Friday, November 17, 2017

2016 - Productivity Research - Information and Important Points - Part 1







Accounting for Productivity Dispersion over the Business Cycle

Robert J. Kurtzman and David Zeke
2016-045




T h e J o u r n a l o f D e v e l o p i n g A r e a s
Volume 50 No. 2 Spring 2016

AGRICULTURAL PRODUCTIVITY GROWTH IN INDIA: AN ANALYSIS ACCOUNTING FOR
DIFFERENT LAND TYPES

Varun Kumar Das*
Indira Gandhi Institute of Development Research, India


Australian Economic History Review, Vol. 56, No. 2 July 2016

ASPECTS OF PRODUCTIVITY

BY MARTIN SHANAHAN* & RAJABRATA BANERJEE
University of South Australia (Martin.shanahan@unisa.edu.au)
JEL categories: C18, O39, O40
While research into the determinants of growth is ongoing, assessing the
productivity of factor inputs, including technological progress, remains a key to
understanding what drives economic growth and how this process can be sustained
in the long run.


Increasing productivity is fundamental to increasing material well-being, and
in world with a growing population, vital to the living standards of millions of people.
Assessing and measuring what contributes to productivity improvement are,
however, difficult. While technological change or change in technical efficiency is
frequently cited (especially in the growth literature) as critical to productivity, other
elements, including human capital, scale economies, changes in organisational or
managerial methods, and institutional change (covering everything from legal
regimes to the level of community trust), are also known to be contributing factors.

In the economic growth literature, the productivity of factor inputs is a major
contributing factor to long run growth.

In recent years, some researchers have increased the number of intermediary
inputs used in their productivity estimates to include materials (M), energy (E),
and services (S). The aim is to better isolate the impact of technology (the residual
in most growth equations) more precisely.22 These KLEMS models are less
applicable in studies using pre 1950 data as accurate information on the inputs is
highly variable or missing.

22. Pilat, D., and P. Schreyer. (2002) Measuring productivity. OECD Economic Studies, Vol. 2001/2. 10.1787/
eco_studies-v2001-art13-en [accessed 2 /1/2016]



CAPACITY PLANNING AND CORPORATE PRODUCTIVITY PERFORMANCE IN THE NIGERIAN AVIATION INDUSTRY

G. I. Umoh
Sylva Waribugo
University of Port Harcourt, Rivers State, Nigeria

Yabing and Abraham (2013) carried out a research on capacity planning and performance
in capital intensive service facilities and found out that when firms plan their capacity through the
integration of service resources and incentive contract design, they can better cope with market
uncertainty and also increase their profitability. They further argued that while firms with high
capacity utilization are likely to have increased profitability and cope with uncertainty in market
demand, they have to also maintain a reasonable service level and shorter waiting periods so as to
have competitive advantage. Alberto and Roberto (2007) carried out a similar study on plant
capacity planning and productivity and submitted that several indices of capacity like gross
utilization, net utilization, working efficiency, availability and saturation have influence on the
aggregate productivity of the system.

Also, Umoh, Wokocha, and Amah (2013) did a study on production planning and
corporate productivity performance in the Nigerian manufacturing industry. Their study reveals
that production planning has a positive influence on cost minimization, return on equity capital
and growth. Adegbuyi and Asapo (2010) followed a similar scholarly trajectory by studying the
effect of production planning and budgeting on organizational productivity in a food and
beverage firm. The result of their study shows that there is a significant relationship between
production planning operations and organizational productivity.

Mahmood, et al. (2014), in their
study, postulated that when policies are directed towards knowledge and technology management
capability, idea management capability, project development capability and commercialization
capability which are dimensions of innovation capacity planning–organizations stand to increase
their productivity.

Maishanu and Kadiri (2012) conducted a study on workers satisfaction and organizational
productivity in the Nigerian aviation sector and concluded that the productivity of the sector is
greatly influenced by the level of satisfaction of workers.





*  Connecting empowerment-focused HRM and labour productivity to work engagement: the
mediating role of job demands and resources

Karina Van De Voorde and Marc Van Veldhoven, Tilburg University
Monique Veld, Open University in the Netherlands
Human Resource Management Journal, Vol 26, no 2, 2016, pages 192–210

The paper does not investigate the effect of any variable on productivity

* Estudios de Economía. Vol. 43 - N° 2, Diciembre 2016. Págs. 199-215 199

Corruption, provincial institutions and manufacturing firm productivity: New evidence from a transitional economy*

Corrupción, instituciones provinciales y productividad manufacturera:
Nueva evidencia para una economía en transición
Tran Quang Tuyen**
Vu Van Huong***
Doan Thanh Tinh****

*  Explaining Cross-Country Productivity Differences in Retail Trade

David Lagakos
University of California, San Diego, and National Bureau of Economic Research

It is regarding productivity in retail trade emphasizing car ownership and tax evasion in traditional
retailing.

