It is the middle of April 2020. Restaurants around the world stand empty.
In the U.S. we are entering the second month of Covid-19 restrictions on movement, business and social activities. As with most of the world, virtually the entire country is under some form of ‘stay at home’ order or lockdown.
Turbulence and uncertainty
The coronavirus pandemic has created huge uncertainties, including: how the easily the virus is transmitted, why some are infected and others aren’t, why some but not others succumb, when the combined health-economic crisis will be over, how bad the damage to the economy will be, and when will it recover.
The fear and uncertainty have been tracked by the stock market, which has exhibited violent swings. March 2020 was the most turbulent month of the stock market in the 124-year history of the Dow Jones Industrials, as measured by daily falls and rises.
While virtually everyone has been affected by the crisis, it would be an overstatement to say that “we’re all in this together.” Amid all the uncertainty, one thing is certain: the poor and vulnerable are going to suffer the most.
Those of us who still have jobs — especially jobs that don’t require us to leave the home — are the lucky ones. We can still count on an income (for now, anyway.) As businesses shutter, jobs have become a scarcer commodity and almost overnight, millions have suddenly lost their main source of livelihood. Since the middle of March, 10 percent of Americans have lost their jobs.
Unprecedented use of the term “unprecedented”
On the economic front, the present pandemic has begun eliciting comparisons not with the global financial crisis of 2008, but with the decade-long Great Depression that afflicted the nation starting in 1929. Policy makers and citizens alike are hoping for a quick recovery.
In the meantime, a shocking number of people face a grim reality as they have been pushed to the unemployment front line. In the three weeks ending on April 9, 16 million people claimed unemployment benefits. The unemployment rate has spiked to an estimated 13 percent, the highest since the Great Depression.
A Pew Research Center survey taken at the end of March found that nearly nine of out 10 Americans said their lives had changed a little and 44% said their lives had changed in a major way as a result of the coronavirus crisis.
These, and many other, massive changes did not gradually set in; they occurred in a matter of weeks.
The people most affected, of course, are those who have succumbed to the virus, and those in hospitals. Then there are the millions of doctors, nurses, EMT workers and thers in the healthcare sector, working on the front lines and putting their lives at risk. These new heroes are the most highly exposed to the dangers of the pandemic.
Unpleasant choices
However, on a structural level, the blow has landed hardest on low-income workers (as usual) and informal workers. Millions of people have lost their jobs in sectors that are collapsing, such as hospitality, retail, travel, manufacturing, house cleaning, childcare, or in sectors which are considered essential businesses and services, where continuing to work means more chances of exposure.
In some of these sectors — groceries, warehouses, delivery services — demand has even surged, and hundreds of thousands of jobs are being created. This is hardly a cause for jubilation, however, because simply leaving your home to go to a workplace puts you at greater risk. The low-paid employees at nursing homes, and in social care, cleaning, etc. naturally are also rewarded with a higher chance of infection.
Those who are low-paid do not have the relative luxury of white-collar workers to stay at home and work remotely. If they quit their jobs, they lose a paycheck. If they stick with it, they may be putting their lives and those of their loved ones at risk. A grim choice, to say the least.
For those in sectors that have just cratered, their source of livelihood has evaporated and it is uncertain for how long, or whether, those jobs will come back. Predictions are that many restaurants and other businesses will close permanently, which does not bode well for the economic recovery after restrictions are lifted and life goes back to normal, or a “new normal,” whatever that will look like.
While the government is spending trillions of dollars to cushion the blow, people working in informal jobs, including undocumented migrants, will not qualify for government assistance. Even the $1,200 in government relief going to the rest is not nearly enough to make up for lost employment.
And so, millions are cast out to sea in a leaky vessel, heading toward the twin catastrophe of Scylla and Charybdis. These are the people who will struggle to survive using the safety nets or their own meagre (and often non-existent) savings to carry them through. They also have fewer resources— such as savings and other assets — to fall back on during hard times.
The global shock
Low-paid, poor households are considered “vulnerable” during normal times. Vulnerable to what? Well, to the catastrophic situation we are in now. This “shock,” a term used by economists to refer to a sudden disruption with negative consequences, is probably already pushing many vulnerable into destitution.
