Now that we’ve established the context, I feel that we can return to our discussion about multi-stratification. Stratification is employed when you can divide the population into sub-groups which are heterogeneous, disjunctive. They do not overlap. This separation is very clear. It somewhat helps to break a large population or area into smaller, more manageable chunks. Region is a good variable which does this, separating a population into smaller and definitely distinct parts. Regions are based on county composition, for example Tulcea is part of Dobrogea, as a historical region, along with Constanţa. They are the only counties to form Dobrogea. Well, there is one more variable which manages to separate the population into distinct groups. It is easy to infer which one if you remember that there are several settlements, predominantly villages, that are assigned to towns or communes. It is the terms municipality, town or village which provide a difference based on the number of inhabitants and makes them feel proud about themselves. In fact, the variable is “settlement size”, which incorporates the levels rural, large town, medium town, small town. The capital, Bucureşti, is by itself a (historical) region and an independent level as it comprises around 2 million inhabitants. The next largest town is Cluj-Napoca with slightly under 300.000 people. Seeing this enormous difference between Bucureşti and the second largest city in terms of population, it is clear that the capital does deserve its own strata. A small town might have 3000 inhabitants, the same as a village. Yet, assignment to an AU or strata, if we use settlement size, is decided by public/ national authorities based on certain criteria. Which is why the SIRUTA codes matter, as well as how those authorities choose to segment the territory. I will detail below the “settlement size” for various strata. In any way, settlement size strata (or urbanization level, or rural/ urban environment) may be used to meet whatever needs your study has, if you have a database containing all settlements of Romania and the number of inhabitants for each settlement. What do you think, do these strata or subgroups, so diverse due to their AU assignment or number of inhabitants, appear in every region, or can we see some type of towns or communes only in certain regions?

Table 1 - Population distribution by region and settlement size – number of inhabitants*

How to read the table (Region by columns X Settlement size by rows):

Cell B2 states that in Ardeal, in Large urban areas, live close to 600.000 people. Cell G5 shows how many people live in rural Dobrogea. Column H contains total number of inhabitants for each stratum, while row 6 indicates total number of inhabitants for each region.

*Note: keep in mind that the data are quite old, the source being INSSE 2015. My advice is to look at these data as an exercise on how we treat data so as to generate a representative sample

Now let us see what the proportions for each cell in Table 1, population distribution by region & settlement size, are. Let’s look at Table 2, where the % is taken out of the total population, 20 million inhabitants.

Table 2 - Population distribution by region and settlement size - % of total

Bucureşti, column A, has a weight of 9% in total population. The rural areas are home to 46% of our country's inhabitants. The largest urban stratum is Small urban, accounting for 18% of the total population. There are 2 regions with good coverage for this stratum, Moldova and Muntenia. Dobrogea has the fewest inhabitants living in small urban lcoalities.

We now have plenty of information and two variables that can adequately separate Romania’s population. How can I know where my respondent is? Or, to rephrase that, suppose we had 100 field agents/ interviewers (ideally 😉 ), where do we send them, to what settlement, on which street? How many of the 13.000 settlements must we visit? We are discussing a face-to-face study, this being the most complex method. We will address later on what happens for online panels or when using CATI – stratification is kept on those two methods as well, but there are fewer steps to it.

Table 3 – Settlement distribution by region and settlement size – number of settlements*

*Note: Treat these data as an exercise, source INSSE 2015.

We can see that once we reach stratum 4, the number of settlements on each cell increases sharply. Obviously, when designing samples, we will not visit each and every one of the settlements. We will select one sample, but an indefinite number of samples might be generated. The solution is to create a sample based on clusters – groups of inhabitants from a homogenous population, who all share the same traits regarding region and settlement size. Bingo, we have to extract population clusters from each cell from the above table. You might be wondering how many people must/ can this cluster include. Before we answer that question, let us see how the distribution of questionnaires/ respondents looks like for a sample of 1000 by region and settlement size.

