(3)
(4)
0.167*** (0.0188)
0.0410** (0.0183)
−0.0153*** (0.00373)
(5)
(6)
−0.00343*** (0.00103) 0.316* (0.166)
−0.0232*** (0.00595) 0.0140 (0.00987)
ln (Duration Index) ln (Frequency Index)
0.118*** (0.00719) 0.765*** (0.00569) 0.113*** (0.00574)
0.119*** (0.00721) 0.765*** (0.00569) 0.113*** (0.00574)
0.118*** (0.00721) 0.763*** (0.00571) 0.113*** (0.00575)
−0.0116*** (0.00218) −3.621*** (0.937) 0.000260*** (6.10e-05) 0.119*** (0.00720) 0.763*** (0.00570) 0.113*** (0.00574)
X 3538 0.959
X 3538 0.959
X 3538 0.959
X 3538 0.959
Duration Index Frequency Index Duration*Frequency ln (Total Labor Cost) ln (Raw Mat. Costs) ln (Total En. Costs) Constant Sector FE Observations R-squared
7.107*** (0.419) 3553 0.025
X 3553 0.001
Notes: The dependent variable is ln(revenues). Total Outages represents the total duration (in minutes) of shortages experienced (both load shedding and unexpected outages) by firms for that distribution company. The Duration and Frequency Indices represent the average duration or number of shortages, respectively, experienced by firms for that distribution company. Heteroskedastic-robust standard errors are shown in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. 1005
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Table 4 Impact of Unexpected outages and Load Shedding on Total Revenues (Natural Log). (1)
Table 6 Impact of unexpected outages and load shedding on value added (natural log). (1)
(2) Hours/Day Outage (Hours)
−0.0934*** (0.0233) −0.0126*** (0.00389)
Hours/Day Outage (Hours) Hours/Day Load Shedding (Hrs)
Hours/Day Load Shedding (Hrs)
ln (Daily Load Shedding) ln (Labor Costs)
0.118*** (0.00719) 0.764*** (0.00569) 0.113*** (0.00574) 3538 0.959 23
ln (Raw Materials) ln (Total Energy Costs) Observations R-squared Number of 2-Digit PSIC FE
−0.201*** (0.0692) −0.0118 (0.0118)
ln (Hours Daily Outage) −0.0159*** (0.00416) −0.0139 (0.0165) 0.118*** (0.00719) 0.764*** (0.00570) 0.113*** (0.00574) 3538 0.959 23
ln (Hours Daily Outage)
ln (Daily Load Shedding) ln (Labor Costs) ln (Raw Materials) ln (Total Energy Costs) Observations R-squared Number of 2-Digit PSIC FE
(2)
0.464*** (0.0215) 0.451*** (0.0162) 0.0679*** (0.0166) 3749 0.670 23
−0.0407*** (0.0122) 0.0569 (0.0489) 0.466*** (0.0215) 0.451*** (0.0162) 0.0684*** (0.0166) 3749 0.670 23
The dependent variable is the natural log of firm-level value-added. Average Daily Outage indicates shortages experienced by firms for that distribution company, and Average Daily Load Shedding is the average daily number of hours of load-shedding experienced by firms for that distribution company. Heteroskedastic-robust standard errors are shown in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
The dependent variable is the natural log of firm-level revenues. Average Daily Outage indicates shortages experienced by firms for that distribution company, and Average Daily Load Shedding is the average daily number of hours of loadshedding experienced by firms for that distribution company. Heteroskedasticrobust standard errors are shown in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
when including duration, frequency and their interaction as in column (6), we see that both frequency and duration have a detrimental effect on revenues. The interaction term has the opposite sign, but the magnitude is significantly lower, so its offsetting impact is minor relative to the main effects. We now turn to Table 4, which shows the results of specifications that allow the impact of unexpected and expected outages due to loadshedding to vary. We find that a 1-h increase in the daily average unexpected outages decreases revenues by nearly 10%, but a similar increase in load-shedding has a smaller impact, decreasing revenues by 1.3%. As discussed in section 2, unexpected shortages would be more difficult for a manager to adapt to than planned load-shedding.
average total duration of outages (including both unexpected and load shedding) decreases firm-level revenues by roughly 0.15 percent. In Table 3 we also show specifications in which two indices for unexpected outages are included rather than the aggregate measure, which allows us to test whether duration and frequency of outages have different impacts on revenues. The estimates suggest that the duration and frequency of outages have somewhat offsetting effects. Holding constant the frequency of outages, longer outages have a significant detrimental effect on revenues. In our preferred specification in column (4), a 10 percent increase in the average duration of outages is associated with a decrease in revenues of roughly 0.23 percent. However, Table 5 Impact of outages on value added (natural log).