Normally, shocks hit people at an individual level, or because a sector has been hit. Individual shocks might come in the form of an illness or injury, or death of a family breadwinner, which dry up an income stream. Sector level shocks affect an entire industry, such as the shutting down of the coal industry in England under Thatcher’s reforms, or of the textile industry in Nigeria when the country began allowing cheaper imports from China. Shocks also occasionally hit an entire nation, such as Hurricane Maria which devastated Domenica and Puerto Rico in in 2017. But this time, the shock is global, all-encompassing and, yes, unprecedented.
For individual workers, compounding the risks that come with working outside the home are the risks of simply getting to work. Many low-paid workers rely on public or shared transportation. Obviously it is more difficult to practice social distancing in confined spaces, such as subways or buses.
Circumstances are tough enough for the vulnerable who are still healthy. But for those who become ill, the situation gets much worse. Getting sick while just getting by imposes a cost not only to people’s immediate physical health, but is a tax on livelihoods and the wellbeing of dependents.
In the U.S., as in many developing countries, there are three specific areas where the poor slip through holes in social safety nets: paid sick leave, health insurance, and unemployment benefits. Combined, these big holes in the net can, perversely, incentivize people to risk their own wellbeing —and now, in the age of Covid-19, their own lives and those of others.
Lack of paid sick leave
The lack of paid sick leave doesn’t just harm individuals, it threatens the public welfare. According to the Center for Law and Social Policy (CLASP), which highlighted this issue in November 2019 (well before the pandemic hit these shores), over 32 million workers in the U.S. have no paid sick days off, with low wage workers being the least likely to have sick leave. The combination of no of sick leave and low income incentivizes behaviors that run against measures needed to mitigate the risks thrown up by the pandemic. Covid-19 can thus “cost them their livelihoods, as well as their health”.
Without paid sick leave, workers cannot afford to take unpaid time off when they are sick. That means they will continue to work, while exposing others to their illness, with potentially deadly implications. In other words, individual choices, resulting from public policies, end up harming the public good, even as it widens inequality.
Lack of health insurance
In the U.S., many of those same workers who do not have paid sick leave also lack health insurance.
According to the Centers for Disease Control, the number of uninsured Americans fell significantly —from 46.5 million to 27 million — between the time the Affordable Healthcare Act was passed in 2010, and 2016, Obama’s last year in office. Since then, the number of uninsured Americans has been creeping up again. From 2017 to 2018 it increased by 500,000, according to Tolbert et al (2019).
Unexpected healthcare costs threaten to burden already limited household finances. They disincentivize seeking treatment or preventive care. Though some governments provide free or reduced-cost services to its poorest, the coronavirus may now present an additional expense for those ineligible, or who cannot access it.
Lack of unemployment benefits
For the many who work in the informal sector, or as gig workers — think Uber and Lyft drivers — there are no unemployment benefits.
Although governments in some countries are making an effort to support those who have lost their jobs as a result of the pandemic, if you don’t have a contract, you can’t be laid off, and you can’t claim benefits. You just watch your income shrink or disappear.
Safety nets under strain
Meanwhile, NBC News reports that nonprofit community centers, who primarily serve as safety nets for un- and underinsured American citizens and immigrants, are running out of funding as the coronavirus ramps up. Not surprisingly, the poorest are suffering the most, as this USA Today article points out. A drop in donations, volunteers, supplies, and certainty of funding, combined with overall lack of preparedness, is forcing these clinics to reduce their services.
Compounding poverty
Similar to the way in which investors reap compound interest, money begets more money in an upward spiral of wealth accumulation, so does poverty beget more poverty in a downward spiral asset depletion.
Thus, other poverty factors and demographics can compound the economic and health impact of the coronavirus on the poor. For example, the poor tend to live in smaller homes, more cramped environments (think of urban slums and shantytowns) and in larger families. This makes social distancing more difficult, and isolating the sick next to impossible.
Even if more vulnerable and elderly family members are staying home and practicing social distancing, someone who works, not by choice but by necessity — may bring the virus home with them.
Being poor is bad enough, but the coronavirus, and especially the government measures taken to combat and contain it – shutting down businesses, ordering people stay at home — hurt the poor much more than anyone else.
I’ve focused on the situation in the US above. With its weak support for the poor, the US, despite being a rich country, has quite a bit in common with developing countries. A big difference is that the US is home to the world’s global currency. It can print as many dollars as it needs to bail itself out (although a move not entirely devoid of fiscal consequences).
A far-off consolation?
