Conjoncture

Poland: Economic growth under scrutiny

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An example of successful economic transition, Poland still enjoys fairly favourable prospects despite the expected slowing of growth  
against a background of less favourable international conditions. Over the medium to long term, there are factors that will weigh on  
potential growth and weaken a Polish economic model based on competitiveness and low labour costs. The first section of this article  
analyses the impact of institutions on productivity, which is a major determinant of the differences in standard of living between  
countries, as illustrated through the example of Poland. The second section examines the question of Poland’s estimated medium-term  
potential growth, after an analysis of its pathway since the 1990s.  
Since the beginning of the 1990s, Poland has conducted a policy of the relationship between economic growth and the institutional  
1
economic liberalisation, which, combined with institutional reforms and environment shows that there is a strong link between the latter and  
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political stability, has generated uninterrupted economic growth since productivity .  
1
992, at an average annual rate of 4.2%. According to the World Bank’s  
The quality and stability of institutions are key to the confidence of  
economic agents: encouraging private investment, making an economy  
more attractive to foreign investors, boosting entrepreneurship and  
innovation, optimising the allocation of resources and factors of  
production and thus, in the final analysis, supporting economic growth.  
classification, Poland is an example of a successful transition from a  
low- to medium-income planned economy (USD 6,600 per capita in  
purchasing power parity terms, ranking 64th in the world according to  
the IMF, in 1992) to a market economy highly integrated within the  
European Union (EU) and global value chains and, since 2009,  
th  
classified as high-income (USD 32,000 per capita in 2018, ranking 45 ).  
GDP growth  
Per capita income in purchasing parity terms is now close to 70% of the  
EU-15 average, demonstrating the real convergence between Poland  
and its European partners. From a low level in the early 1990s, income  
inequalities expanded rapidly in the first phase of the transition, before  
narrowing slowly over the past fifteen years. Poland therefore seems to  
have avoided the ‘middle income trap’, in contrast with countries such  
as Argentina, Brazil, Mexico, Turkey and even Romania, which are still  
classified as “Upper middle income” economies.  
%
8
6
4
2
0
-
2
In its first section, this article will analyse the link between institutions  
and productivity, using an efficient frontier model, drawing lessons for  
the particular case of Poland. The second section will present an  
analysis of Polish growth in supply terms from the beginning of its  
transition to a market economy, and will discuss the constraints on  
medium-term potential growth incorporating, in particular, the link  
between institutions and productivity.  
-4  
Poland  
-
-
6
8
Advanced Economies  
Emerging & Developing Europe  
-
10  
1
980 1984 1988 1992 1996 2000 2004 2008 2012 2016  
Chart 1  
Source: IMF  
Poland is relatively well placed in major international rankings of  
rd  
governance and the business environment: 33 out of 190 countries in  
th  
the World Bank’s 2019 Ease of Doing Business listing; 37 of 135  
countries in the WEF Global Competitiveness Index 4.0 2018 edition;  
th  
3
6 of 180 in Transparency International’s perceived corruption index.  
The breakdown of growth in supply terms often reveals differences in However, despite the supervisory role of the EU, the World Bank’s  
productivity that are more significant in explaining the differences in governance indicators and the ‘Institutions’ component of the WEF-GCI  
standards of living between countries than are the accumulation of have deteriorated during the recent years.  
factors of production (capital and labour). Empirical research examining  
2
For example, Barro (1991), covering 98 countries from 1960 to 1985, showed  
1
Adopting Tiffin’s definition (2006), the notion of ‘institutions’ refers in general  
terms to the formal and informal constraints and incentives that structure the  
individual’s capacity to act in a manner that is productive and cooperative.  
Typically, an institutional framework favourable to the market will be founded on  
the rule of law, respect for property rights, legally binding contracts, impartial  
and transparent government and so on.  
a positive relationship between growth rates and political stability. Mauro (1995)  
concluded that the three indicators of corruption, red tape and political instability  
had a significant negative relationship with productivity and investment. Lastly,  
Sekkat and Méon (2004) showed that the quality of institutions (tackling  
corruption and the effectiveness of government) favoured foreign direct  
investment (FDI).  