ln (Total Outages)
(1)
(2)
(3)
0.000830 (0.0147)
−0.0588*** (0.0180)
−0.0338*** (0.0111)
(4)
(5)
(6)
−0.00978*** (0.00293) 0.965* (0.500)
−0.0707*** (0.0173) 0.0781*** (0.0294)
ln (Dur Index) ln (Freq Index)
0.465*** (0.0215) 0.452*** (0.0162) 0.0673*** (0.0166)
0.468*** (0.0215) 0.450*** (0.0162) 0.0706*** (0.0166)
0.465*** (0.0215) 0.447*** (0.0163) 0.0709*** (0.0167)
−0.0218*** (0.00656) −4.755* (2.838) 0.000379** (0.000185) 0.468*** (0.0215) 0.447*** (0.0163) 0.0707*** (0.0167)
X 3749 0.670
X 3749 0.671
X 3749 0.670
X 3749 0.671
Duration Index Frequency Index Duration*Frequency ln (Total Labor Cost) ln (Raw Mat. Costs) ln (Total En. Costs) Constant Sector FE Observations R-squared
8.843*** (0.336) 4391 0.000
X 4391 0.002
Notes: The dependent variable is ln(value added). Total Outages represents the total duration (in minutes) of shortages (both load shedding and unexpected outages) experienced by firms for that distribution company. The Duration and Frequency Indices represent the average duration or number of shortages, respectively, experienced by firms for that distribution company. Heteroskedastic-robust standard errors are shown in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. 1006
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Table 7 Impact of outages on labor-output ratio.
ln (Total Outages)
(1)
(2)
(3)
(4)
−0.00560*** (0.000949)
−0.000734 (0.00102)
0.00111 (0.000888)
ln (Duration Index)
(5)
(6)
0.00500** (0.00231) −1.472*** (0.372)
0.00551*** (0.00141) −0.00948*** (0.00234)
ln (Frequency Index) Duration Index/1000
−0.0334*** (0.00120) 0.0112*** (0.00126)
−0.0336*** (0.00120) 0.0109*** (0.00126)
−0.411*** (0.0113) 0.154*** (0.0119)
0.0236*** (0.00488) 7.459*** (2.102) −0.000591*** (0.000137) −0.412*** (0.0113) 0.154*** (0.0118)
X 3515 0.247
X 3515 0.251
X 3515 0.350
X 3515 0.354
Freq Index/1000 Durat.*Freq./1000 ln (Raw Mat. Costs) ln (Total En. Costs) Constant
0.227*** (0.0215)
Sector FE Observations R-squared
X 3530 0.000
3530 0.010
Notes: The dependent variable is the labor share of revenues. Total Outages represents the total duration (in minutes) of shortages (both load shedding and unexpected outages) experienced by firms for that distribution company. Heteroskedastic-robust standard errors are shown in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
duration of outages each time. This result indicate that long outages are particularly harmful. In column (6) we allow for an interaction between frequency and duration. The main effects both are negative, while the interaction is positive. The interaction term has the opposite sign, but the magnitude is significantly lower, so its offsetting impact is minor relative to the main effects. In Table 6we present results of specifications that allow the impact of unexpected and expected outages due to load-shedding to vary. We find that a 1 h increase in the daily average shortage decreases revenues by roughly 20%, but a similar increase in load-shedding has a small, negative but insignificant impact (see Table 7).
Table 8 Impact of unexpected outages and load shedding on labor-output ratio. Dependent Variable:
Daily Outage (Hours) Daily Load Shedding (Hrs) ln (Daily Outage) ln (Daily Load Shedding) ln (Raw Materials) ln (Total Energy Costs) Observations R-squared
(1)
(2)
(3)
(4)
Labor Share of Output
ln (Labor Share of Output)
0.00407 (0.00555) −0.000136 (0.000927)
0.0780 (0.0524) 0.0189** (0.00875)
−0.0333*** (0.00120) 0.0112*** (0.00126) 3515 0.247
0.00182* (0.000990) −0.00772** (0.00393) −0.0335*** (0.00120) 0.0111*** (0.00126) 3515 0.248
−0.413*** (0.0113) 0.158*** (0.0119) 3515 0.348
0.0194** (0.00934) 0.00435 (0.0371) −0.414*** (0.0113) 0.158*** (0.0119) 3515 0.348
5.3. Labor share of output As with the revenue and value-added results, we find that an increase in outages tends to increase the labor share of output. That is, the cost of labor relative to the value of output increases when electricity outages are prevalent. Both duration and frequency of outages increase the labor share of output, while the interaction again has a small dampening effect. The results for planned vs. unplanned outages are consistent with the results for output and value-added, though the coefficients for the labor share of output are more noisily estimated.