Plagues and pandemics sometimes reorder societies. It may be a meager consolation, but sometimes the reordering benefits the lower classes, leveling the playing field. After the Black Death swept through Europe in the 14th century, killing between 30 and 60 percent of its population, demand for labor jumped, serfs were freed from their masters, and wages increased.
For those who have been alarmed by the relentless rise in inequality in rich countries, the pandemic may be a blessing in disguise, although for many of the poor living through this crisis now it will be too little too late.
This blog post about beneficiaries is built around a series of questions. Why? Because whether you are a program designer, a program implementer or an evaluator, you spend much of your professional life trying to find answers to them.
What policymakers want to know
Policies are generally designed with the aim of allocating
resources or creating opportunities to one or more segments of a given population.
A question policymakers often ask
themselves is: Who will benefit if…?
Addressing issues related to program beneficiaries depends a
lot on how the questions are framed. Typical questions might be: Who will
benefit from a new policy? Who is benefiting from the status quo? What are my
policy options? or What will be the effect of choosing policy A over policy B?
Existing conditions, and who is benefiting from them, are
themselves influenced by prior policy decisions. (And don’t forget that not implementing
a policy change is a policy in itself.)
Policy choices and questions like those above also concern actors
in the foreign aid / development assistance sector. Governments in rich
countries need to be able justify their assistance programs to their taxpayers.
They want to be able to justify their foreign aid spending. One important way they
do this is by demonstrating that their foreign aid program is making a positive
difference in people’s lives.
Policymakers in recipient countries want to exert a positive
impact for some or all of their constituents. And they generally want to be
rewarded for it, for example by getting re-elected, or their bosses getting
re-elected.
Design, deliver and evaluate
The task of program designers is to demonstrate how investments will deliver. How will the money flowing into one end of the foreign aid funnel be transformed into better lives at the other end? This has to be figured out. It is a program logic issue.
The task of program implementers is to deliver on the program’s design, to realize the goals and reach the targets that are set out. Implementers address the question of How do I use the resources I have to produce the results they want? Think of it as a form of foreign aid alchemy. “The base metal” of financing, resources, plans etc. is, if all goes well, turned into a golden opportunity. This is a management issue.
The task of program evaluators is to figure out whether the desired benefits, in fact, came through. Evaluators apply a set of methods to determine What happened? Why, or why not? This is an analytical issue.
When a beneficiary isn’t a beneficiary
Some organizations prefer to avoid using the term
“beneficiary” as it has connotations of passivity. Other terms include client,
customer, end-user, program participants, affected groups or
program-affected-populations. Each has a slightly different connotation. Because
“beneficiary” is a catch-all term, I’ll stick with it in this post.
The case of roads
Let’s use investments in roads as an example. (I am
currently involved in three evaluations of road projects for the Millennium
Challenge Corporation, and so find myself thinking quite a bit about this
issue.) While road networks connect virtually everyone nowadays, roads
deteriorate over time. They need maintenance, resurfacing, and complete
rehabilitation.
Roads facilitate the movement of people and goods. Similar
to roads, canals (mostly for cargo), railways (people and cargo), sewerage
systems (wastewater), pipelines (oil and gas), power lines (electricity) telephone
lines (communication), and broadband cables (information), improve the flow of
things people need and value.
By reducing resistance, these various channels save time and
energy compared to alternatives for moving things or people from point A to
point B. When these channels are cut,
blocked, or destroyed, havoc can occur. Access is impeded. Very quickly, everything
becomes more difficult and costly.
The beneficiary perspective
From an individual’s viewpoint, the impact will depend on, for
example, where that road leads, how often they use it, what they use it for,
and how easy it is to reach. Is there
a paved feeder road, a gravel road, or a path? Is there a mountain or river
that must first be crossed?
Many other questions can be asked. Are you a farmer who
sells produce at a weekly market along the road? Is your village close by and connected to the
road via a well-maintained access road?
Maybe the new road won’t make any difference to you
financially, but it saves you time, and improves your quality of life. It’s
much more pleasant to drive along a smooth asphalt road at 80 km per hour than
a bumpy one with perilous potholes. You might not see money in your pocket, but
you will feel more connected to the outside world if you live near a good road
instead of a bad one.
It’s also possible that you don’t see the effect today, but
will see it years later. If your parent has a stroke five years from now, the
new road could make a big difference in how quickly can you get them to the
hospital.
