Group assignment topic research on factors affecting the labor productivity of vietnamese households from 2014 to 2016

NATIONAL ECONOMICS UNIVERSITY FACULTY OF ECONOMICS  GROUP ASSIGNMENT TOPIC Research on factors affecting the labor productivity of Vietnamese households from 2014 to 2016 FE62A – Group 2 Hanoi, 0[.]

NATIONAL ECONOMICS UNIVERSITY

FACULTY OF ECONOMICS

- -GROUP ASSIGNMENT

TOPIC:

Research on factors affecting the labor productivity of Vietnamese

households from 2014 to 2016

FE62A – Group 2

Hanoi, 03/2023

TABLE OF CONTENTS

ABSTRACT 3

I INTRODUCTION 5

II LITERATURE REVIEW 6

III METHODOLOGY 7

1 Data 7

2 Model and variables 8

3 Conclusions and recommendations 11

REFERENCES 15

ASSIGNMENT MADE BY:

ACKNOWLEDGEMENT

Firstly, we would like to express our sincere thanks of gratitude to our lecturer, Mr Phung Minh Duc for giving us the treasure opportunity to work with him Thousands of thanks would have to deliver for his patience, enthusiasm, motivation and immense knowledge toward us We are grateful to have our assignment done under his valuable time and guidance He provided us with detailed guidance with his professional experience and knowledge Without him, we would not be completed our assignment successfully

Lastly, special thanks to all the group members for their endless efforts, time, and hard-working in completing this research We do appreciate full cooperation from every single

of them By that, this research can be completed successfully

Drawing on a new set of nationally representative, internationally comparable household surveys, this paper provides an overview of key features of labor productivity in Vietnam Labor productivity is a major component of agricultural development Boserup is the scholar most associated with the view that labor productivity declines with the intensification of agriculture This paper aims at investigating the determinants of agricultural productivity and rural household income The results of the regression of the array data with the fixed-effects model show that the difference in GDP per employee between the industrial-service sector and the agricultural sector has a positive impact on the profit from farming activities rural households The study also shows the role of the local government in managing and operating economic activities and the importance of the current agricultural-rural development investment policies Three econometric models namely: Pooled ordinary least square (POLS), fixed effects (FE), and random effects (RE) model were used to test the effect of factors on Labor productivity

Keywords: agriculture, the relationship between agriculture and industry - services, rural

households in Vietnam, labor productivity, economic scale

I INTRODUCTION

In this paper, we investigate the role of labor productivity growth and the determinants of labor productivity growth through panel data between the period 2014 and 2016 Numerous studies on labor productivity have been conducted around the globe with the aim of evaluating and identifying the variables influencing the productivity of construction workers, forecasting labor productivity, etc In particular, as well as the low labor productivity of the economic sector in other countries, the research results assist economists in identifying problems that affect labor productivity and in developing strategies and solutions to improve labor productivity in economic projects The results will become worthwhile information in determining the major steps to improve the performance of project completion time and also as part of further research in modeling the interaction relationship between the key factors affecting productivity to improve

labor productivity We investigate the role of labor productivity growth and whether the

determinants of labor productivity growth rates between the variables: Labor productivity

= total income from farming/total labor, Age of household head (1-under 30; 2-from 30 to 45; 3-from 46 to 60; 4-over 60), Number of members of the household and Total pesticides expenditure/total arable land

The level of productivity in construction showed a decreasing rate compared to other sectors (Bernstein 2007) The facts of unsatisfactory project completion are indicators of problems associated with productivity and productivity problems usually associated with labor performance (Lowe 1987; Handa and Abdalla 1989; Olomolaiye and Ogunlana 1989) Efforts to produce better performance and increase productivity in labor require an understanding of the various indicators of productivity as a path to understanding the performance of the project (Atkinson et al 1997) Besides that, efforts to improve productivity in the labor industry can essentially be done by reducing pesticide expenditure and selecting a suitable age The number of members of the household and understanding the age of the household head affecting labor productivity is needed to determine the focus of the necessary steps in an effort to maximize labor productivity, thereby increasing productivity and overall project performance The results will be useful information for to construction improve labor productivity in Vietnam