*  Kaasa A., 2016.

"Culture, religion and productivity: Evidence from European regions”, 

Business
and Economic Horizons, Vol.12(1), pp.11-28, http://dx.doi.org/10.15208/beh.2016.02



Productivity plays an important role for economic growth and the welfare of people.
Hence, there is no doubt that the possible determinants of productivity deserve to be
studied. When looking at the determinants of productivity at the aggregate (society) level,
the research has mainly focussed on factors like human capital, R&D, innovations etc. that
have been shown to be positively related to productivity and economic growth. However,
it has been argued that these factors may not be sufficient for explaining differences in the
levels of productivity in different countries (Sayes, 2011). Hence, the research has to go
beyond these standard factors of productivity and explore other possible factors
(Beugelsdijk and van Schaik, 2005).

Culture comprises people’s  values, beliefs, attitudes, behaviour, etc.. In addition,
religion is something that often guides people’s choices and behaviour. As productivity
can be expected to be related to the everyday performance of the workforce, cultural and
religion-related differences may prove quite useful in explaining differences in productivity
levels between countries or regions.

While the possible impact of culture and/or religion on productivity has been theoretically addressed more or less directly in many studies, only a few studies have examined these relationships empirically (Hall and Jones, 1999; Islam, 2008; Grafton et al., 2002; Gorodnichenko and Roland,
2010).

Both the correlation and regression analysis showed individualism to be positively and
power distance to be negatively related to labour productivity, confirming the
expectations. Masculinity also turned out to be negatively related to productivity,
confirming the positive impact of feminine values rather than masculine values.
Uncertainty avoidance, although negatively related to productivity according to the
correlation analysis, appeared to be insignificant in the regression analysis. Both general
religiosity and the achievement motivation indicator capturing the values of a strong work
ethic turned out to be insignificant after cultural dimensions were added, indicating that
cultural dimensions seem to capture the sources of labour productivity better than
religiosity or values associated with religiosity.



*  PERSONNEL PSYCHOLOGY
2016, 69, 3–66

CUMULATIVE ADVANTAGE: CONDUCTORS AND INSULATORS OF HEAVY-TAILED PRODUCTIVITY DISTRIBUTIONS AND PRODUCTIVITY STARS


HERMAN AGUINIS
Indiana University
ERNEST O’BOYLE, JR.
University of Iowa
ERIK GONZALEZ-MUL´E
University of Iowa
HARRY JOO
Indiana University

marginal costs also vary at the individual level
of analysis across occupations and measures of productivity.
Given such, the extent to which the context allows productivity stars
to keep their marginal costs low will serve as a conductor of cumulative
advantage exhibited in that productivity distribution. We refer to this
source of cumulative advantage as multiplicity of productivity.Multiplicity
of productivity is a conductor because it makes it easier to draw on past
success to create future success.

Hypothesis 1: Multiplicity of productivity will be a conductor of cumulative
advantage, such that the end result of higher
multiplicity work contexts will be a greater likelihood
of a power law distribution and a greater proportion of
productivity stars (i.e., heavier tail).

Productivity stars, wittingly or unwittingly, are able to dominate through
monopolistic means (e.g., Borghans & Groot, 1998; Franck & N¨uesch,
2012). Accordingly, we offer the following hypothesis involving monopolistic
productivity as a conductor for cumulative advantage:
Hypothesis 2: Monopolistic productivity will be a conductor of cumulative
advantage, such that the end result of higher
monopolistic work contexts will be a greater likelihood
of a power law distribution and a greater proportion of
productivity stars (i.e., heavier tail).

Empirically, job autonomy generally has a positive relation with productivity
(Humphrey, Nahrgang, & Morgeson, 2007).

job autonomy provides the discretion that can allow stars to
show their creativity and innovation (Ohly & Fritz, 2010) as well as
allowing them to more fully utilize their unique competencies (McIver,
Lengnick-Hall, Lengnick-Hall, & Ramachandran, 2013).