The evaluator perspective
Now consider the issue from the perspective of a researcher
or evaluator. You’re looking not at individuals but at aggregate effects. These
are less precise, but more useful. You don’t want to collect five hundred
stories of how the improved infrastructure has changed, or not changed, lives.
You want to measure trends and aggregate changes. You want to know the effect
of the new road on the population as whole.
And there are questions about distance: What if an old
road with a rough surface and full of potholes is rebuilt? Are you a
beneficiary if you are living in a town 2 km from the newly rehabilitated road?
What about if you live 5 km or 10 km away?Or only people who use
vehicles, i.e. not pedestrians?
In the past, it was common for road evaluations to pick a
distance on either side of the road in question, draw two imaginary lines, and consider
anyone within this band, the “corridor of influence” was a beneficiary. The distance
of 5 km was considered too wide, and 2 km came to be considered a more
reasonable distance. An alternative, and more conservative way of estimating
road beneficiaries is to only survey people directly using the road. This ignores
indirect beneficiaries, but road users are easier to count, and gives more
confidence in the result.
The data you collect will depend very much on how you draw
the line, who you choose to include among your potential beneficiaries, and
what assumptions you make.
Analytical tools
The previous questions in this blog post are just the tip of
the iceberg. When thinking about how people are affected, we can ask many, many
more. For example, here are questions
about (potential) beneficiaries that evaluators will ask:
What is our population of interest?
How many beneficiaries are there?
Where do they live?
How many are direct / indirect beneficiaries?
How are they benefiting?
By how much are they benefiting, in relative and absolute terms?
Among project-affected persons, how many are benefiting?
Why are some people benefiting and not others?
How are the benefits distributed among income groups? Among different stakeholders?
Are some people taking advantage of the benefits more than others?
Are some people dis-benefiting, i.e. negatively affected as a result of the project?
What methods should we use?
What are the critical information sources?
Who should we to talk to?
How should we talk to them?
How many people should we survey?
How big should the survey sample be?
Where (what population) should the sample be taken from?
What types of groups should be sampled?
What questions should be asked?
Although beyond the scope of this blog post, common
measurement tools for addressing questions about road project beneficiaries road
are origin-destination (O-D) surveys and traffic counts. A range of approaches
and methods exist for each.
Your decision on what approach to take will be influenced by
resources at your disposal – money, time, expertise, etc. Your decision will
also be guided by what others have done before you.
But, perhaps more than anything else, your findings will be
influenced by the questions you ask. Assuming your methods are sound, the findings
may vary, but none will be entirely wrong.
Prices go up. That’s part of life, whether we like it or not. We can just go along with it, reduce our consumption, or…take a stand. For governments providing a service for which they charge, it’s a balancing act. How to raise the price of something without causing hardship or protests, while still covering the cost of providing it?
Last year I was involved in designing a study that, among other things, assessed customer’s willingness to pay for better utility service. This meant asking people all over the country, as part of a household survey, whether they would pay more for better service.
Governments planning to develop or upgrade public services may be interested in knowing to what extent consumers are willing to bear the costs of investment, via higher rates. For example, if water supply service provision is substandard, governments will develop investment plans to improve quality, supply, access, etc. Governments can go to commercial or development banks to access financing up front. However, typically they will seek to recoup at least part of those costs by passing them on to customers via higher tariffs. That was what brought me to the country in question.
The survey would, ideally, help determine what poor households could afford and whether proposed new tariff levels would pose a hardship or not. An array of mitigation measures, including various kinds of subsidies, can be developed for those households deemed to need them. Beyond that, governments also want to know about overall household tolerance for paying more. Will higher tariffs lead to higher non-payment levels? Will they bring people to the streets? Could proposed tariff increases fail to pass? In that case the whole investment strategy would be called into question.
The way a willingness to pay (WTP) study works is that respondent are asked if they would pay more, either a specific amount, or as a percentage of their current water bill. Originally, this method contingentvaluation, was used to estimate whether and how much extra people would pay for an environmental good, such as clean air or water.
There are all kinds of different ways of asking WTP questions. For example, you can ask a yes/no question, ask different households about different amounts and build a demand curve, or use an open-ended approach, asking them to volunteer an amount themselves. Naturally, how you formulate the question will affect the answer. And getting reliable answers is a challenge. Some people hold their cards close to their chest, unwilling to reveal they might pay more. Consciously or unconsciously, they enter bargaining scenario, like at a bazaar, hoping to get the best deal. Others may answer that they’re willing to pay a higher amount than they would like. They may be trying to convince the government to just hurry up and get on with the investment, and let’s worry about the tariffs later (when maybe they won’t go up quite so much).
