Abstract
This paper examines the absorption of government spending shocks in the presence of formal and informal production. Calibrating a two-sector open economy model that is consistent with data from India for the period 1990–2017, we show that increases in both government consumption and investment lead to a long-run decline in the shares of employment and output of the informal sector. However, the underlying mechanisms through which these long-run outcomes are achieved differ depending on the type of government spending shock, with the short-run immobility of capital and labor and the adjustment of the real exchange rate playing a key role in the process. While an increase in public consumption reduces the share of the informal sector through a real exchange rate depreciation and aggregate contraction in GDP, an increase in public investment works through a real exchange rate appreciation and an expansion of aggregate GDP. Overall, we find that while the reduction in informality driven by public investment is welfare enhancing, an increase in public consumption reduces welfare.
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Notes
The dependent economy model, as it originated with Salter (1959) and Swan (1960), was purely static. Dynamic extensions have been developed by many authors including Bruno and Sachs (1982), Brock and Turnovsky (1994), Turnovsky and Sen (1995), and Brock (1996). The role of traded versus nontraded investment in the dependent economy model is addressed in detail by Brock and Turnovsky (1994). While most of this literature treats private investment as non-traded, Chatterjee and Mursagulov (2016) depart from this framework by introducing investment goods that are produced in the traded sector. Their paper, however, is focused more on the dynamic implications of the composition of government spending in the dependent economy framework.
The function \(U(.)\) has the standard curvature properties, i.e., both consumption goods yield positive but diminishing marginal utility. The separability of the labor-leisure choice in utility is mainly for simplicity of exposition and does not affect our results substantially.
See e.g., Schneider and Enste (2000), Fleming et al. (2000), Ihrig and Moe (2004), and Johnson et al. (1998), among others. Turnovsky and Basher (2009) examine how a mix of taxation of labor and capital in the formal sector and auditing and penalties in the informal sector can help raise tax revenues and expand long-run economic activity, while keeping the sectoral shares of output unchanged.
Betts and Kehoe (2008) provide evidence of a strong positive correlation between the relative price of non-traded goods and the real exchange rate.
Intersectoral mobility costs for capital have been studied extensively in international trade; see Mayer (1974) and Jones (1975) for early contributions. These costs have also recently been incorporated within the framework of small open economy models, as in Morshed and Turnovsky (2004), van der Ploeg (2011), Chatterjee and Mursagulov (2016), Chatterjee and Turnovsky (2018) and Chatterjee et al. (2022).
As discussed in Chatterjee and Turnovsky (2018), this is a slightly loose definition of the labor force participation rate, since the model does not generate any involuntary unemployment. For example, if there were job seeking and separation every period, then these allocations of time would also be included in the labor force participation rate. In our specification, the agent either works or consumes leisure, which implies that the fraction of time allocated to working in the two sectors is identical to the labor force participation rate.
The instantaneous movement between sectoral labor and leisure is obtained by setting \(\chi = z = 0,{\text{ and }}\sigma \to \infty\). Our approach to modeling the labor market is somewhat more reduced-form, but the main results remain unaffected by alternative modeling choices such as the introduction of unemployment and search and matching frictions. Our principal focus is not the structure of the labor market per se, but rather the dynamic absorption of government spending shocks in the presence of an informal sector. As Turnovsky and Basher (2009) point out, the sluggishness of intersectoral mobility is both realistic and supported by empirical evidence, and leads to a depiction of sectoral movements as a function of forward-looking wage differentials across sectors, similar to the Harris and Todaro (1970) framework.
An important issue in modeling small open economies such as this is the closure of the financial market; see Turnovsky (1997) and Schmitt-Grohé and Uribe (2003) where several alternatives are detailed. These include introducing an endogenous borrowing premium, as in (15), which is most appropriate in the case of the developing economy being analyzed here. This approach originated with Bardhan (1967), and many variants can be found in the literature. These pertain primarily to the normalization of debt as a reflection of the country’s debt-serving ability.
That is, if we let \(\mu_{kf}\) denote the shadow (utility) value of capital, then \(q_{kf} \equiv {{\mu_{kf} } \mathord{\left/ {\vphantom {{\mu_{kf} } {q_{1} }}} \right. \kern-0pt} {q_{1} }}\), and similarly for the other shadow prices. Written in this way, the normalized prices become “asset prices” independent of utility units, with the optimality conditions (16i) and (16j) treating labor as an asset, analogous to capital.
Intersectoral labor movement is proportional to the differential asset price, which upon integrating the arbitrage relationships (16i) and (16j) forward, is the discounted sum of expected future sectoral after-tax wage differentials.
Writing the household budget constraint (2) as \(\dot{N}(t) = r(t)N(t) + X(t) + T_{f} (t)\), the first transversality condition in (16 k) can be written as \(N_{0} + e^{{\int_{0}^{t} {r(\tau )d\tau } }} \int_{0}^{t} {[X(\tau ) + T_{f} } (\tau )]e^{{ - \int_{0}^{\tau } {r(s)ds} }} d\tau = 0\), which constrains the path for lump sum taxes.
Public capital, \(K_{G}\) is also a state variable but evolves exogenously in accordance with (19c) and hence is determined residually.
These earlier studies subjected the relative magnitude of these parameters to robustness checks.
The productivity parameters \(A_{f} {\text{ and }}A_{s}\) are indices and therefore not directly comparable. While we set \(A_{f} > A_{s} ,\) the magnitude of the differential \(A_{f} - A_{s}\) does not affect the qualitative implications of the model.
As noted above, we use the share of self-employment in the non-agricultural labor force as a proxy for informal employment. This measure most likely overstates the actual share of self-employment. As such our model’s equilibrium share of informal employment is below this proxy measure. Similarly, there are scarce reliable estimates of the share of informal output in India’s GDP. This prompts us to use a range from two recent papers, namely Elgin et al. (2021) and Chatterjee et al. (2021). The model’s equilibrium share of informal output falls nicely within this range.
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Acknowledgements
Chatterjee acknowledges support from the Dr. Harold A. Black Distinguished Professorship at the University of Georgia and Turnovsky’s research was supported in part by the Ford and Louisa Van Voorhis Professorship at the University of Washington. We are grateful for comments from an anonymous referee that helped clarify the scope of this paper.
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Chatterjee, S., Turnovsky, S.J. Government expenditure and informality in an emerging economy: the recent experience of India. Ind. Econ. Rev. 58 (Suppl 2), 293–318 (2023). https://doi.org/10.1007/s41775-023-00183-y
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DOI: https://doi.org/10.1007/s41775-023-00183-y