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GDP per capita in purchasing power parity terms (US$)  
Ease of Doing Business Indicators  
60000  
50000  
40000  
30000  
20000  
10000  
0
Ranking out of 190 countries  
Advanced Economies  
Emerging & Developing Europe  
Poland  
140  
121  
1
1
20  
00  
80  
69  
58  
57  
53  
60  
40  
41  
33  
32  
4
2
0
0
0
25  
1
1
980 1984 1988 1992 1996 2000 2004 2008 2012 2016  
Chart 2  
Source: IMF  
Chart 5  
Source: World Bank  
GDP per capita (% of EU-15 average)  
Real wages and productivity  
GDP per capita, % of EU-15 (2018)  
00%  
Real wage growth in manufacturing sector  
Manufacturing productivity growth  
y/y, % change  
20  
1
90%  
80%  
70%  
60%  
50%  
40%  
30%  
20%  
10%  
Market exchange rates  
PPP  
15  
10  
5
0
-5  
10  
15  
-
-
0
%
Czech  
Slovakia  
Poland  
Hungary Romania Bulgaria  
2
009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019  
Republic  
Chart 3  
Source: European Commision, BNP Paribas  
Chart 6  
Source: GUS  
Governance indicators  
The efficiency frontier  
Percentile rank out of 209 countries  
Y/L  
Control of corruption  
1
00  
Frontier  
Government effectiveness  
Political stability & absence of violence & terrorism  
Regulatory quality  
Rule of law  
Voice & accountability  
95  
90  
85  
80  
75  
70  
65  
60  
55  
50  
y optimal  
Optimal production  
yi  
Observed production  
K/L  
ki  
1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016  
Chart 4  
Source : World Bank  
Chart 7  
Source: BNP Paribas  
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Stochastic frontier analysis (SFA)  
The stochastic frontier model was introduced by Aigner et al (1977) and Meeusen and Van Den Broeck (1977). Battese and Coelli (1995) used this type of  
model with panel data, in which inefficiency is expressed as a function of explanatory variables. The SFA approach was used by Adkins et al (2002) to  
measure the link between the quality of institutions and efficiency.  
The idea of SFA is to add to the standard regression model including a random component , a component of technical inefficiency , also random.  
Standard model:  = 푓ꢂ푥, 훽ꢃ + 푣  
Stochastic model:  = 푓ꢂ푥, 훽ꢃ + 푣 − 푢  
For panel data, the level of production for a country  at date  can be expressed as:  
, = 푓(푥 , 훽)푒푥푝(푣 ) ∗ 푒푥푝ꢂ−푢 ꢃꢂ 1)  
푖,푡  
푖,푡  
푖,푡  
For a Cobb-Douglas log-linear function, (1) can be expressed as:  
푙푛ꢂ푌/퐿, = 훽 + 훽 푙푛ꢂ퐾/퐿ꢃ + 훽 푇푟푒푛푑 + 푣,  푢,푡  
(2)  
0
푖,푡  
with 푌/퐿, 퐾/퐿 respectively representing output per worker and capital per worker. 푇푟푒푛푑 denotes technical progress.  
2
, is a random variable which is assumed to be independently and identically distributed 푁ꢂꢄ, 휎 ꢃ.  
 denotes the technical inefficiency of production, a non-negative random variable distributed independently of  ;  is assumed to be independently  
푖,푡  
푖,푡  
푖,푡  
2
distributed as truncation at zero of the normal distribution with mean  = 훿 푧, z and variance  .  
Technical inefficiency is specified as:  
, = 훿푧, + 훿푂퐺, + 푤,푡  
(3)  
Where , is the principal component of governance indicators.  is the vector of its estimated parameter, which we expect to have a negative sign. OGi,t is the  
output gap which allows to control cyclical variations. , is a residual term  
We define technical efficiency (TE) as:  
푇퐸, = 푦  
표푏푠푒푟푣푒푑 푓(푥 , 훽)푒푥푝ꢂ푣ꢃ ∗ expꢂ−푢 ꢃ  
=
)
푖,푡  
푖,푡  
푖,푡  
= 푒푥푝(−훿푧,  훿푂퐺,  푤,ꢀ  
,표푝ꢀꢁ푚푎푙  
푓(푥 , 훽)푒푥푝ꢂ푣 ꢃ  
푖,푡 푖,푡  
The conditional expectation of 푇퐸, is given in equation (9) (see Appendix) which can be used to estimate the level of technical efficiency for each country  at  
date . 푇퐸, is between 0 and 1, where 1 indicates a fully efficient country.  