Almost all WTP methods are quantitative. The idea is to collect data from a sample of households that represent a population of interest (a city, a region, a country). Now, getting back to our survey: about half replied they would not be willing to pay a cent more for water and sanitation improvements. Of the half that did say they’d pay more, the amount was about 5%. Fair enough.
What was interesting, however, was that when we broached the subject of paying more during a focus group, covering questions very similar to our survey, but in an open-ended, more in-depth manner, the share of people saying they’d pay more for better service was very high. Almost everyone said yes. Of course, this is not a scientific comparison – a thousand households vs ten people sitting around a table. But it got me thinking again about the manner in which we as researchers engage with our subjects. How much does the context matter? Focus group discussions, by their nature, foster open dialogue and exchange of ideas. They tend to put the research subjects on a different footing. They are given more agency, they able to share their thoughts and ideas with a moderator who guides the conversation. (Unlike the survey interviewer, the focus group moderator is not trying to get through a long list of questions, looking to get a limited range of responses.)
This got me thinking about a hypothesis that might be worth testing. It goes something like this: asking about people’s willingness to pay as part of a back-and-forth conversation (i.e. focus group) rather than via a survey questionnaire leads to a greater stated willingness to pay. In the context of a conversation people can ask clarifying questions, can explain their reasoning, can describe under what conditions they would be willing to cough up more. Engaging them more as equals, as ‘experts’ so to speak, in the matter of their own consumption habits, leads to a different place. Trust is probably going to be higher when people don’t feel so much like they’re just going to be a data point.
Methodological purists may argue that, ‘well, of course you’re getting different responses, since you’re asking questions in a different way.” My response is – that’s exactly the point. To me, the more relevant question is, to what extent do the research methods mimic real world conditions under which higher tariffs are pushed through?
While it would be certainly more difficult to analyze qualitative research on WTP than quantitative survey data, let us test what the former approach can add. Engaging customers through such an approach, treating them as thoughtful partners with valuable perspective on a public service, rather than only as data points, could be useful in informing tariff strategy. It could yield more information concerning the conditions people would countenance paying more, why and why not, and how to best to engage them on this often difficult topic.
The next time you ask someone to pay more for a service, don’t just take ‘no’…or ‘yes’ for answer.
I’m a big fan of using mixed methods in evaluation. That means combining qualitative and quantitative data, such as statistical data on household energy consumption and interview findings, where people reveal what it’s actually like to live with regular blackouts. This is not just because it’s always interesting to poke around at problems from different angles, but because the resulting analysis is generally much more layered and nuanced. Let me use a case study from a few years ago to illustrate what I’m talking about.
Applying different methods to the same problem is like stepping into the shoes of different blind men around an elephant, all trying to determine the nature of the creature. Instead of standing in front, holding the trunk and guessing it’s some kind of snake, or standing alongside, grabbing a leg and guessing it’s a tree, and so on, you test every dimension by shifting your position. A key advantage in using mixed methods is the ability to triangulate findings. Not only can you compare and cross-check your results – when done well, a more multi-layered, nuanced version of the underlying issues emerges. On the other hand, doing so can also complicate your life: there is always the risk of ending up with findings that don’t make a lot of sense.
Nonetheless, when findings derived from different methods not only highlight different attributes, but point in different directions, that is when the most interesting decision points and nuanced analysis can arise. Assuming the problem does not lie with the methodology or data collection itself (something which should always be checked), evaluators will need to do some probing to figure out why the data points in different directions.
We faced this issue as part of a 2004 World Bank-financed evaluation of the household impacts of electricity sector reforms in Moldova, using the ‘poverty and social impact analysis’ (PSIA) approach. The accompanying steep increase in electricity tariffs was seen by some as hurting the poor, and a reason to roll back reforms.
The data we analyzed showed a marked improvement in electricity consumption among the poorest 20% of households. However, the perception among poor households, gleaned through qualitative methods (focus groups) painted a less than rosy picture. We heard a lot of complaints from them. Based solely on the quantitative evidence, our report might have concluded that all was well, that concerns over hurting the poor with high prices were overblown. Conversely, if we had only used qualitative findings, we might have concluded that the reforms were indeed a negative for the poor.