The dependent variable is the firm's total labor costs as a share of total revenues (or the natural log of that ratio in (3) and (4)). Average Daily Outage indicates shortages experienced by firms for that distribution company, and Average Daily Load Shedding is the average daily number of hours of load-shedding experienced by firms for that distribution company. Heteroskedastic-robust standard errors are shown in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. There are 23 2-Digit PSIC Fixed Effects included in each regression.
6. Conclusion and policy implications Pakistan faces severe power shortages, but the question remains as to what impact shortages have on its economy. Using a survey of 4500 manufacturing firms in Punjab Province for fiscal year 2010–2011, we provide new empirical evidence on the impact of power shortages on firm productivity in Pakistan. Results suggest that there is indeed a strong negative correlation between electricity shortages and manufacturing revenues, value-added, and a positive relationship for the labor share of output. We use several measures of the reliability of power supply, including the duration and frequency of unexpected power outages, the total outages faced by firms, and separate measures for unexpected outages and load-shedding. Our results suggest that unexpected outages are especially detrimental to firm productivity. We find that an additional average daily hour of unexpected shortages decreases revenues by nearly 10%. Similarly, an increase in shortages by 1 h per day
5.2. Value-added In Table 5 we show estimates analogous to those for the product revenue regressions in Table 3, but with the dependent variable being the natural log of firm-level value-added. When the relevant controls and sector fixed effects are included (i.e. column 3), we find that a 10% increase in outages leads to a 0.34 percent decrease in value-added. In column (4) we include indices for duration of unexpected outages and the frequency of unexpected outages. As with our revenue regressions, we find that holding constant the total duration of outages in a year, more frequent shortages have an offsetting effect on the decrease in value-added due to power shortages. When the total duration of outages is held constant in a year, more frequent outages means shorter
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decreases value-added at the firm level by roughly 20%, and increases the labor share of output. We find that the impact for a similar amount of load-shedding is significantly smaller, likely due to predictability and firm adaptation. We note that the overall cost of shortages in Pakistan (and elsewhere in South Asia) are likely much larger than indicated by our estimates. In the absence of outages, firms would likely to behave very differently, including producing at different times during the day, employing different mixes of capital and labor, and even engaging in different activities altogether. Therefore, we believe our estimates to be conservative and underestimates of the actual losses due to the frequent outages experienced by firms in Pakistan. There are a few caveats worth discussing. First, as discussed earlier, self-generation at the firm level is likely to endogenous to outages; Second, outages are likely to be endogenous to economic growth at the district level. Endogeneity of shortages at firm- and district-level could both lead to an underestimation of the impact of power shortages on firm productivity. Therefore, our estimated impact of power shortages is likely to be a lower bound of the true effect. Producing a reliable source of electricity would no doubt help generate significant growth in Pakistan. As a development imperative, improving the reliability of electricity should be a first-order concern, but because demand for electricity would grow along with per-capita GDP, this provides a critical opportunity to consider a comprehensive power sector reform. That includes addressing pre-existing
inefficiencies in the generation and distribution networks, removing distortions in the pricing and subsidies in the sector, and expanding investment in hydroelectric capacity and renewables. References Abeberese, A.B., 2017. Electricity cost and firm performance: evidence from India. Rev. Econ. Stat. 99 (5), 839–852. Alby, P., Dethier, J.-J., Straub, S., et al., 2011. Let There Be Light! Firms Operating under Electricity Constraints in Developing Countries. Technical Report. Toulouse School of Economics (TSE). Allcott, H., Collard-Wexler, A., O'Connell, S.D., 2016. How do electricity shortages affect industry? Evidence from India. Am. Econ. Rev. 106 (3), 587–624. Andersen, T.B., Dalgaard, C.-J., 2013. Power outages and economic growth in Africa. Energy Econ. 38, 19–23. Business Recorder. https://fp.brecorder.com/2017/03/20170305148067/2017. Fisher-Vanden, K., Mansur, E.T., Wang, Q.J., 2015. Electricity shortages and firm productivity: evidence from China's industrial firms. J. Dev. Econ. 114, 172–188. Foster, V., Steinbuks, J., 2009. Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa. Grainger, C., Zhang, F., 2017. The Impact of Electricity Shortages on Small Enterprises: Evidence from India. World Bank Policy Research Working Paper. McRae, S., 2014. Infrastructure quality and the subsidy trap. Am. Econ. Rev. 105 (1), 35–66. USAID, 2011. Evaluation of Economic Value of Natural Gas in Various Sectors of Pakistan. World Bank, 2015. Second Power Sector Reform Development Policy Credit Program Document. Washington DC. Zhang, Fan, 2019. In the Dark: How Much Do Power Sector Distortions Cost South Asia. World Bank Reports, Washington DC.
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