2
2
To estimate parameters ꢂ훽, 훿, 훾 , 휎 and  ꢃfor equations (2) and (3), we use the  
maximum likelihood estimator (see Appendix). The likelihood function is expressed as a  
2
2
2
function of the total error ( = 휎 + 휎 ), and the share of the variance in technical  
Estimated stochastic production frontier (SFA)  
2
2
inefficiency , in total variance, or  = 휎 /휎 with 0<  <1. The closer  is to 1, the  
more the deviations around the frontier are attributed to the inefficiency variable.  
Estimate  
Std.Error  
Pr(>|z|)  
The model uses a panel of 51 countries over the 1996 to 2017. GDP ꢂ푌ꢃ, the capital  
stock ꢂ퐾ꢃ, the labour ꢂ퐿ꢃ and the output gap are provided from the Penn World Table,  
WEO and AMECO base ; the governance indicators, which constitute the principal  
component, are provided from the World Bank and have been published since 1996  
Frontier  
Intercept)  
(
10.34  
0.67  
0.02  
0.058  
0.045  
0.001  
< 2.2e-16 ***  
< 2.2e-16 ***  
< 2.2e-16 ***  
Log (K/L)  
Trend  
(political stability, government effectiveness, regulatory quality, rule of law and control of  
corruption).  
Inefficiency  
(
Intercept)  
0.37  
-0.86  
0.01  
0.86  
0.046 1.638e-15 ***  
0.039 < 2.2e-16 ***  
The results of our model’s estimates are presented in the table 1. The coefficients of the  
production equation are broadly in line with expectations, with the elasticity of production  
per capita equal to 0.67 and trend of 2% per year of technical progress. The coefficients  
of the inefficiency equation are significant, and their signs are as expected. A negative  
value indicates that an improvement in the institutional variables used is associated with a  
reduction in inefficiency. The significance of the gamma value ꢂ훾ꢃ indicates that  
governance indicators are an important determinant of the production function and the  
stochastic specification is appropriate.  being very close to 1 in all the equations, we can  
conclude that it has a substantial explanatory power for the inefficiency variables of  
deviations around the efficient frontier.  
PCA  
OG  
0.006 0.396722  
0.017 < 2.2e-16 ***  
gamma ()  
PCA: principal component of governance indicators  
** significant at 5%  
*
Table 1  
Source : BNP Paribas  
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Production frontier (2017)  
Our goal here is to examine the impact of institutional quality, for which  
the World Bank indicators are considered as the best proxy, on the  
productivity of nations, and in particular Poland.  
logarithmic scale  
Developed countries  
NOR  
1
3
2
Poland  
1
IRL  
Productivity differences between countries are theoretically explained  
by two factors: technology and technical efficiency. Technology is  
defined here as all the knowledge available to local producers. This  
concept is broader than the technologies actually used and can vary  
substantially from one country to the next, particularly in the context of  
the Cold War and the countries in transition during the 1990s. Efficiency  
corresponds to the technical relationship that allows maximal output for  
a given level of factors of production, independently of demand and  
prices. According to Tiffin (2006), the rapid dissemination of techniques  
and knowledge around the world limits the explanatory power of  
technology for the productivity differences between rich and poor  
countries. Under this hypothesis, which has become increasingly less  
restrictive since the collapse of the Soviet bloc and the acceleration of  
globalisation, analysis of technical efficiency has come to play a central  
role.  
KOR  
CHL  
JPNRUS  
HR VR O TU UR  
SVN  
MEXCZE  
EGY  
BGR  
UKR BRA  
PER  
11  
SVK PRT  
TUN  
HUN DZA  
ZAF  
PHLTHA  
COL  
IDN  
1
0
9
8
MAR  
NGA  
VNM  
logarithmic scale  
12 14  
0
2
4
6
8
10  
Capital per worker (K/L)  
Chart 8  
Source: Penn World Table, World Bank, BNP Paribas calculations  
Country transitions 1996-2017  
logarithmic scale  
1
3
To measure technical efficiency by country and its relationship to the  
quality of institutions, we have adopted a stochastic frontier analysis  
12  
2017  
996  
1
1
1
0
9
8
(
see box). This econometric technique is particularly well suited to  
HUN  
POL  
UKR  
situations where economic agents act sub-optimally. It is applied to a  
standard production function, enhanced by a technical efficiency term  
RUS  
1
3
plus a trend which traditionally reflects total factor productivity (TFP) .  