Is raising electricity tariffs good or bad for the poor?
A bit of background: by 2003, with World Bank assistance, Moldova had privatized two-thirds of its electricity distribution network (the part of the grid which delivers electricity to customers). The aim was to improve sector performance by moving it to a commercial footing. Then, as now, Moldova was one of the poorest countries in Europe. A Spanish operator, Union Fenosa, had won the tender, with a pledge to invest $54 million in upgrading the infrastructure.
Prior to privatization, bills had gone unpaid, operating costs had not been covered, barter payments were common, and the network was severely degraded. Outside of Chisinau, the capital, most people could only count on a few hours of electricity per day. However, a Communist government had recently been elected and, with electricity tariffs rising steadily for years, some policy makers were now arguing that the reforms were having a punitive effect on poor households. There were fears among the donors that the privatization would be reversed, returning the distribution networks to state control. This is what triggered the study.
I was part of a team of Bank staff and consultants tasked with evaluating whether the poor were, in fact worse off now than before the reform. As tariffs increased, were they consuming less electricity? Or were they perhaps cutting back on consumption in other areas? Either scenario would have pointed to a negative welfare effect.
The methods behind the madness
By matching billing data provided by the electricity with data on household expenditures (from the country’s Household Budget Survey), we were able to track consumption patterns over the 5-year period which coincided with tariff rises and privatization. Tariffs had begun climbing prior to privatization (to make the sector more attractive to potential bidders) and continued to afterwards. Over that period, they rose a whopping 300%, in nominal terms which translated into 26% in inflation-adjusted terms. This meant that the cost of electricity rose slightly faster than the average costs of other goods in the household consumption basket. That fact doesn’t provide a sufficient basis on which to base a conclusion about welfare.
With this in mind, we did not just analyze the data, we also talked to the population directly (thereby mixing our methods). A local research firm held 43 focus group discussions and 59 key informant interviews across the country. We basically wanted to hear about the users’, the electricity consumers’, perspective on these changes. Many focus group participants related how badly conditions had become during the, literally, dark years after independence.
“About two years ago a girl was raped, and now I don’t permit them to leave the house in the night,” reported a woman named Anea
“Everybody, children, the elderly, grown-ups, are affected by the darkness in the entrance hall and on the streets. It is very difficult to walk when we return home late in the evening. The roads are in a bad state, full of holes and one can easily fall down and break the neck,” Tamar said.
“In our entrance hall, the bag of an old woman was stolen, together with her pension for one month,” according to Elena
What did the data show? We found that over the period electricity consumption among the poorest 20% had risen by 14.6%, even as electricity tariffs rose more than 300% in nominal terms and 26% in real terms. Of course, people weren’t consuming more electricity because the cost went up; they were consuming more because their incomes were increasing. We also found that among all other households (which we designated the ‘non-poor’), consumption rose by 3.2% (i.e. the poor experienced bigger consumption gains). Perhaps more importantly, there were no more rolling blackouts. From having on average 4 hours of electricity per day, everyone now enjoyed electricity service 24/7. Based on the data, we could confidently say that the poor were not being hurt by privatization with the concomitant tariff increases. (We did not seek to determine whether the electricity reform made the poor better off, a rather more complex and somewhat subjective question.)
Data vs. perceptions
So was the government wrong? Were the poor pleased with the changes? Hardly. Although many people acknowledged that things were better – they now had electricity 24/7 – the overriding message that emerged from focus group discussions was that many people were unsatisfied. They did not feel better off. They certainly did not express undying gratitude for the reforms. They complained about costs going up, about having to save, and about quality issues (e.g. voltage fluctuations). And they were unhappy about what they perceived as Union Fenosa’s excesses. The company had built itself a multi-story headquarters building, bought new company vehicles, sent out electricity bills using colored ink on high quality paper (replacing the cheap brown paper used previously, a Soviet-legacy. To top it off, it was seen to be spending money frivolously by paying for schoolchildren to go on outings to the circus and other measures aimed at burnishing its corporate image. All of this did not seem to impress the average Moldovan. In her view, the company should instead have kept its tariffs lower.