II LITERATURE REVIEW

Up to now, the world has had many studies on labor productivity, with the objectives of assessing and identifying factors affecting the productivity of construction workers, and forecasting labor productivity The results of the research help economists see issues affecting labor productivity, thereby building strategies and solutions to improve labor productivity in projects economic projects in particular as well as the lack of labor productivity in economic sectors in other countries Studies on factors affecting labor productivity have been carried out in the world that the author has summarized as follows:

analysis

Influence variables

Nicolas

Belorgey,

Rémy Lecat

Determinants of productivity per employee: An empirical estimation using panel data

Method of moment

- Labor

- Management

- Technology

- Financial

- External Vandenberghe Ageing and Employability Panel data - Average

productivity hereafter

- The number of workers (e.g: young, prime-age, old/men, women)

- Productivity

Nicole

Maestas,

Kathleen J

Mullen, David

Powell

The Effect of Population Aging

on Economic Growth, the Labor Force, and Productivity

Instrumental variables

- Outcome

- Number of individuals aged 60

- Population aged 20

- Output shocks Serafeim

Polyzos

Garyfallos

Arabatzis

Labor Productivity of the Agricultural Sector in Greece:

Determinant Factors and Interregional Differences

Fixed effects panel model

- The amount of used capital per worker

- The size of the farms

Analysis - The degree of crop

intensification and the inflow of circulating capital

- Infrastructure

- Climatic conditions and geographical zones

Shamil

Factors influencing labor productivity on construction sites: A state-of-the-art literature review and a survey

OLS approach - Experience of the

selected site and project managers

- Project planning

- Communication

- Procurement method

The data used in this study are taken from the Household Living Standards Survey (VHLSS) conducted by the General Statistics Office (GSO) in the years 2014 and 2016, including information reflecting the state of production of agricultural households such as total revenue, expenditure, the output of crops, level of use of inputs for agricultural production such as land, fertilizers, pesticides, human characteristics, etc demographics

of the household such as age, education, employment status and income of workers In addition, we use data sets provided by the General Statistics Office on indicators such as total agricultural land area, total labor productivity, GDP structure, labor structure, and structure Investment capital is broken down by agricultural, industrial, and service sectors in 2014 and 2016 to calculate indicators representing the state of development of agriculture and industry - services at the provincial level We also exploit the Provincial Competitiveness Assessment (PCI) dataset, conducted by the Vietnam Chamber of Commerce and Industry (VCCI) in the years 2014 and 2016, for the purpose of assessing the role of economic practices of local governments for agricultural production The

complete array dataset used in the study includes 4930 agricultural households in 63 provinces and cities with a length of 2 years, a total of 4774 observations established by connections between the above datasets

The regression model of array data to assess the impact of the industry-service development on agricultural profits of households in this study has the following form:

labor product¿=β0+β1hh age¿+β2hh¿¿¿+ β3pesticides¿+c i +u¿¿

In which, i and t are cross-unit (household) and time (years), c i are unobserved individual characteristics of agricultural households and u¿ is false random number The variables in the specific model are as follows:

larbor product: is the labor productivity - a dependent variable, which is identified by the total

income from farming divided by total labor In which income is the revenue from rice, crops, industrial crops, and perennial plants, fruit plants and other revenue from farming

namely, under 30 (years old), 30-45, 41-60, and from 61 This is expected to be relative to the labor productivity, in detail, with an older household head means an experienced farmer, so he might know house to do farming effectively and know to make advantage of other factors

hh¿ ¿: is the number of members in a certain household This factor is expected to be

positive relative to labor productivity, the bigger size of the household the higher the labor productivity of that household, and more members are expected to have a bigger ability to work or help in farming But there is a scratch that, it is helpful when the members in that household are adults, in other scenery, if there is a big household with a number of children and the old more than the employers so it will have a negative effect on labor productivity

pesticides: is calculated by dividing the total expenditure (measured by thousand VND) by

total arable land measured by square meter Since a higher expenditure implies higher labor productivity, the total amount spent on pesticides is anticipated to have a positive relationship with labor productivity Since labor can perform other tasks without having

to expend energy applying manual insecticides, labor productivity is expected to increase