Chart 7 represents the notion of an efficient frontier, which indicates the  
optimal production level for each combination of capital and labour  
production factors. Observed production is then expressed as optimal  
production multiplied by a technical efficiency rate (TE) of between 0  
logarithmic scale  
12 14  
0
2
4
6
8
10  
Capital per worker (K/L)  
Source: Penn World Table, World Bank, BNP Paribas calculations  
(
completely inefficient) and 1 (completely efficient).  
Chart 9  
Governance and technical efficiency indicators  
1
9
8
70%  
60%  
5
4
3
2
1
00%  
0%  
0%  
Poland  
Norway  
Singapore  
Netherlands  
France  
Cyprus  
Czech Republic  
Germany  
The results of the model’s estimates for a panel of 51 developed and  
emerging economies over the period from 1996 to 2017 (see Box) show  
that an improvement in the institutional variables used (i.e. the World  
Bank’s five governance indicators) is associated with a reduction in  
inefficiency and thus reduce the distance from the efficient frontier.  
Chart 10 illustrates the strong positive relationship between the quality  
of institutions and efficiency.  
Italy  
Croatia  
Romania Slovakia  
Bulgaria  
Turkey  
HungarySlovenia  
Portugal  
0%  
0%  
0%  
0%  
0%  
Algeria  
Russia  
Egypt  
Ukraine  
Morocco  
0%  
-
2.0  
-1.0  
Main component of governance indicators  
Source: Penn World Table, World Bank, BNP Paribas calculations  
0.0  
1.0  
2.0  
Chart 10  
3
The breakdown of growth in terms of supply based on the standard analysis of  
the production function draws on the Solow model (1956). It provides an  
estimate of the contributions to growth from the factors of production (capital  
and labour) and the development of total factor productivity (TFP or the “Solow  
residual”). TFP is an unobserved variable. It is defined as the technical progress  
resulting from the degree of efficiency in the allocation and combination of  
factors of production, the quality of infrastructure and human capital, and R&D  
investment (this investment is, in part at least, included in the stock of capital),  
to which the institutional framework and business environment make significant  
contributions.  
According to our estimates (see Box) the average technical efficiency  
rate (TE) of the eight economies of Central and Eastern Europe in our  
sample increased from 45% to 50% between 1996 and 2017. Over the  
same period, the average TE for the whole of our panel of countries  
remained stable, at around 62%, and that of the reference group of  
developed economies stayed above 80%. The countries in transition  
are a very mixed group. From 1996, the Czech Republic and Hungary  
had TEs of 69% and 68%. These have been falling in recent years,  
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particularly in Hungary (from 65% in 2014 to 60% in 2017). Conversely, These results bear out the following stylised facts. Unproductive activity,  
Ukraine stands out for its very low, albeit rising, TE of 24% in 2017, the production of goods not matched to demand, under-use of  
from 17% in 1996.  
resources and poor allocation of factors between sectors were all  
sources of inefficiency in planned economies. Economic openness and  
the introduction of institutions compatible with the operation of a market  
economy have contributed to the improvement in technical efficiency in  
countries in transition since the 1990s. According Schiffbauer and  
Varela, “the progressive integration into the EU bloc boosted growth  
and productivity because of three key factors: (i) increased openness to  
trade, investment and talent, (ii) increased domestic competition, and  
regulatory harmonization with the EU, and (iii) increased certainty in  
reforms, through a commitment to EU institutions.”  
Efficiency estimates (TE)  
Average  
1
996  
2017  
1996-2017  
United States  
Germany  
92%  
86%  
84%  
85%  
80%  
87%  
69%  
87%  
80%  
79%  
74%  
72%  
72%  
66%  
84%  
83%  
France  
United Kingdom  
Spain  
However, our estimates seem to suggest that the technical efficiency  
rate for Poland and its central and eastern European neighbours is  
capped at around 60%. The ability of these countries to catch up with  
the reference group of the most advanced economies is now a major  
challenge for the next decades.  