Table 4.1. - Respondents spread by region and settlement size, N=1000

Table 4.1 states that we have to recruit 93 respondents in Bucureşti, 126 from rural Muntenia. While we can include 93 people from Bucureşti, making sure to visit all 6 sectors, it is impossible to recruit 126 people from the same village in Muntenia. Looking at Table 3, there are over 2,000 villages (some will be communes, some villages dependent on communes). Selecting just one village out of 2600 means covering only 0.04% of the region’s potential. The sample keeps it representativity if we maintain a good territorial spread (it is rather needed/ desired to cover all counties) and if the methods employed ensure its randomness. As you might have guessed, we are yet at the selecting the respondent stage, this is just the first step, selecting the sampling points (and, implicitly, the settlements) for every stratum/ cell.

This is where the cluster sampling method comes into play - to determine the number of sampling points, meaning settlements, and then selecting the respondents for each point. For a given settlement we might have one or several sampling points, it very much depends on the number of settlements each stratum/ cell contains. For a better understanding, we’ll equate sampling point to address/ start point. At these addresses you’ll send your field agent to begin recruiting, rules in hand!

Let us exemplify for Dobrogea – a region which has only one Large urban settlement (Constanţa), only one Medium urban settlement (Tulcea), and 15 Small urban settlements. It is obvious that we conduct interviews in Constanţa and Tulcea, those being our only options. For Constanţa we have to find 14 respondents. Do we recruit them from a single sampling point, or from several? To ensure a better sample, it’s obvious several are needed. Considering a cluster of 7 participants, then 2 sampling points would be used. Were we to employ a 10 sized cluster, we’d end up with a cluster and almost half, a bit tricky to handle, as it is better to have equally sized clusters. For the Small urban stratum, we’ve established there are 15 settlements, where we have to find 11 participants. We might use either one or two clusters, so I’d rather use 2 clusters in 2 different settlements.

What does, essentially, a x sized cluster mean? It means that, starting with the first address, the field agent/ interviewer employs a random selection rule to select the household and another random selection rule to select the participant from within said household until they reach a number of contacts/ selections equal to the cluster size. (A contact/ selection does not necessarily mean a complete questionnaire/ done interview, but we’ll discuss such matters on a later date).

Table 4.2. - Distribution of sampling points for region and settlement size, for cluster=7 respondents, N=1000

For a sample of 1000 respondents and a size 7 cluster, we will be working with 143 sampling points. Using a size 10 cluster, there would be 100 sampling points – a rather large discrepancy. You are probably considering which approach would be best. A theorist would say that more sampling points is better, meaning a smaller sized cluster, 7 in our experiment, because it ensures better spread, allowing for a higher chance to cover all counties and more settlements. Someone focused on cost optimization (fewer rural settlements in the sample, for lower travel expenses) while maintaining an adequate sample quality would favor a size 10 cluster. We could try a somewhat middle of the road approach with a size 8 cluster, for 125 sampling points. Anyways, for a sample of 1000 respondents I wouldn’t recommend a cluster smaller than 7 or larger than 10.

Conclusion

It is very important to be familiar with the country where you are conducting the survey/ study and understand the way its territory is organized.

Its area, as well as the average density and settlement spread provide valuable insight.

Combining stratified sampling with cluster sampling is ideal for any random/ probabilistic sample, regardless of the sample source. It helps in segmenting/ stratifying a population into smaller groups, more easily managed and contacted.

Stratified sampling and cluster sampling are two of the four types of probabilistic sampling. I suggest we combine them... it might seem weird, yet it really helps with managing random samples.

One thing is certain regarding Romania and managing population data records – there is no possibility to draw a random sample of citizens from a database containing contact information for all Romanian residents, so that you may claim to perform an adequate probabilistic sampling, with each person having an equal chance of being selected in the sample, or at the very least so you could compute the probability of being selected for each individual that gets drawn in the sample. Personally, I had the opportunity to experience a collaboration with D.E.P.A.B.D. (The Directorate for Persons Record and Databases Management) that was responsible for randomly extracting addresses following an algorithm I supplied. I needed a sample of 5000 addresses for Romanian residents aged 50 and above. Even so, I had to design the sample as a multi-stratified cluster based and extract the localities, and specify how many addresses I needed for each locality. The collaboration was somewhat good, notwithstanding the long time it took to complete. What surprised me, though, was when after several weeks of waiting, I finally received the database containing addresses, only to find out that for some rural localities, for which the concept of a street is a foreign one, they were unable to perform the sampling. I panicked... we eventually found a solution to maintain the conditions needed for a probabilistic sample for these administrative units as well, but it delayed us by 2 weeks.

