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Download miễn phí Impact of Stimulus Package on Firm-Level Performance An Econometric Assessment from 2009 PCI Data for Vietnam
Content
Introduction.3
Methodology and Variables.3
Methodology . 3
Variables . 5
Estimation Results .6
Regression Method . 6
“Propensity Score” Method . 8
Conclusions and Implications .11
References.13
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Tóm tắt nội dung tài liệu:
erest subsidy, stimulus package, Vietnam.* This paper is mainly extracted from a VEPR report submitted to Vietnam Competitiveness Initiative (VNCI)
on January 2010. We would like to thank VNCI for kindly providing us a full dataset of the PCI 2009 survey to
conduct relevant analyses.
† Lecturer at Faculty of Economics, National Economics University; Email: [email protected].
© 2010 Vietnam Centre for Economic and Policy Research
University of Economics and Business, Vietnam National University Hanoi
WP-07
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Content
Introduction................................................................................................................................3
Methodology and Variables.......................................................................................................3
Methodology ....................................................................................................................................... 3
Variables ............................................................................................................................................. 5
Estimation Results .....................................................................................................................6
Regression Method ............................................................................................................................. 6
“Propensity Score” Method ................................................................................................................ 8
Conclusions and Implications ..................................................................................................11
References................................................................................................................................13
Tables
Table 1. Definition and Measurement of Variables...................................................................6
Table 2. Estimated Coefficients of Regression Model (2).........................................................7
Table 3. Logit Model of Access to Interest rate Subsidy Program............................................8
Table 4. Impact of Interest rate Subsidy Package on Firms’ Outcomes..................................10
Table 5. Interest Rate Subsidy Impacts by Different Characteristics ......................................11
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Introduction
Earlier 2009, in the midst of the world crisis, the Vietnamese Government launched an
economic stimulus package worth US$ 8 billion to support domestic enterprises suffering
from the global economic downturn. The package included tax incentives, public investments
in infrastructure, and, of particular interest, 4% interest rate subsidy program. The interest
rate subsidy scheme is unique in the world, thus it has been paid much attention and among
endless controversies on its impact on the domestic economy.
Most of the previous studies have applied qualitative analysis or computable general
equilibrium modeling technique to investigate the macroeconomic effects of the stimulus
package‡. Given the 2009 PCI datasets, in which 3,225 of the 9,890 respondents received the
4% interest rate subsidy, the stimulus package evaluation at the firm level would be feasible,
and this helps to get more convincing conclusions on the effects of this policy on private
investment, performance and growth. That is also the objective of this study, which is
structured into three main parts. The first part presents the rational for methodology and
description of variables. Next come the estimation results and comments, and the study is
finalized by conclusions and implications.
Methodology and Variables
Methodology
Denote G as the expected gain in firm performance due to interest rate subsidy program,
then
G = E(R1i – R0i|Pi=1) (1)
where Pi is the ith firm’s access to subsidy, which takes the value 1 if the firm
participates in program, and 0 otherwise, R1i (R0i) is the performance outcome of the ith firm
if the firm access (does not access) to subsidy. G is the conditional mean impact, conditional
on accessing to the subsidy program, which is called the treatment effect. To estimate G,
there are two possible methods that we apply for this study.
‡ See Nguyen Duc Thanh et al. (2008), Nguyen Thi Nhung and Ha Thi Hieu Dao (2009), James and Hoang Nhi
(2009).
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The first method is to estimate the regression: Ri = a + bPi + cXi + ei (2), where Xi are
observable characteristics of the ith firm that could determine the firm’s performance
outcome. The regression itself controls the different characteristics of firms and let the
estimated coefficient b be understood as the program impact on firms’ performance.
However, there might be a problem of endogeneity in that regression, and instrumental
variable technique rather than OLS technique should be applied in that case. The instrumental
variables are variables that matter to program participation but not to outcomes given
participation. It is, nevertheless, difficult to identify those variables and given limited relevant
datasets can not provide such variables even when those are identified. In addition, this
method makes strong assumptions about regression functional form. Therefore, besides this
method, we also make use the second method for result robustness.
The second method to deal with subsidy program evaluation is to compute the difference
in performance outcome between participants (treatment group) and a comparison group.
Because we can not observe the performance of participants if they had not accessed to
subsidy, the comparison group has to be extracted from non-participants. This group is used
to identify the counterfactual of what would have happened without the program. The
comparison group should be very similar to the participants in terms of characteristics and
the only one main difference between these two groups is whether they access to subsidy
package or not.
Identifying this comparison group, however, is not easy task due to some possible
biases. The first type of biases is due to differences in unobservable characteristics. For given
values of observable characteristics (Xi), there could be a systematic relationship between
subsidy package participation and outcomes in the absence of program. In other words, there
could be some unobservable variables which jointly affect the outcomes and participation
conditional on the observed variables. In this case, one should apply “double difference”
method§, which compares the treatment group and the comparison group (first difference)
before and after the subsidy program (second difference). To apply this method, there must
be a panel data where outcomes and the determinants both before and after the program
introduced are collected, for both treated and untreated groups. Due to the data availability,
we cannot apply this method.
§ See more in Imbens and Wooldridge (2008).
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The second type of biases is due to observable characteristics, where the set of control
variables for which outcomes and participation are observed is different between the
treatment group and the comparison group. This bias will be eliminated if one could find a
sample of non-participants with the same characteristics (Xi) as the treatment group. Given
many variables to be controlled, it seems to be impossible to find a non-participant with
exactly the same all observables for a participant. To deal with that problem, instead of
matching all variables (Xi) to ensure they are the same for both participants and non-
participants, we match the probability of accessing the subsidy program, given Xi. Applying
this so called “propensity score” method**, we follow steps as follows:
• Estimating a logit model of program access as a function of the variables that
are likely to determine the participation, then calculate the predicted probability of access
(propensity scores).
• For each firm in the participant sample, we find five firms in the non-
participant sample that have the closest propensity scores (five nearest neighbors).
• Computing mean value of outcomes of five nearest neighbors and the
difference between that mean and the actual value of the treated firm is the estimated gain
due to program access.
• The mean of all individual firm gains could represent for the general impact of
stimulus package on firms’ performance outcomes. This is also stratified by some
variables of interest to get more insight on the stimulus package impact.
Variables
Two datasets available for the analysis include PCI firm-level and PCI provincial-level
datasets, with the main objective of describing firms perceptions of ...