76%  
79%  
65%  
Italy  
Czech Republic  
World (51 countries)  
61%  
61%  
62%  
Hungary  
Poland  
Slovenia  
Slovakia  
Romania  
Portugal  
Russia  
68%  
50%  
67%  
54%  
26%  
60%  
31%  
26%  
17%  
60%  
59%  
58%  
56%  
53%  
50%  
43%  
36%  
24%  
67%  
55%  
63%  
57%  
36%  
59%  
39%  
32%  
21%  
We set out here the results of our breakdown of growth into factors of  
production (capital and labour) and changes in total factor productivity  
4
(
TFP) between 1996 and 2018 . We then use this classical analysis  
framework to estimate potential Polish growth through to 2025.  
Bulgaria  
Ukraine  
Table 2  
Source: BNP Paribas calculations  
Between 1996 and 2018, 61% of growth came from the accumulation of  
capital and 34% from TFP, the remainder coming from an increase in  
the labour factor. These results are broadly in line with those of  
Schiffbauer and Varela (2019) for the period from 2000 to 2014.  
Estimated technical efficiency rates for Poland  
To borrow Paul Krugman’s phrase, the “perspiration” behind growth, the  
accumulation of factors of production, came almost exclusively from  
physical capital. Alongside private domestic and foreign investment,  
public investment benefited from European co-financing, particularly in  
infrastructure projects, as Poland has been the leading recipient of  
European structural funds. Meanwhile, the “inspiration”, a reflection of  
technical progress, also made a substantial contribution to growth,  
driven in particularly by improvements in the institutional framework,  
business environment and human capital.  
65%  
60%  
55%  
50%  
45%  
4
To estimate TFP we have used a standard Cobb-Douglas function:  
t
t
t
t
1
996  
1999  
2002  
2005  
2008  
2011  
2014  
2017  
Based on this equation, and under certain conditions (constant returns to scale,  
perfect competition), GDP growth can be broken down as follows:  
Chart 11  
Source: BNP Paribas calculations  
Y  A  
K  
L  
L
  
K  (1)  
Y
A
For Poland, the picture is positive for both the level and trend in its TE.  
From around 50% in 1996, the TE has reached 59% in 2017 though it  
has decreased from its peak of 63% in 2012. Chart 9 shows the  
progress made by the country which moved towards the efficient frontier  
between 1996 and 2017.  
Where Y represents real GDP, A total factor productivity, K the stock of  
physical capital calculated using the perpetual inventory method and L the  
workforce adjusted to reflect the quality of human capital based on the average  
number of years of education. The coefficient  , the share of capital in  
production, is normalised at 0.3.  
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Breakdown of growth and potential GDP  
slowing of TFP growth reflected a slowing of technical progress that  
began shortly before the international financial crisis against a  
background of diminishing effects from previous structural reforms, the  
slowing of innovation at the “technological frontier”, along with, perhaps,  
the ageing of the population.  
%
Labor contrib  
Capital contrib  
GDP Growth  
5
0
.1  
.2  
TFP  
4
3
2
1
0
1
0
.1  
.4  
0.2  
2
1
Lastly, between 2014 and 2018, Poland at first saw GDP growth in line  
with its long-term average of 4%, followed by stronger growth, of 5% in  
0.5  
1.8  
0.6  
2
.5  
.4  
1.5  
2.0  
1.4  
1.4  
3
.9  
2
017 and 2018. The slower rate of capital accumulation, whose  
2.4  
2.5  
contribution to growth has slowly fallen from 3.9 points per year  
between 1996 and 2002 to 1.4 points between 2014 and 2018, was  
offset by a fresh acceleration in growth of TFP. Over this most recent  
period, efficiency gains thus returned to their level of contribution to  
growth seen before the crisis, estimated at 2.5 points per year.  
1
.8  
1
1.5  
0.5  
0.2  
.0  
0
-0.3  
-
-
96-02 03-08 09-13 14-18 96-18 19-25 (L) 19-25(M) 19-25(H)  
Chart 12  
Source: AMECO, World Bank,BNP Paribas  
At the same time, demographic constraints have limited growth in the  
active population and employment: the fertility rate has fallen  
Over and above the downturn in the global economy, there are some  
structural factors that will hold back Poland’s potential growth over the  
(
1.4 children per woman in 2018, from 2 in 1990), the migratory balance  
5
medium to long term. With a central scenario estimating potential  
is structurally negative, the natural balance (births less deaths) has  
been negative since 2013, the population is ageing (17% were aged  
over 65 in 2018, from 9% in 1990) and the activity rate is below the  
European average (70% compared to 74% in the EU in 2018 according  
to Eurostat), particularly amongst women.  
growth of 2.9% through to 2025, we have a low-range estimate of 2.4%  
and a high-range figure of 3.4% (Chart 12). Even in the most favourable  
scenario, growth will be below the trend line of the last three decades.  