Now, before going into detail on multi-stratification, I wish to highlight some particularities about Romania and the way its managed or how its territory is organized. I will employ already existing data published by several authorities. I also have several data and I noticed there are discrepancies compared to what one might find on INSSE, provided you are patient and process their files. By the way, INSSE’s structuring of the files containing population details at settlement level is severely lacking. I could never figure out why the SIRUTA codes (unique code for each settlement in Romania), managed by an entity which has a responsibility to organize the territorial management of the country, are not found throughout all the INSSE files, the latter preferring to use text documents. If you are lucky enough to find excel documents, you can be sure you will find the same settlement written sometimes with diacritics, sometimes without, and when dealing with rural areas, you will only find data on communes (and not villages). One wonders... which is why I am stocking up on my patience reserves for the data for the census that is just beginning (named December 1^st 2021) and hope that they learned how to create smart files.

A few data about Romania

Population: around 20 mil

Area: 238,397 km2

Population density: 84,4 inh./km²

41 counties

7 historical regions (București, Ardeal, Banat/ Crișana/ Maramureș, Moldova, Muntenia, Oltenia, Dobrogea) or 8 micro-regions (NUTS 2) defined by INSSE, somewhat more balanced (Bucuresti – Ilfov, Nord-Vest, Centru, Nord – Est, Sud – Est, Sud – Muntenia, Sud - Vest Oltenia, Vest).

In 2016 there were 3181 territorial administrative units (a file published by Romanian authorities on Eurostat), called LAUs in European lingo. These AUs, short for administrative units, are meant to manage several settlements. That can mean municipalities, towns, or communes. There are no self-managed villages. Villages, standing at over 10.000 total, are assigned for management to towns or other villages, the latter serving as communes. Among the 3181 LAUs we only find those villages which act as communes. Apart from these, there are also an additional 10 thousand or so villages, pardon my repetition, which are subordinated to a town or a commune.

I would also like to focus your attention on population density. I provided some data above, but without comparing the number with data from other countries one can not say whether Romania is crowded or rather bare.

Below you’ll find a map from Eurostat. Blue areas are low density, people have plenty of available space and live apart, whereas orange areas are denser. As you can see, there’s plenty of space to go around in our country, Romania’s settlements are rather scattered.

Click here for part II.

This week’s question is “What will the Black Friday sales look like this year? Will they reach last year’s level – by far its best performance, an increase of 30%, will they be higher or perhaps lower?”

eCommerce has grown organically throughout 2020 in an accelerated manner

Throughout the year several reports forecasted an increase in the total number of online sales and their cumulated value. Some internet users dared to make their first ever online purchase, others expanded the range of products they bought online and decided it was no longer worthwhile to go to the hypermarket for their basic shopping needs or those that found themselves wanting office items to use at home – laptops, office chairs, printing machines, home & deco. The platforms that offer solutions for online retailers also reported an increase in activity this year – more clients needed an online interface to sell their products, there was an increase in online purchases on the sites that they manage (source: press articles MerchantPro, 2Performant).

INSSE statistics show major increases in online sales starting with April 2020, with a 75.7% growth compared to the same month of 2019, and ending with September 2020 with a 44% increase compared to 2019.

The Romanian Association of Online Shops (ARMO) states that Romanian e-commerce will reach this year € 5.5 billion turnover, a 30% increase compared with last year, but offer no estimation for 2020’s Black Friday.