Hypothesis 3: Job autonomy will be a conductor of cumulative advantage,
such that the end result of jobs with greater
autonomy will be a greater likelihood of a power law
distribution and a greater proportion of productivity
stars (i.e., heavier tail).

a highly complex job such
as that of a academic researcher has long been known to demonstrate a
heavy-tailed productivity distribution in terms of number of publications
as well as citations (Shockley, 1957), as have other prototypically complex
jobs that have become so pervasive in today’s knowledge economy (e.g.,
software engineers; Curtis, Sheppard, Milliman, Borst, & Love, 1979;
Darcy & Ma, 2005). On the other hand, less complex jobs from the manufacturing
sector exhibit little variance in outputs (Schmidt & Hunter,
1983).

resource-based theory, which usually focuses on productivity at the firm and not the
individual level of analysis, describes complex output, especially output at the tails of the distribution, as more difficult to imitate and less likely to be substituted by even slightly less productive firms (Barney, Ketchen, & Wright, 2011).

Hypothesis 4: Job complexity will be a conductor of cumulative advantage, such that the end result of jobs with greater complexity will be a greater likelihood of a power law distribution and a greater proportion of productivity stars (i.e., heavier tail).

Hypothesis 5: Productivity ceiling will be an insulator of cumulative advantage, such that the end result of jobs with lower productivity ceilings will be a smaller likelihood of a power law distribution and a smaller proportion of productivity stars (i.e., lighter tail).

becoming aware of the shape of the productivity distribution, and not assuming normality, is a necessary first step before such decisions can be made.

The presence of nonnormal productivity distributions also has implications
for compensation practices. In particular, pay dispersion may be
seen as more acceptable and fair to employees if they are aware that the
distribution has a heavy tail (i.e., a large proportion of productivity stars).
Thus, it may be beneficial to share information on the shape of the productivity
distribution with various organizational members. However, if
the compensation system does not offer additional rewards to productivity
stars, productivity information may lead to dissatisfaction among those
individuals who are the top producers—possibly leading to a decrease in
their productivity or even departure from the organization. Thus, it is important
to consider the anticipated consequences of making information
on productivity distributions available.

Barney JB, Ketchen DJ, Wright M. (2011). The future of resource-based theory:
Revitalization or decline? Journal of Management, 37, 1299–1315.
doi:10.1177/0149206310391805

Buzacott JA (2002).
The impact of worker differences on production system output.
International Journal of Production Economics, 78, 37–44. doi:10.1016/S0925-
5273(00)00086-4



* RAND Journal of Economics
Vol. 47, No. 3, Fall 2016
pp. 608–630

Demand or productivity: what determines firm growth?

Andrea Pozzi∗
and
Fabiano Schivardi∗∗

Modern theories of industry dynamics assume that firms are heterogeneous along a single
unobserved dimension, productivity, which determines the firm’s performance and growth
(Jovanovic, 1982; Hopenhayn, 1992).

the assumption that all firms look alike to consumers fails to capture an important ingredient of
firm performance.

We start our analysis by setting up a standard model of monopolistic competition on the
demand side and Cobb-Douglas technology on the production side, each with its own stochastic
shifter. 

Productivity shocks are then identified as residuals of the production function equation, with output deflated with firm-level prices.

To explain our findings, we rely on insights from scholars emphasizing the role of managerial ability
and corporate practices in the exploitation of technology shocks (Bloom, Sadun, and Van Reenen,
2012; Dranove et al., 2014). Our results show that managerial practices are important for not only
within-firm productivity growth, but also to enhance the process of efficient factors allocation
across firms.

Foster, Haltiwanger, and Syverson (2008) were the first to separately
identify demand and productivity shocks. They show that failing to disentangle demand and TFP
shocks leads to an underestimation of new entrants’ contribution to productivity growth. Foster,
Haltiwanger, and Syverson (2016) study the process of accumulation of idiosyncratic demand,
finding that demand shock builds up slowly and that it depends on past firm sales. Our results
complement theirs: though it takes time to build idiosyncratic demand, we show that reacting to
its fluctuations is easier than reacting to changes in productivity.

Our theoretical framework relies on a model of monopolistic competitionwhere firms choose
inputs to produce output, subject to a CES demand and a Cobb-Douglas production function as in
Melitz (2000).

The market appeal component ( i t ) picks up heterogeneity in firms’ demand driven by
differences in the perceived quality of the product, controlling for its physical attributes. It relates
to similar concepts introduced by Foster, Haltiwanger, and Syverson (2016) and Gourio and
Rudanko (2014), who link it to the stock of consumers who have tried the product in the past (the
“customer base”). Other instances of demand shocks consistent with our setting are spreading of
good word-of-mouth, improvements in the brand image, and the perception or the visibility of
the products, for example, as a result of advertising.