This said, even in the most pessimistic scenario, growth remains  
compatible with the already advanced stage of the country’s socio-  
economic development.  
In the absence of any significant increase in the quantity of labour, its  
quality has improved through better standards of education and skills in  
the labour force that has accompanied the increasing sophistication of  
production and exports. The share of the active population (aged 15 to  
Inherited from the period of economic transition, Poland’s economic  
model of competitiveness and low labour costs is undermined by a zloty,  
which is considered overvalued by many local industries and the  
generous social and redistributive policies introduced by the  
government. The PiS party, which has been in power since 2015, put a  
huge increase in the minimum wage at the heart of its manifesto for the  
parliamentary election that it won in mid-October.  
6
2
4) educated to degree level or above rose from 10% in 1997 to 27% in  
018 (Eurostat figures), bringing it close to the EU average of 29%.  
According to the IMF (Selected Issues, February 2019), an analysis of  
TFP carried out with data from business suggests that the  
manufacturing sector made a substantial contribution to the increase in  
TFP between 2005 and 2016. The retail and construction sectors also  
made positive contributions to growth in TFP. Meanwhile the  
productivity trend was negative in the mining and utilities sectors. At the  
same time, companies with foreign capital and/or exporters performed  
better than domestic public and private companies, with significantly  
bigger gains in productivity. Lastly, large companies appear more  
productive but less dynamic, resulting in a narrowing of the productivity  
gap as a function of company size over the period considered.  
Demographic projections  
2016=100  
1
20  
15  
Poland  
Czech Republic  
Bulgaria  
Hungary  
Slovakia  
Romania  
1
110  
1
05  
00  
1
Splitting this period into four sub-periods allows us to flesh out the  
details of the composition of Polish growth over the economic cycle:  
In the initial transitional phase (1996 to 2002), the increase in capital  
was fundamental, contributing 95% to Polish GDP growth that averaged  
95  
90  
85  
4.1%, despite the world economy seeing a cyclical low in 2001 to 2002.  
80  
1
993 1997 2001 2005 2009 2013 2017 2021 2025 2029 2033 2037  
From 2003 to 2008, a period that brought strong growth in the global  
economy and the formal admission of Poland to the EU (1 May 2004),  
Chart 13  
Source: United Nations  
Polish GDP growth peaked at 4.8% per year. The accumulation of Faced with the slow demographic decline seen over the past two  
capital remained rapid, albeit slower than in the previous period. The decades, the situation of full employment has resulted in labour  
key point of note in this period, however, was the acceleration in growth shortages limiting production capacity, notably in construction and  
in TFP, which contributed half of total economic growth.  
5
Our scenarios are based on different investment rate assumptions between  
Between 2009 and 2013,Polish growth slowed significantly (to 2.8% per  
2
019 and 2025. The average annual growth rate investment in central, high and  
year), largely due to weaker growth in TFP. According to the IMF, the  
low scenarios respectively equal to 4.6%, 5.1% and 4.1%.  
1
8
Conjoncture // February 2020  
economic-research.bnpparibas.com  
industry. To date, the use of foreign workers, notably from Ukraine, has  
limited the increase in unit labour costs and inflationary pressures  
thanks to the fall in NAWRU (the non-accelerating wage rate of Poland’s macroeconomic performance since its transition from  
unemployment). But faced with competition from the rest of Europe, and communism in the early 1990s has been remarkable. The reform of its  
particularly Germany, in attracting qualified workers, labour shortages institutions and stability of its politics have come alongside the opening  
must be met with innovation and automation for Poland to make up of its economy. Strong and relatively stable economic growth has  
productivity gains and move its products up the value chain.  
allowed it to converge towards the socio-economic standards of  
advanced economies. After its re-election in the parliamentary vote of  
The main factor differentiating between our three scenarios is the  
demographic constraint. Demographic projections (Chart 13)  
established by the Polish Office of Statistics, Eurostat, the United  
Nations and the US Census Bureau agree on an acceleration of the  
decline in the Polish population, evident since 2014, over the next few  
decades (-0.3% per year between now and 2030). Despite family policy  
measures (family benefits, childcare, etc.) and scope for increases in  
the activity rate (notably amongst women), against a background of  
pressure on the labour market, only massive recourse to immigration  
can help avoid the possibility of the labour factor making a negative  
contribution to economic growth between now and 2025.  