There have been several sales periods throughout the year with no particular reason or occasion, only as a marketing and sales strategy employed by online retailers to recoup losses from the lockdown period and, perhaps, to maintain notoriety and loyalty on the backdrop of the boost un number of online shops as more and more retailers are adapting to an online environment.

People aged 34 or less and those with a monthly income of at least 3000 lei could be the engine behind the 2020 Black Friday purchases, shows a study carried out by Wisemetry Research between October 30^th and November 4^th 2020.

"It’s certain that the most important products this year will be the same as before (electronics, home appliances, IT&C, home & deco, fashion, beauty), but I expect online retailers to employ a strategy showcasing predominately low value items. And that is a good thing, as 31% of those intending to make a purchase on Black Friday, urban dwellers and internet users, will most likely make an on-the-spot decision – will check the sales only on Black Friday and buy whatever catches their eye. That explains the success of the frying pans eMAG sold in 2019, ending up in the top of all items sold. Large items or those that require a significant investment are sought after by 26% of those intending to make a purchase this Black Friday – “I wanted to buy several products throughout the year, but I waited out for Black Friday on purpose”. Most likely this category will raise the value of the shopping cart. The rest of the potential Black Friday shoppers (43%) start prepping for shopping the day before – they think about what exactly they need and then start searching. Will it be products that they need, or products that require replacing?” says Violeta Bahaciu, Senior Research Consultant at Wisemetry Research, based on the results of the study carried out by Wisemetry Research.

“I wouldn't be surprised if that special trip, which you have dreamed of for years and are ready to gift to yourself as a well-deserved reward at least once in a lifetime ends up being bought, restrictions and financial uncertainty notwithstanding. Maybe not this Black Friday, but travel companies should not neglect them from their strategy for 2020. There will be a lot of spontaneous decision making. And since saving will prove trying for the following 12 months, who knows?” continues Violeta Bahaciu.

The great performance of the 2019 Black Friday as compared to the 2018 one is also due to the increased wages. Today, only the retirees enjoy higher purchasing power as compared to 2019.

The study conducted by Wisemetry Research shows a higher intent for saving – 42% of urban dwellers, internet users, state they wish to increase the amount of money they save during the following year.

INSSE does not show any drop in the average net income but reports an increase of 1.1 percentage points in unemployment for September 2020 compared to September 2019. Data also show which industries were or are impacted by the pandemic crisis (hospitality, tourism, fuel, and retail). “Uncertainty is high. The only category who experienced an increase in their income for the second half of the year were the retirees, yet most of them are not internet users, nor did they manage to save any money ... they are getting ready for winter. I expect, however, an increase in value for Black Friday of 10-15% as compared to 2019” concludes Violeta Bahaciu.

The root of this article was a simple question and resulted in a small quest for providing a straight answer. What should we change in our schools to get better results at PISA*?

In 2018, OECD conducted TALIS - Teaching and Learning International Survey. This survey is set to help countries face diverse challenges in their educational system. It is not focused on students, but on teachers and school leaders and asks them about working conditions and environments at their schools. It interviewed around 260,000 teachers in 15,000 schools across 48 countries.

Putting these two studies together, we compared Romania’s results at TALIS with the averages of the PISA top scoring countries and the average of all TALIS participant countries.

What are Romanian teachers and school leaders saying about their workplaces? On one hand both report less satisfaction with their salaries and employment terms, especially when compared to staff from the PISA highest scoring countries. On the other hand, teachers in Romania are less willing to change to another school/ workplace.

Team activities and collaboration in Romanian schools are below the average of the participating countries and the gap is even higher when compared with the top PISA countries. And we are referring here to collaborative professional monthly learning and team teaching, responsibilities concerning school policies, instruction and curriculum, induction activities, and mentorship for novice teachers.

Romanian teachers consider themselves ready to teach in a mixed ability setting and well prepared for random student behavior and classroom management. However, we have fewer teachers engaged in teaching students with special needs and the school principals report a shortage of teachers with competence in this area. Supporting this is the fact that our schools integrate fewer students with special needs. On top of the issue regarding discrimination in access to education for persons with disabilities, we generate fewer opportunities for our kids to learn compassion, to support each other, to foster inclusion, to collaborate effectively with others, and to recognize the value of variety in terms of abilities in a team, rather than competition.