Table 1: Descriptive statistics of independent variables

Variable | Obs Mean Std dev Min Max

ln_nsld | 4,744 8.749264 1.555926 1.609438 13.29632

hh_age_ | 4,930 2.792089 806231 1 4

hh_size_ | 4,930 4.006694 1.561661 1 12

pesticies_ | 4,466 8.180229 87.26383 0 2612.5

-+ -Source: Author's calculation based on the data set using Stata software

According to descriptive statistics, the hh_size variable's mean value is roughly 4 with a relatively low spread of about 1.56, so we can say that on average each household has 4 members The pesticide variable’s mean is about 10.14 with a relatively big spread of about 108.96, this might imply how the difference in pesticide expenditure of each household, there might be some households spend a lot on pesticides and there might some households spend a little on this factor

The effect model fixed is more appropriate, according to the Hausman test, and the model also has issues with the variable error variance (see Appendix 1 and 2) As a result, we employ the Robust Standard Error Method Error) by the White effect estimate technique with fixed dynamics and standard error correction (1980) The results estimated are shown in Table 2

Table 2: Effects of household factors (such as the age of household head, number of members of the household, and total expenditure on pesticides) on labor productivity of the households:

Fixed-effects (within) regression Number of obs = 4,382 Group variable: idho Number of groups = 2,421

R-squared: Obs per group:

Within = 0.0288 min = 1 Between = 0.0001 avg = 1.8 Overall = 0.0006 max = 2 F(5,2420) = 11.23 corr(u_i, Xb) = -0.2093 Prob > F = 0.0000

(Std err adjusted for 2,421 clusters in idho) | Robust

ln_nsld | Coefficient std err t P>|t| [95% conf interval] hh_age_ |

2 | .4445385 .2203514 2.02 0.044 0124415 .8766355

3 | .7272451 .2449473 2.97 0.003 246917 1.207573

4 | .9622134 .2655801 3.62 0.000 4414255 1.483001 |

hh_size_ | -.1507913 .0261457 -5.77 0.000 -.2020615 -.0995211 pesticies_ | .0003562 .0001428 2.49 0.013 0000762 .0006361 _cons | 8.687403 .242912 35.76 0.000 8.211065 9.16374 sigma_u | 1.5856532

sigma_e | .8886598

rho | 76098266 (fraction of variance due to u_i)

hh_age

2 4444705 0.044 (**)

4 9618307 0.000 (***)

pesticides 0002958 0.010 (**)

_cons 8.686463 0.000 (***)

(*) (**) (***): significance level at 10%, 5%, and 1%

The estimated result presented in table 2 shows that:

The estimated coefficient of the variable hh_age is generally positive and significant at a 5% significant level with group 2, and 1% with groups 3 and 4, which implies that a household with an older household head usually has higher labor productivity Group 4 has the biggest effect on labor productivity, group 3 works more efficiently than group 2, and group 1 is less efficient This is reasonable since older household heads have more experience and can make advantage of the sources of labor

The hh_size’s estimated coefficient is negative, with 1% off significant level, which means the bigger the household size is the lower labor productivity it can achieve, this can

be explained, at least in this situation with our data, even though the household is big but the number of employers in the household is much less than the number of children or the old people who don’t have the ability to work on the farm

10% is the significant level of the pesticides’ estimated coefficient A positive relationship between the expenditure on farming of the household implies that the more money spent

on pesticides the higher labor productivity it is But with a small estimated coefficient that means it has a small positive impact on labor productivity than the hh_age variable

Fixed-effects (within) regression Number of obs = 4,382 Group variable: idho Number of groups = 2,421

R-squared: Obs per group:

Within = 0.0288 min = 1 Between = 0.0001 avg = 1.8 Overall = 0.0006 max = 2

F(5,1956) = 11.59 corr(u_i, Xb) = -0.2093 Prob > F = 0.0000

ln_nsld | Coefficient Std err t P>|t| [95% conf interval] hh_age_ |

2 | .4445385 .1790463 2.48 0.013 0933968 .7956802

3 | .7272451 .2001565 3.63 0.000 3347028 1.119787

4 | .9622134 .2205675 4.36 0.000 5296413 1.394785 |

hh_size_ | -.1507913 .025534 -5.91 0.000 -.200868 -.1007147 pesticies_ | .0003562 .0002157 1.65 0.099 -.0000669 .0007792 _cons | 8.687403 .2015181 43.11 0.000 8.29219 9.082615 sigma_u | 1.5856532

sigma_e | .8886598

rho | 76098266 (fraction of variance due to u_i)