Returning to our quantitative findings in the light of public perceptions, one particular piece of information was of critical importance for setting the context: electricity consumption levels in Moldova at the time of privatization were extremely low. The average household consumed just 50 kWh per month, the equivalent of a couple of light bulbs and a TV. This was close to one third the Eastern European average, and a small fraction of developed country consumption levels. Many people (exactly half, of course) fall below the average increase in electricity consumption, and many had to reduce
According to Vasile: I think that the reform had a positive impact, the regular blackouts prior to the reform practically stopped the activity in many fields. However, there is the price issue.
“During winters I unplug the refrigerator, hence I pay by 35 lei less per month” a participated named Victoria said.
Did these findings invalidate our quantitative findings? I would say no, they were complementary. They helped explain why the government had been hearing mostly negative feedback about the privatization. While we found that the poor were not being hurt financially, it would have been a stretch to say that privatization had been a boon for them. The difference between 50 and 57 kWh per month is not exactly a game changer or cause for rejoicing if you’re a poor household.
This study illustrated the importance of using different methods of inquiry and, conversely, the risks of not doing so. If we had used one method only, we would have arrived at rather different conclusions – one too rosy (based on the consumption data) and one two bleak (based on people’s perceptions). Neither finding was right or wrong. They simply told different sides of the same story, and helped explain two very different but valid perspectives on the complex and charged (no pun intended) issue of privatization.
Well, it’s happened again, to paraphrase the Car Talk guys from NPR: someone has wasted another perfectly good opportunity to do tariff reforms properly.
This time, the Government of Argentina is in the hot seat. The Economist reported that the government recently tried to quadruple the price of gas and raise the price of electricity sixfold. It did so just as autumn set in, when customers heating bills go up anyway. Protests erupted, predictably enough. Reducing subsidies or raising prices is a balancing act requiring
Now, raising the price of anything by 400% or 600% at one go may sound extreme. But Argentina’s utility prices have been at rock bottom for a long time. They are a a legacy of populist measures under the previous Kirchner regimes. In fact, the cost of electricity per kWh is just 1 cent per kWh, compared with 16 cents in Brazil and 12 cents in Chile. The previous governments had kept prices artificially low through massive state subsidies, which ballooned to 12.3% of government spending by 2014. A nice parting gift to the new administration of President Mauricio Macri. This is a precious use of public resources that could be put to far better use elsewhere. The Economist noted that the subsidies were costing the government $16 billion a year.
When a commodity is so cheap, it also tends to be wasted. Who is going to turn the lights off when you will hardly notice the difference in your bill? A knock-on effect is rolling blackouts, as power producers struggle to generate enough supply.
With mounting opposition to the price hikes on August 18, the Supreme Court ordered the increases blocked. According to the Financial Times , the grounds for the decision were that the gas price increase “violated the right to participation by consumers in the form of public hearings in the revision of tariffs.”
And so yet another chapter can be added to the story of how not to raise utility prices. There is a long list of governments failing in this particular endeavor.
What could the Macri government have done differently? Lessons from efforts by other countries to introduce price reforms could have been studied. They boil down to taking some basic measures. A few, but not all of them relate to the Supreme Court’s comments:
Engage in consultations with key stakeholders
Offer some concessions, instead of imposing the changes by fiat
Study the potential impacts, preferably using multiple tools – surveys, focus groups, stakeholder consultations – and doing so in an open, manner.
Time price increases better, so they didn’t kick in right at the time when usage is highest, i.e. with the cold weather season starting
Do a better job of communicating your rationale for raising prices
Introduce measures to mitigate the impact on those most affected
Of course, none of the above would have guaranteed that the price increase would have stuck. But preparing better would have considerably reduced the risk of failure, while also addressing public concerns. The failure to both raise prices and to consult more with consumers may effectively cost the government billions of dollars. That is money which could be spent on a host of other areas the country needs to invest in.
Regardless of what you think of politicians who love to speak in these terms, it is fair to say that the world is full of winners and losers. Government policy may be active, e.g. raising tariffs or taxes to improve its fiscal situation, or it may be passive, e.g. standing by while the forces of globalization and technology sweep jobs from an economy. In either case, some people benefit, some don’t, and some lose out. In fact, just about every benefit comes with a cost, but those costs are not evenly distributed.
Despite great progress in many sectors of the economy, inequality has increased in the US and elsewhere. According to the Pew Research Center, while median real wages have barely budged since 1970, most of the gains since 2000 have gone to the top 10 percent of the population.
How do we know what the impacts will be?