The idea behind this postulate is
that TFP shocks represent a shift in the production technology, and responding to them likely
entails shifting the way things are done within the firm: for instance, a change in the skill mix
of the employees or the use of different types of capital inputs. If the firm’s management lacks
the expertise to implement these complementary reorganizations, the adjustment of the scale
of operation following a TFP shock will be incomplete. This scenario might be less likely for
demand shocks, where the need to cater to a larger mass of customers can be met by simply
scaling up production without necessarily requiring reorganization.

The framework sketched in Section 5 delivers an empirical prediction for the presence
of a managerial ability friction: the size of the untransmitted component of TFP should be
smaller for firms with better managerial ability, as they are more likely to be able to reorganize
their activities to take full advantage of the productivity shock.18

Our results imply that managerial practices
are not only important for within-firm productivity growth, but also for the process of efficient
factors allocation across firms. Improving our understanding of the determinants of firms’ reaction
to shocks of different nature may contribute in an innovative way to the debate on the efficient
allocation of resources.


* J Prod Anal (2016) 45:131–155

The determinants of productivity in Chinese large and medium-sized industrial firms, 1998–2007

Sai Ding, • Alessandra Guariglia • Richard Harris

The average TFP growth in Chinese industries is 9.6 % per annum during the period
1998–2007, and is mainly driven by firm entry. The subsector decomposition exercises show that the inter-firm
resource reallocations are more prominent across industries than across provinces.

Productivity is viewed as the most important long-run
driver of economic growth in both economic theory and
empirical research. According to Klenow and Rodrı´guez-
Clare (1997), total factor productivity (TFP) growth
accounts for 90 % of the international variation in output
growth. Easterly and Levine (2001) argue that the major
empirical regularities of economic growth indicate an
important role for the residual rather than for factor accumulation.

Second, unlike most previous
studies, which rely on the method of Olley and Pakes
(1996) or Levinsohn and Petrin (2003) to construct TFP,
we use a system Generalized Method of Moments (GMM)
estimator (Blundell and Bond 1998). We believe it is
important to use this approach as many studies have shown
that firms have (unmeasured) productivity advantages that
persist over time, which need to be captured.

Our results indicate increasing returns to scale in the
majority of industries and a (usually large) positive time
trend representing technical change.

we calculate TFP using a Cobb-Douglas log-linear
production function approach including fixed effects. The
inclusion of fixed effects is necessary as empirical evidence
using firm-level panel data consistently shows that firms
are heterogeneous (productivity distributions are significantly
‘spread’ out with large ‘tails’ of firms with low
TFP), but more importantly that the distribution is persistent—
firms typically spend long periods in the same part of
the distribution (see, for instance, Bartelsman and Dhrymes
1998; Haskel 2000; and Martin 2008). Such persistence
suggests that firms have ‘fixed’ characteristics (associated
with access to different path dependent (in)tangible
resources, managerial and other capabilities) that change
little through time, and thus need to be modeled. In the
light of these considerations, we estimate the following
model:


** Younger firms more productive

Firm age is found to affect TFP significantly and negatively
for most industries. This is consistent with the
belief that younger firms produce with greater efficiency
and better technology than older firms. Obviously the
hypothesis that productivity increases as the firm ages
through learning-by-doing is not supported by our data for
China.

(Interesting articles with many references on productivty determinants)

FACTOR DETERMINANTS OF TOTAL FACTOR PRODUCTIVITY GROWTH FOR THE JAPANESE MANUFACTURING INDUSTRY

SANGHO KIM∗
Contemporary Economic Policy (ISSN 1465-7287)
Vol. 34, No. 3, July 2016, 572–586
Online Early publication October 23,

the measured TFP fails to provide true technology shocks if one of the assumptions is violated.

This section summarizes the theoretical background of estimating embodied technical
progress simultaneously with disembodied technical progress when there are scale economies
and imperfect competition. An empirical framework developed here will extend the model of
Kim (2014) to include a variety of factors that have influence on technical change.


We use the Japan Industrial Productivity (JIP)
Database 2011, which comprises various variables
necessary to estimate TFP for the Japanese
economy. Based on the database, we compile a
panel of 52 manufacturing industries for the year
1973–2010, from which we can construct all the
variables required for estimation.

Estimates of Determinants for Japanese Durable Manufacturing Productivity

Average TFP growth (Δ¯a)
Impact of trade
Interindustry externality
Embodied technology
Technology and market environment

The results suggest significant influence of
embodied technical progress on productivity
growth for the Japanese manufacturing industry,
which should be isolated from disembodied
technical progress in estimating the impact of
factor determinants of the productivity growth.