Our school leaders are younger (46 y.o versus 51 y.o the average of all countries in the survey) and have less experience in their current job (7 years versus 9 years the average of participating countries).

Three times more school leaders from Romania are reporting a shortage or inadequacy of digital technology for instruction, while our teachers show openness to integrating technology into their classrooms. From the 78 countries participating in the PISA test in 2018, only 9 countries applied tests on paper and one of them was Romania.

Concluding, the major aspects that set Romania apart from top PISA countries, when looking solely to teachers and school leaders working conditions and learning environments are:

• A salary level that does not meet teachers’ expectations.
• Lack of collaborative professional learning and team teaching.
• Teachers are not involved or consulted in school policies, instruction, and curriculum.
• Lack of induction activities and mentors for novice teachers.
• School leaders unsatisfied with their salaries, younger and less experienced in school management.
• More schools in Romania need finance to purchase technology.
• Fewer teachers are trained to teach special needs kids, which could lead to lack of inclusion and discrimination.

Teachers hold the future. The study shows that Romania’s poor performance in standardize testing could surely be attributed to lack of financial investment, but we should also look into ways of supporting collaboration in schools, sharing of knowledge, training the teachers in order to stimulate and provide space for inclusion, nurture compassion, and collaboration to the detriment of competition.

Sources:

OECD’s country reports during 2019 and March 2020: https://www.oecd.org/pisa/aboutpisa/

* PISA is the Program for International Student Assessment and measures the school performance of 15 years old students in terms of reading, math, and science knowledge. This huge study is conducted and managed, regularly, once in 3 years, by the Organization for Economic Co-operation and Development – OECD. At PISA, our country is placed 49th next to our close neighbors: Bulgaria and Moldova and the results are not improving from one wave of the study to another. On top of the list, we find China, Singapore, Estonia, Japan, South Korea, Canada, Finland, Poland, Ireland, United Kingdom, Slovenia.

OECD’s TALIS - Teaching and Learning International Survey: http://www.oecd.org/education/talis/

The 4-day work week could be a solution to improving work productivity while also reducing office crowding in the post-pandemic Romanian workforce landscape. 

Experiments undertaken in several countries support this argument, results showing a boost in work productivity coupled with other benefits: improved traffic congestion and less crowdedness in public transportation, reduced carbon footprint, reported higher work satisfaction and quality of life among employees.

The idea of cutting work time to boost productivity is not new.

Going as back as 1930 economist John Maynard Keynes posited that in a hundred years people would see a dramatic increase in their leisure time, as the working hours would see a significant cut. Prior to this, Ford was a pioneer of reducing the employees’ working hours, implementing the five-day work week (as opposed to the standard six-day), which yielded a positive outcome in terms of productivity. Recently, in January 2020, Finland’s Prime Minister, Sanna Marin, floated the prospect of implementing a four-day work week, in an effort to combat work-related stress and encourage employees to spend more time with their families. 

Experiments show that a decrease in work hours could bring many benefits to both employees and companies.

In 2015 Sweden implemented the 6-hour work day as part of a pilot study in a nursing home in Gothenburg, one of its largest cities. The study concluded that workers who benefited from the reduced work hours reported less work-related stress, were more productive (increased the number of activities organized for the patients) and overall felt healthier.

Perpetual Guardian, a company in New Zeeland ran the 4-day work week experiment for 2 months in 2018, with results showing a steady performance among employees, who were motivated to optimize their work day by reducing down-time (taking long breaks, engaging in distractions). Additionally, workers reported spending more time with their families, finding more time for their hobbies and returning to work more energized after the long weekend.

In August 2019 Microsoft Japan experimented with the 4-day work week, granting 2,300 employees one extra day off per week. The results showed a 40% productivity boost and higher satisfaction among employees. Additionally, the company managed to reduce energy costs and print fewer pages of paper, thus cutting down its carbon footprint.