It is extremely difficult to predict the exact impacts of a given policy. Many factors come into play, and there are direct and indirect impacts. However, you can make estimates, and that can be very useful. A big part of my work involves looking at the potential impacts of policy reforms in developing countries. Years ago, the World Bank developed an approach called Poverty and Social Impact Analysis , or PSIA. This is a type of evaluation. It deploys quantitative and qualitative research methods to muck around in people’s financial and economic cupboards (using surveys and focus groups, for example) to try and figure out how they might fare if the government enacts a new policy.
How does this work in practice? I often conduct studies on the effects of price increases on different population groups. Analysis allows us to predict what the effects will be in terms of affordability for different income groups. Quite simply, we assess the winners and losers of a given reform. If electricity rates go up 25%, but you also get more hours of electricity supply per day, what does that mean for you, as a poor household? The risk is that the losers, in the short-term, will be those for whom electricity bills are a serious chunk of their total expenditures.
The agony of asking others to delay their gratification
Of course, reforms typically aim for positive outcomes. But these don’t kick in immediately. In the short-term, most people may experience only costs. The long-term benefits are abstract and perhaps uncertain. This is especially the case if the government has a poor track record of following through on its plans.
The relative costs of a price hike will be higher for those least able to afford the good or service being reformed. Thus, raising water or electricity rates can be a delicate exercise. It can make people quite unhappy, and politicians quite nervous. But those tariffs do need to rise. Otherwise, what will ensure that a utility company can operate sustainably, invest in operations and maintenance, attract external financing, and expand its network?
Tariffs are not boring if you’re poor
If you think utility rates is a boring topic, think again. Such an attitude suggests you are comfortably middle class. Probably your eyes glaze over when you see news reports from some faraway country about the latest unrest over price rises. At most, you may be mildly irritated when your utility bill goes up.
In fact, it is not unusual in some countries that when prices rise, some people pay with their lives. Demonstrations and riots occur on a regular basis. People protest against government attempts to squeeze more money out of people (the demonstrator’s perspective). The argument that it will put a utility on a sustainable footing (the technocrat and economist’s perspective) is not accepted. Especially when the government is perceived as corrupt and unaccountable.
Demonstrations over prices can block policies…and bring down governments
Just last month, violent demonstrations broke out in Cote d’Ivoire against proposed electricity price increases. One person was shot dead by security forces. In Nigeria in 2012 cuts to fuel subsidies led to widespread, violent protests leading to two deaths and many injured. In Bulgaria in 2013 in response to a doubling of electricity rates, six people immolated themselves, and street protests brought down the government. In 2015, Armenians took to the streets to protest proposed electricity price hikes, and dozens were injured. There are many other such cases throughout the world.
The Cochabamba demonstrations in Bolivia, dramatized in the 2010 feature film Even the Rain starring Gael Garcia Bernal, is a great primer on this, although it shows just one side of the story: the poor protesting against the privatization of the city’s water supplier. (It’s also an excellent and evocative film which draws a parallel between oppression by elite outsiders over 500 years of history.) The other side of the story, not covered in the film, concerns the aftermath of the multinational water company’s departure. Sixteen years on, the cost of water has indeed stayed low, but most of the poor still don’t have access to it. In other words, it was Pyrrhic victory for the protesters
Put some effort into finding a way forward
Yet it doesn’t have to be either/or. Yes, tariffs do have to cover costs or the infrastructure will break down and a lot of people will be left without any water at all. But there is more than one way to skin a cat. As a government, you can raise prices – or reduce subsidies, effectively the same thing for the consumer – gradually. You can include protection measures for the poor. You can engage on the problem with people affected. You can be smart about communicating why you have to do this.
In 2003, I was part of a team that studied the impact on the poor of rising electricity rates in Moldova. We found that the poorest 20% of households were not cutting back on electricity use, but were even consuming more. This was because the poverty rate had been coming down, so people in general had more money in their pockets to spend. In another study, in Lebanon, we found that the real (inflation-adjusted) cost of public electricity tariffs had been falling for years, but reliance on expensive private generation has pushed up household electricity expenditures.
The type of approach mentioned above – poverty and social impact analysis – can be usefully applied to all sorts of different reforms, not just utility tariffs. By doing some investigating, and carefully analyzing the data, the road to reforms can be smoothed. Social unrest can be avoided.
Government wins by generating goodwill among the population. It shows cares about them and is trying to work out a solution. And the people win because their needs and circumstances are being taken into account, and painful up-front costs are being reduced.