13 October 2019, the government promised prosperity for all. But the  
structural drags on growth could complicate the efforts that Poland still  
needs to make if it is to catch up with the income levels of other EU  
countries.  
Sylvain Bellefontaine & Tarik Rharrab  
Research and development expenditure  
%
of GDP, 2018  
5
.5  
4
.5  
3
.5  
2
.5  
1
.5  
0
4
3
2
1
0
Chart 14  
Source: Cornell/INSEAD/WIPO  
Moreover, there are cyclical and structural factors arguing for a slowing  
of investment and thus the accumulation of capital over the short and  
medium term. The rates of growth in GFCF seen over the past two  
years are not sustainable given the expected downturn in the cycle  
(
private investment in machine tools and construction) and the expected  
reduction in payments from European structural funds for 2021-27  
public investment).  
(
Lastly, the quality of the business environment has deteriorated  
somewhat over recent years. The improvement in the institutional  
framework, the improvement in human capital, the quest for productivity  
gains through innovation (Chart 14) and the shift up-market of Polish  
products will be essential to underpin economic growth in Poland over  
the medium and long term.  
1
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Conjoncture // February 2020  
economic-research.bnpparibas.com  
Estimating efficiency6  
Consider the following stochastic frontier model:  
푓ꢂ푥, 훽ꢃ + 푣 − 푢  
휀 = 푣 − 푢  
(1)  
(2)  
(3)  
(4)  
2
푣~푁ꢂꢄ, 휎 ꢃ.  
2
푢~푁 ꢂ푧훿, 휎 ꢃ  
Technical efficiency is specified as:  
푇퐸 = 푒푥푝ꢂ−푢ꢃ  
(5)  
To estimate technical efficiency, we will estimate the conditional expectation[푒푥푝ꢂ−푢ꢃ|휀] .  
The density function for u is truncated at zero of normal distribution:  
ꢑ1  
ꢂꢆꢑꢋꢌꢃꢒ  
2ꢒ  
ꢋꢌ  
푓 ꢂ푢ꢃ = ꢈ√ꢉ휋휎 훷 ꢊ ꢏꢐ 푒푥푝 ꢈ−  
 , ≥ ꢄ (6)  
ꢎ  
훷ꢂ. ꢃ denote the standard normal distribution function  
 and  are random variables of independent distributions, we can be written the joint density function for  and  as follows7:  
ꢑ1  
∗ ꢒ  
ꢂꢆꢑꢆ ꢃ  
2∗ꢒ  
ꢂꢓꢇꢋꢌꢃꢒ  
ꢐ  
ꢋꢌ  
푓 ꢂ휀, 푢ꢃ = ꢈꢉ휋휎 휎 훷 ꢊ ꢏꢐ 푒푥푝 ꢈ−  
+
푢 ≥ ꢄ (7)  
ꢓ,ꢆ  
ꢇꢍꢔ  
ꢎ  
where  
= ꢂꢕ − 훾ꢃ푧훿 − 훾휀 and  =    = 훾ꢂꢕ  훾ꢃ휎2 (8)  
ꢋꢌꢑꢓ  
2
푢 =  
ꢇꢍꢒ  
ꢇꢍꢎ  
휎 = 휎 + 휎2 and  = 휎 /휎  
2
2
(9)  
To estimate technical efficiency for each country  at date , we use the parameter estimates of the equation (8):  
ꢉ  
∗  
∗  
ꢑ1  
푇퐸 = 퐸[푒푥푝ꢂ−푢ꢃ|휀] = 푒푥푝ꢂ− + ) 훷 ꢊ − 휎 ꢏꢐ ꢈ 훷 ꢊ ꢏꢐ  
(9)  
6
Battes & Coelli (1995) ), A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Model for Panel Data, Empirical Economics  
2
0
Conjoncture // February 2020  
economic-research.bnpparibas.com  
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