Abstract
We propose a structural approach to investigate total factor productivity (TFP) and economic growth of 58 provinces and municipalities of Vietnam (known as one of the most dynamic emerging economies in the last few years). The analysis is applied to the provincial data that are available to us for the period 2000–2007. TFP is composed of three components: an autonomous technological change, an observed deterministic part depending on external factors, and an unobserved stochastic part. Estimation results do not show any evidence regarding the impacts of national and local public spending on TFP and economic growth of Vietnam’s provinces. On the contrary, human capital and industry share (compared to shares of agriculture and services) significantly increase the provincial TFP, helping to explain the cross-province differences in terms of productivity. Finally, TFP of Vietnam’s provinces does not converge in the long run as it displays a polarization feature around two main groups of provinces, a large group with low TFP levels and a much smaller group with high TFP levels. This bipolar pattern of TFP distribution supports the competitiveness disparity among the Vietnam’s provinces.
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Data sources: World Development Indicators (World Bank), General Statistics Office (Vietnam), and Ministry of Finance (Vietnam)
Data sources: World Development Indicators (World Bank), General Statistics Office (Vietnam), and Ministry of Finance (Vietnam)
Source: General Statistic Office of Vietnam, Ministry of Finance, and the World Bank
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Notes
Vietnam was consequently viewed as a relatively highly decentralized country (World Bank 2014).
The Vietnam Provincial Competitiveness Index is administered by the Vietnam Chamber of Commerce and Industry (VCCI) with support from the U.S. Agency for International Development (USAID). It is built on the opinions of domestic non-state firms from all 63 provinces and foreign-invested ones operating in Vietnam. The PCI aims to assess and rank the provincial economic governance that can affect private sector development.
This also motivates our approach which allows for the correlation between TFP and production input factors.
At the country level, the international transmission of R&D knowledge may be implemented through the channel of trade and its contribution to TFP growth has been found in several studies (Del Barrio-Castro et al. 2002; Madsen 2007, etc.). The underlying idea is that one economy’s TFP depends on its R&D activity and R&D of foreign economies that spill over into the world economy by mean of trade. Trade partners benefit from technological spillovers, which increase their TFP, leading to economic growth. In this regard, the magnitude of international R&D spillovers may depend on human capital of an economy.
See also Van Beveren (2012) for a literature survey.
We can also use the moment conditions
$$\begin{aligned} E\left[ (\tilde{\zeta }_{it} + \tilde{\varepsilon }_{it}) \left( \begin{array}{c} k_{i,t-1} \\ l_{i,t-1} \\ g_{i,t-1} \end{array} \right) \right] = 0 \end{aligned}$$because Eq. (6) becomes
$$\begin{aligned} y_{it} = \hat{\lambda } t + Z_{it}' \hat{\eta } + \hat{a}_K k_t + \hat{a}_L l_t + \hat{\theta } g_t + \alpha _K k_{it} + \beta _L l_{it} + \gamma g_{it} + E(\tilde{\omega }_{it} \mid \tilde{\omega }_{i,t-1} ) + \tilde{\zeta }_{it} + \tilde{\varepsilon }_{it}, \end{aligned}$$where estimates were plugged.
See Appendix for a map of Vietnam’s provinces and municipalities.
Official data at the provincial level, usually provided by the General Statistics Office for a longer period are not available. Vietnam has in total 58 provinces and 5 municipalities. However, five provinces (Ha Giang, Hau Giang, Kon Tum, Dong Thap, and Tra Vinh) were excluded from our data sample due to missing data. Ha Tay was merged into the capital Ha Noi in 2008.
The equation characterizing the PIM is \(K^{\tau }_{it} = S^{\tau }_{it} + (1-\delta )K^{\tau }_{i,t-1}\) where \(S^{\tau }_{it}\) is the flow of investment of type \({\tau }\) (\({\tau } = \mathrm{{PI}}\) or FDI), \(K^{\tau }_{it}\) is the capital stock of type \({\tau }\) at time t, and \(\delta \) is the depreciation rate. The initial capital stock is given by \(K^{\tau }_{i0} = S^{\tau }_{i0}/(g_S^{\tau } + \delta )\) where \(g_S^{\tau }\) is the average geometric growth rate of investment of type \({\tau }\) for the period of study. Usually the depreciation rate is set between 4 and 6%. In this paper, changing \(\delta \) from 4 to 6% does not modify the qualitative results.
Let \(g_x\) denote the average geometric growth rate of a series x. Hence, the relation between the initial value (period 0) and the value of this variable at time t is \(x_t = x_0 (1+g_x)^t\). Thus, the average growth rate of x between 0 and t is approximately calculated as \(g_x = \ln (x_t / x_0)/t\). Equivalently, this growth rate can be computed as \(g_x = \exp (b)-1\) where b is the slope coefficient of the ordinary least squares regression \(\ln x_t = a + b t + \upsilon _t \), \(t=1,2, \ldots ,T\).
However, information on provincial governance is not available.
We are thankful to an anonymous reviewer for pointing out this issue.
More precisely, if \(x_{it} = x_{it}^{*} + \varepsilon _{it}^x\) where \(x_{it}^{*}\) is the unobserved true value of \(x_{it}\), \(x_{it} = k_{it}, l_{it}, g_{it}\), and \(\varepsilon _{it}^x\) is the corresponding measurement error, the new residual terms of Eq. (6) becomes \(\vartheta _{it} \equiv \varepsilon _{it} + \varepsilon _{it}^k + \varepsilon _{it}^l + \varepsilon _{it}^g\). Hence, \(E(\vartheta _{it} \mid k_{it}, l_{it} , g_{it}) = E(\vartheta _{it} \mid k_{it}^{*} + \varepsilon _{it}^k, l_{it}^{*} + \varepsilon _{it}^l , g_{it}^{*} + \varepsilon _{it}^g) = E(\vartheta _{it} \mid \varepsilon _{it}^k, \varepsilon _{it}^l , \varepsilon _{it}^g) \ne 0\).
Using lagged values \(x_{i,t-1}\) reduces the sample size from 423 observations to 364 observations.
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We are grateful to Yoshiro Higano, the Editor-in-Chief, and two anonymous reviewers of the journal and participants of several seminars for their very helpful comments that allowed us to improve the content of the paper. The usual caveat applies.
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Nguyen-Van, P., Pham, T.K.C. & Le, DA. Productivity and public expenditure: a structural estimation for Vietnam’s provinces. Asia-Pac J Reg Sci 3, 95–120 (2019). https://doi.org/10.1007/s41685-018-0085-1
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DOI: https://doi.org/10.1007/s41685-018-0085-1
Keywords
- National public expenditure
- Local public expenditure
- Human capital
- Sectoral shares
- Total factor productivity
- Vietnam’s provinces