Romania reports a higher number of weekly work hours but lower productivity compared to other EU countries.

Compared to most other EU countries, Romania reports a lower labour productivity per hour worked. However, Romania also reports a higher number of weekly hours worked per person employed: 39,8 hours compared to the EU27 average of 37,1 hours (data available for the year 2019).

Data source 1: Eurostat[1]

Data source 2: Eurostat

Figures suggest the need to come up with ways of improving productivity among Romanian employees, while also supporting a good work-personal life balance.

At the beginning of 2019 a country-wide lockdown that isolated people in their homes opened the door towards flexible working hours for Romanian employees. People needed to find ways of juggling work obligations with caring for their children, or helping them with school work. This unintentional experiment is likely to have significant consequences over the way people view their work day and their expectations regarding working hours.

Towards a better work-life balance for Romanian employees.

Several companies in Romania have already announced their plans of extending the work from home policy beyond the year 2020. A large Romanian online retailer reported higher employee productivity during the work-from-home period, although some experts have argued that the apparent productivity boost is due to people actually working more hours as the time spent commuting to and from work and the lunch breaks have turned into time spent working.

The question thus remains: Could cutting down on working hours for employees be a viable solution to increase productivity, while also reducing crowding in offices and public transport and encourage people to spend more time with their families, cultivate hobbies and learn new skills? While likely not a “one size fits all” solution, what better time than now to test it?

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[1] Labour productivity per hour worked is calculated as real output per unit of labour input (measured by the total number of hours worked). Measuring labour productivity per hour worked provides a better picture of productivity developments in the economy than labour productivity per person employed, as it eliminates differences in the full time/part time composition of the workforce across countries and years.

Basic figures are expressed in PPS, i.e. a common currency that eliminates the differences in price levels between countries allowing meaningful volume comparisons of GDP between countries.

The question on everyone’s minds is what to expect from a financial standpoint in 2021. Without a doubt, the COVID-19 pandemic threw off all the economic forecasting announced at the beginning of 2020, as the impact of the measures taken to limit the spread of the virus has yet to be fully understood or evaluated.

Recession seems to be unavoidable; globally, the Word Bank forecasts an economic contraction of 7,7% in 2020 and a very modest recovery (1,6%) by the end of 2021, while also cautioning that the situation remains highly volatile. For Romania, Word Bank anticipates that the negative impact of the COVID-19 pandemic will be significant, announcing a -5,7% real GDP growth for the year 2020 (a 9,1 percentage point difference from the January 2020 projections).

According to the European Commission, Romania should expect an economic contraction of 6,0% in 2020 and an inflation of 2,5%. Weighing heavily on the economic prospective is the severity of a potential second wave of COVID-19 infections, and the authorities’ ability to manage investors’ concerns, given the pre-pandemic already fragile confidence due to a perceived fiscal indiscipline. The European Commission expects that the Romanian economy will make a 4% rebound in 2021.

The International Monetary Fund (IMF) projects a global economic decline of 4,9% in 2020, and expects that the recovery will be gradual. IMF experts weigh in that the sharp drop in consumption is due to the unique set of circumstances caused by the pandemic: volunteer social distancing, lockdowns imposed by authorities, drops in income and increased consumer anxiety. IMF also reports that even in countries where lockdown restrictions have been lifted, mobility data from cell phone tracking suggest that retail traffic and activity remain low. For Romania, the International Monetary Fund projects a 2020 GDP downturn of 5,0% and an inflation rate of 2,2%.

The business environment remains rather pessimistic; a survey conducted by PwC indicates that over half of interviewed entrepreneurs from Central and Eastern Europe expect revenue declines in the coming year, with liquidity being a main concern.

On top of the economic downturn caused by the pandemic, the National Bank of Romania also warns that the 40% increase of the pension point is not sustainable and could lead to a national budget deficit of 11%. According to Erste, the 2021 economic outlook will rest heavily on the fiscal policy stance after the December 2020 general elections and the measures taken to manage the budget deficit and avoid rating downgrades.