Technology embodied in the physical capital,
not the capital itself, determines productivity
growth. On the other hand, coefficient estimates
of R&D investment are statistically insignificant
in every model, suggesting technology acquired
from R&D investment generates productivity
growth when embodied into physical and
human capital.
For interindustry externality, IT investment of
the total manufacturing industry has positively
significant effects on productivity growth,

For globalization and trade, openness has a
negative and significant influence on productivity
growth,

Estimation results show that coefficient
estimates of technologies embodied in human
capital, physical capital, and IT capital are all
positively significant, suggesting the existence
of considerable embodied technical progress for
the Japanese manufacturing industry. Furthermore,
including embodied technical progress
renders the impact of physical and R&D capital
on productivity growth insignificant, implying
that R&D impacts are realized only after being
embodied into other capitals.

The Economic Journal, 126 (May), 654–681.Doi: 10.1111/ecoj.12373©2016 Royal Economic Society. Published by

JohnWiley & Sons, 9600 Garsington
Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

DO PERSONALITY TRAITS AFFECT PRODUCTIVITY? EVIDENCE FROM THE LABORATORY*

Maria Cubel, Ana Nuevo-Chiquero, Santiago Sanchez-Pages and Marian Vidal-Fernandez


Despite the large body of literature on the determinants of labour force earnings, a substantial part of the wage inequality across and even within a range of demographic characteristics and occupations still remains unexplained. In his seminal work, Becker (1964) highlighted the relevance of cognitive skills in explaining earning differences. However, variations in cognitive abilities fail to account fully for the residual wage inequality.

Within the set of non-cognitive skills, personality traits are one of the most relevant instruments in the study of differences in earnings. Mueller and Plug (2006) show that the effect of personality traits on earnings is of similar magnitude to the one of cognitive skills.


Recent studies have linked job performance and wages to the so-called ‘Big Five’ personality traits: openness, conscientiousness, extraversion, agreeableness and neuroticism (Heckman et al., 2006; Fletcher, 2013).


This article uses a laboratory experiment to directly test the relationship between the Big Five personality traits and individual productivity (claimed as the first article to use the methodology in this area).

 Nyhus and Pons (2005) report a negative correlation between neuroticism and wages for
both men and women, and a negative correlation of agreeableness with wages for women only.

To summarise the literature reviewed, the take home messages are:

(i) neuroticism and agreeableness are consistently correlated with lower earnings while more conscientious individuals present better labour market outcomes;
(ii) gender differences in the effects of personality traits can contribute to explain the gender wage gap; and
(iii) the estimated effect of personality is of comparable magnitude to that of cognitive skills.

Hypotheses

Neuroticism
This trait is defined as lack of emotional stability and predictability and by the presence of mood changes, anxiety and insecurity. Neuroticism has been consistently found to hinder wages.
Hence, hypothesis is that high levels of neuroticism should be correlated with low
performance in our experiment.

Conscientiousness
This trait measures the extent to which individuals are careful, responsible and hard
working. Because it is associated to efficient, organised, achievement-oriented and selfdisciplined
individuals, conscientiousness shows a consistent positive relation with labour market outcomes. In a similar way, we expect a positive relationship between conscientiousness and performance in our experiment, because being careful, efficient and focused should improve accuracy in the task.

Openness
Individuals who are open to new experiences are typically imaginative, artistic, curious,
creative and intellectually oriented. In their laboratory study, M€uller and Schwieren (2012) observe a negative correlation between openness and performance in the same addition task under piece-rate payment, albeit in a five-minute round. Our conjecture is that this  result might be driven by creative and artistic individuals who are likely to find the task repetitive and boring. They might also be more likely to engage in the experiment, as a new experience, but the characteristics of the task are likely to countervail this initial positive effect. Therefore, we expect a negative net effect of openness on performance in our task.

Survey evidence suggests that the overall effect of agreeableness on labour market outcomes is negative.

The facet of extraversion associated to ambition could have a positive impact on performance.

The hypotheses tested in the paper are:
HYPOTHESIS 1. Neuroticism is negatively associated with performance.
HYPOTHESIS 2. Conscientiousness is positively associated with performance.
HYPOTHESIS 3. Openness has a negative relationship with perforitude.

Results

The Big Five personality traits are jointly significant and the individual scores are largely consistent with our hypotheses. As in the previous literature using survey data and in line with our hypothesis H1, more neurotic subjects perform significantly worse in our task:

Our hypothesis regarding conscientiousness (H2) is also supported. We find a positive and significant effect of this trait on performance, in line with the results obtained in both the economics and the psychology literatures. The coefficients for agreeableness and openness are, although insignificant, negative and of sizeable magnitude




































No comments:

Post a Comment